U.S. patent application number 17/199337 was filed with the patent office on 2021-08-26 for system and method of a biosensor for detection of health parameters.
The applicant listed for this patent is Sanmina Corporation. Invention is credited to Robert Steven Newberry, Matthew Rodencal.
Application Number | 20210259640 17/199337 |
Document ID | / |
Family ID | 1000005571905 |
Filed Date | 2021-08-26 |
United States Patent
Application |
20210259640 |
Kind Code |
A1 |
Newberry; Robert Steven ; et
al. |
August 26, 2021 |
SYSTEM AND METHOD OF A BIOSENSOR FOR DETECTION OF HEALTH
PARAMETERS
Abstract
A photoplethysmography (PPG) circuit obtains PPG signals at a
plurality of wavelengths of light reflected from tissue of a user.
A processing device generates parameters using the PPG signals to
determine a glucose level in blood flow of the user. The parameters
include one or more ratio values obtained using the plurality of
PPG signals; a phase delay between the plurality of PPG signals; a
correlation of phase shape between the plurality of PPG signals or
a periodicity of one or more of the plurality of PPG signals.
Inventors: |
Newberry; Robert Steven;
(New Hope, AL) ; Rodencal; Matthew; (Huntsville,
AL) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Sanmina Corporation |
San Jose |
CA |
US |
|
|
Family ID: |
1000005571905 |
Appl. No.: |
17/199337 |
Filed: |
March 11, 2021 |
Related U.S. Patent Documents
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Application
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10952682 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
A61B 5/4845 20130101;
A61B 5/0002 20130101; A61B 5/743 20130101; A61B 2560/0223 20130101;
G16H 40/63 20180101; A61B 5/6893 20130101; A61B 5/7275 20130101;
A61B 5/0022 20130101; A61B 5/6817 20130101; A61B 5/681 20130101;
A61B 5/6826 20130101; A61B 5/14551 20130101; A61B 5/02416 20130101;
A61B 5/14532 20130101; A61B 5/01 20130101; A61B 5/7225 20130101;
A61B 5/1455 20130101 |
International
Class: |
A61B 5/00 20060101
A61B005/00; A61B 5/01 20060101 A61B005/01; A61B 5/145 20060101
A61B005/145; A61B 5/1455 20060101 A61B005/1455; A61B 5/024 20060101
A61B005/024; G16H 40/63 20060101 G16H040/63 |
Claims
1. A system, comprising: an optical circuit configured to obtain a
plurality of PPG signals at a plurality of wavelengths reflected
from or transmitted through tissue of a user, wherein the plurality
of wavelengths have varying penetration depths of tissue; one or
more processing devices configured to: determine one or more PPG
parameters using the plurality of PPG signals; and determine a
health index using the one or more PPG parameters, wherein the
health index indicates a vascular health of the user.
2. The system of claim 1, wherein the one or more processing
devices are configured to determine the one or more PPG parameters
by: determining at least one of the following PPG parameters: a
phase delay between a first PPG signal and a second PPG signal of
the plurality of PPG signals, a correlation of phase shape between
the first PPG signal and the second PPG signal of the plurality of
PPG signals or a periodicity of first PPG signal or the second PPG
signal of the plurality of PPG signals.
3. The system of claim 1, wherein the one or more processing
devices are further configured to: determine an insulin release
event using at least a first PPG signal of the plurality of PPG
signals, wherein the first PPG signal is obtained from a wavelength
of light in a range of 380 nm-410 nm.
4. The system of claim 3, wherein the one or more processing
devices are further configured to: determine the one or more PPG
parameters using at least the first PPG signal, wherein the first
PPG signal is obtained during the insulin release event.
5. The system of claim 4, wherein the one or more processing
devices are further configured to: determine a relative change in
diameter of vessels during the insulin release event; and determine
the health index using the relative change in diameter of
vessels.
6. The system of claim 1, wherein the one or more processing
devices are further configured to determine the one or more PPG
parameters by: determining a first AC component of a first PPG
signal of the plurality of PPG signals, wherein the first PPG
signal is obtained using a first wavelength of light in a range of
380 nm-410 nm; and determining a second AC component of a second
PPG signal of the plurality of PPG signals, wherein the second PPG
signal is obtained using a second wavelength of light equal to or
above 660 nm.
7. The system of claim 6, wherein the one or more processing
devices are further configured to determine the one or more PPG
parameters by: determining a first ratio R value using the first AC
component and the second AC component.
8. The system of claim 7, wherein the one or more processing
devices are further configured to determine the health index,
wherein the health index indicates a diabetic risk in the user.
9. A device, comprising: at least one memory configured to store a
plurality of PPG signals obtained using a plurality of wavelengths
reflected from or transmitted through tissue of a user, wherein the
plurality of wavelengths have varying penetration depths of the
tissue of the user; and one or more processing circuits configured
to: determine one or more R values using the plurality of PPG
signals; determine one or more PPG parameters using the plurality
of PPG signals; and determine a health index of the user using the
one or more R values and the one or more PPG parameters.
10. The device of claim 9, wherein the one or more processing
circuits are configured to determine the one or more R values using
the plurality of PPG signals by: determining a first R value using
a first PPG signal and a second PPG signal of the plurality of PPG
signals, wherein the first PPG signal is obtained using a first
wavelength of light in a range of 380 nm-410 nm and the second PPG
signal is obtained using a second wavelength of light at or above
660 nm; and determining a second R value using the first PPG signal
and a third PPG signal of the plurality of PPG signals, wherein the
third PPG signal is obtained using a third wavelength of light in a
range of 510 nm-550 nm.
11. The biosensor of claim 10, wherein the one or more processing
circuits are configured to determine the one or more PPG parameters
using the plurality of PPG signals by: determining at least one of:
a phase delay between the first PPG signal and the second PPG
signal, a correlation of phase shape between the first PPG signal
and the second PPG signal, or a periodicity of one or more of the
first PPG signal, the second PPG signal or the third PPG
signal.
12. The device of claim 9, wherein the one or more processing
devices are configured to: determine an insulin release event using
at least a first PPG signal of the plurality of PPG signals,
wherein the first PPG signal is obtained from a wavelength of light
in a range of 380 nm-410 nm; and determine the one or more PPG
parameters using the plurality of PPG signals obtained during the
insulin release event.
13. The device of claim 9, wherein the one or more processing
devices are configured to: determine a first L value by isolating
an alternating current (AC) component of a first PPG signal in a
range of 380 nm-410 nm and a second L value by isolating an AC
component of a second PPG signal at a wavelength at or above 660 nm
and a third L value by isolating an AC component of a third PPG
signal in a range of 510 nm-550 nm; determine a plurality of R
values using the plurality of L values, wherein a first R value is
determined from a ratio of the first L value and the second L value
and a second R value is determined from a ratio of the first L
value and the third L value.
14. The device of claim 13, wherein the one or more processing
devices are further configured to determine the health index,
wherein the health index indicates a diabetic risk in the user.
15. The device of claim 9, wherein the one or more processing
circuits implement regression neural network processing or a
classifier neural network processing to determine the health
index.
16. A method, comprising: obtaining a plurality of PPG signals at a
plurality of wavelengths reflected from or transmitted through
tissue of a user, wherein the plurality of wavelengths have varying
penetration depths of tissue; determining one or more PPG
parameters using the plurality of PPG signals; and determining a
health index using the one or more PPG parameters, wherein the
health index indicates a vascular health of the user.
17. The method of claim 16, wherein determining the one or more PPG
parameters using the plurality of PPG signals comprises:
determining at least one of the following PPG parameters: a phase
delay between a first PPG signal and a second PPG signal of the
plurality of PPG signals, a correlation of phase shape between the
first PPG signal and the second PPG signal of the plurality of PPG
signals or a periodicity of first PPG signal or the second PPG
signal of the plurality of PPG signals.
18. The method of claim 16, further comprising: determining at
least one insulin release event using at least a first PPG signal
of the plurality of PPG signals, wherein the first PPG signal is
obtained from a wavelength of light in a range of 380 nm-410
nm.
19. The method of claim 18, wherein determining the health index
using the one or more PPG parameters comprises: determine the one
or more PPG parameters using at least the first PPG signal, wherein
the first PPG signal is obtained during the insulin release
event.
20. The method of claim 19, further comprising: determining a
relative change in diameter of vessels during the insulin release
event; and determining a health index using the one or more PPG
parameters and the relative change in diameter of vessels.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation of U.S. patent
application Ser. No. 16/779,453 filed Jan. 31, 2021 entitled
"SYSTEM AND METHOD OF A BIOSENSOR FOR DETECTION OF HEALTH
PARAMETERS" to issue on Mar. 23, 2021 as U.S. Pat. No. 10,952,682,
which claims priority under 35 U.S.C. .sctn. 119 to U.S.
Provisional Application No. 62/935,589 entitled, "SYSTEM AND METHOD
OF A BIOSENSOR FOR DETECTION OF HEALTH PARAMETERS," filed Nov. 14,
2019, and hereby expressly incorporated by reference herein.
[0002] U.S. patent application Ser. No. 16/779,453 claims priority
under 35 U.S.C. .sctn. 120 as a continuation in part to U.S. patent
application Ser. No. 16/433,947 entitled, "SYSTEM AND METHOD OF A
BIOSENSOR FOR DETECTION OF MICROVASCULAR RESPONSES," filed Jun. 6,
2019, now U.S. Pat. No. 10,736,580 issued Aug. 11, 2020, and hereby
expressly incorporated by reference herein,
[0003] U.S. patent application Ser. No. 16/779,453 claims priority
under 35 U.S.C. .sctn. 120 as a continuation in part to U.S. patent
application Ser. No. 16/172,661 entitled, "SYSTEM AND METHOD OF A
BIOSENSOR FOR DETECTION OF VASODILATION," filed Oct. 26, 2018, now
U.S. Pat. No. 10,744,261 issued Aug. 18, 2020, and hereby expressly
incorporated by reference herein, which claims priority under 35
U.S.C. .sctn. 119(e) to: [0004] U.S. Provisional Application No.
62/675,151 entitled, "SYSTEM AND METHOD OF A BIOSENSOR FOR
DETECTION OF VASODILATION," filed May 22, 2018, and hereby
expressly incorporated by reference herein; [0005] U.S. Provisional
Application No. 62/577,707 entitled, "SYSTEM AND METHOD FOR HEALTH
MONITORING OF AN ANIMAL USING A MULTI-BAND BIOSENSOR," filed Oct.
26, 2017, and hereby expressly incorporated by reference herein;
and [0006] U.S. Provisional Application No. 62/613,388 entitled,
"SYSTEM AND METHOD FOR INFECTION DISCRIMINATION USING PPG
TECHNOLOGY," filed Jan. 3, 2018, and hereby expressly incorporated
by reference herein.
[0007] U.S. patent application Ser. No. 16/779,453 claims priority
under 35 U.S.C. .sctn. 120 as a continuation in part to U.S. patent
application Ser. No. 15/898,580 entitled, "SYSTEM AND METHOD FOR
OBTAINING HEALTH DATA USING A NEURAL NETWORK," filed Feb. 17, 2018,
now U.S. Pat. No. 10,888,280 issued Jan. 12, 2021, and hereby
expressly incorporated by reference herein.
[0008] U.S. patent application Ser. No. 16/779,453 claims priority
under 35 U.S.C. .sctn. 120 as a continuation in part to U.S. patent
application Ser. No. 16/239,417 entitled, "SYSTEM AND METHOD FOR
MONITORING BLOOD CELL LEVELS IN BLOOD FLOW USING PPG TECHNOLOGY,"
filed Jan. 3, 2019, and hereby expressly incorporated by reference
herein, which claims priority under 35 U.S.C. .sctn. 119(e) to:
[0009] U.S. Provisional Application No. 62/613,388 entitled,
"SYSTEM AND METHOD FOR INFECTION DISCRIMINATION USING PPG
TECHNOLOGY," filed Jan. 3, 2018, and hereby expressly incorporated
by reference herein.
[0010] U.S. patent application Ser. No. 16/779,453 claims priority
under 35 U.S.C. .sctn. 120 as a continuation in part to U.S. patent
application Ser. No. 16/208,358 entitled, "VEHICULAR HEALTH
MONITORING SYSTEM AND METHOD," filed Dec. 3, 2018, which claims
priority as a continuation to U.S. patent application Ser. No.
15/859,147 entitled, "VEHICULAR HEALTH MONITORING SYSTEM AND
METHOD," filed Dec. 29, 2017, now U.S. Pat. No. 10,194,871 issued
Feb. 5, 2019 and both of which are hereby expressly incorporated by
reference herein.
[0011] U.S. patent application Ser. No. 16/779,453 claims priority
under 35 U.S.C. .sctn. 120 as a continuation in part application to
U.S. Utility application Ser. No. 15/958,620 entitled, "SYSTEM AND
METHOD FOR DETECTING A HEALTH CONDITION USING AN OPTICAL SENSOR,"
filed Apr. 20, 2018, now U.S. Pat. No. 10,524,720 issued Jan. 7,
2020 and hereby expressly incorporated by reference herein, which
claims priority under 35 U.S.C. .sctn. 120 as a continuation
application to U.S. Utility application Ser. No. 15/680,991
entitled, "SYSTEM AND METHOD FOR DETECTING A SEPSIS CONDITION,"
filed Aug. 18, 2017, now U.S. Pat. No. 9,968,289 issued May 15,
2018 and hereby expressly incorporated by reference herein.
[0012] U.S. patent application Ser. No. 16/779,453 claims priority
under 35 U.S.C. .sctn. 120 as a continuation in part application to
U.S. patent application Ser. No. 16/711,038 entitled, "SYSTEM AND
METHOD FOR MONITORING NITRIC OXIDE LEVELS USING A NON-INVASIVE,
MULTI-BAND BIOSENSOR," filed Dec. 11, 2019 and hereby expressly
incorporated by reference herein, which claims priority under 35
U.S.C. .sctn. 120 as a continuation to U.S. patent application Ser.
No. 15/718,721 entitled, "SYSTEM AND METHOD FOR MONITORING NITRIC
OXIDE LEVELS USING A NON-INVASIVE, MULTI-BAND BIOSENSOR," filed
Sep. 28, 2017, now U.S. patent Ser. No. 10/517,515 issued Dec. 31,
2019 and hereby expressly incorporated by reference herein, which
claims priority as a continuation application to U.S. Utility
application Ser. No. 15/622,941 entitled, "SYSTEM AND METHOD FOR
MONITORING NITRIC OXIDE LEVELS USING A NON-INVASIVE, MULTI-BAND
BIOSENSOR," filed Jun. 14, 2017, now U.S. Pat. No. 9,788,767 issued
Oct. 17, 2017, and hereby expressly incorporated by reference
herein, which claims priority under 35 U.S.C. .sctn. 119 to: [0013]
U.S. Provisional Application No. 62/463,104 entitled, "SYSTEM AND
METHOD FOR MONITORING NITRIC OXIDE LEVELS USING A NON-INVASIVE,
MULTI-BAND BIOSENSOR," filed Feb. 24, 2017, and hereby expressly
incorporated by reference herein.
[0014] U.S. patent application Ser. No. 16/779,453 claims priority
under 35 U.S.C. .sctn. 120 as a continuation in part application to
U.S. patent application Ser. No. 15/404,117 entitled, "SYSTEM AND
METHOD FOR HEALTH MONITORING INCLUDING A USER DEVICE AND
BIOSENSOR," filed Jan. 11, 2017, now U.S. Pat. No. 10,932,727
issued Mar. 2, 2021, and hereby expressly incorporated by reference
herein.
[0015] U.S. patent application Ser. No. 16/779,453 claims priority
under 35 U.S.C. .sctn. 120 as a continuation in part application to
U.S. patent application Ser. No. 16/183,354 entitled, "SYSTEM AND
METHOD FOR HEALTH MONITORING BY AN EAR PIECE," filed Nov. 7, 2018,
now U.S. Pat. No. 10,744,262 issued Aug. 18, 2020 and hereby
expressly incorporated by reference herein, which claims priority
under 35 U.S.C. .sctn. 120 as a continuation application to U.S.
patent application Ser. No. 15/485,816 entitled, "SYSTEM AND METHOD
FOR A DRUG DELIVERY AND BIOSENSOR PATCH," filed Apr. 12, 2017, now
U.S. Pat. No. 10,155,087 issued Dec. 18, 2018 and hereby expressly
incorporated by reference herein, which claims priority under 35
U.S.C. .sctn. 120 as a continuation application to U.S. Utility
application Ser. No. 15/276,760, entitled, "SYSTEM AND METHOD FOR A
DRUG DELIVERY AND BIOSENSOR PATCH," filed Sep. 26, 2016, now U.S.
Pat. No. 9,636,457 issued May 2, 2017, which is hereby expressly
incorporated by reference herein, which claims priority under 35
U.S.C. .sctn. 119 to: [0016] U.S. Provisional Application No.
62/383,313 entitled, "SYSTEM AND METHOD FOR A DRUG DELIVERY AND
BIOSENSOR PATCH," filed Sep. 2, 2016, and hereby expressly
incorporated by reference herein.
[0017] U.S. patent application Ser. No. 16/779,453 claims priority
under 35 U.S.C. .sctn. 120 as a continuation in part to U.S. patent
application Ser. No. 16/270,268 entitled, "SYSTEM AND METHOD FOR A
BIOSENSOR INTEGRATED IN A VEHICLE," filed Feb. 7, 2019, and hereby
expressly incorporated by reference herein, which claims priority
under 35 U.S.C. .sctn. 120 as a continuation application to U.S.
patent application Ser. No. 15/811,479 entitled, "SYSTEM AND METHOD
FOR A BIOSENSOR INTEGRATED IN A VEHICLE," filed Nov. 13, 2017, now
U.S. Pat. No. 10,238,346 issued Mar. 26, 2019 and hereby expressly
incorporated by reference herein, which claims priority under 35
U.S.C. .sctn. 120 as a continuation in part application to U.S.
patent application Ser. No. 15/490,813 entitled, "SYSTEM AND METHOD
FOR HEALTH MONITORING USING A NON-INVASIVE, MULTI-BAND BIOSENSOR,"
filed Apr. 18, 2017, now U.S. Pat. No. 9,980,676 issued May 29,
2018, which claims priority under 35 U.S.C. .sctn. 120 as a
continuation application to U.S. patent application Ser. No.
15/275,388 entitled, "SYSTEM AND METHOD FOR HEALTH MONITORING USING
A NON-INVASIVE, MULTI-BAND BIOSENSOR," filed Sep. 24, 2016, now
U.S. Pat. No. 9,642,578 issued May 9, 2017, which claims priority
under 35 U.S.C. .sctn. 119 to: [0018] U.S. Provisional Application
No. 62/307,375 entitled, "SYSTEM AND METHOD FOR HEALTH MONITORING
USING A NON-INVASIVE, MULTI-BAND BIOSENSOR," filed Mar. 11, 2016,
and hereby expressly incorporated by reference herein; and [0019]
U.S. Provisional Application No. 62/312,614 entitled, "SYSTEM AND
METHOD FOR DETERMINING BIOSENSOR DATA USING A BROAD SPECTRUM LIGHT
SOURCE," filed Mar. 24, 2016, and hereby expressly incorporated by
reference herein.
[0020] U.S. patent application Ser. No. 16/779,453 claims priority
under 35 U.S.C. .sctn. 120 as a continuation in part application to
U.S. patent application Ser. No. 15/400,916 entitled, "SYSTEM AND
METHOD FOR HEALTH MONITORING INCLUDING A REMOTE DEVICE," filed Jan.
6, 2017, now U.S. Pat. No. 10,750,981 issued Aug. 25, 2020, and
hereby expressly incorporated by reference herein.
[0021] U.S. patent application Ser. No. 16/779,453 claims priority
under 35 U.S.C. .sctn. 120 as a continuation in part to U.S. patent
application Ser. No. 16/391,175 entitled, "SYSTEM AND METHOD FOR
GLUCOSE MONITORING," filed Apr. 22, 2019, which claims priority
under 35 U.S.C. .sctn. 120 as a continuation application to U.S.
patent application Ser. No. 14/866,500 entitled, "SYSTEM AND METHOD
FOR GLUCOSE MONITORING," filed Sep. 25, 2015, now U.S. patent Ser.
No. 10/321,860 on Jun. 18, 2019, and hereby expressly incorporated
by reference herein, which claims priority under 35 U.S.C. .sctn.
119(e) to: [0022] U.S. Provisional Application No. 62/194,264
entitled, "SYSTEM AND METHOD FOR GLUCOSE MONITORING," filed Jul.
19, 2015, and hereby expressly incorporated by reference
herein.
FIELD
[0023] This application relates to systems and methods of
non-invasive health monitoring, and in particular, a system and
method for detection of glucose levels in blood flow using an
optical sensor.
BACKGROUND
[0024] A person's vitals, such as temperature, blood oxygen levels,
respiration rate, relative blood pressure, etc., may need to be
monitored periodically. Typically, monitoring a plurality of vitals
requires multiple instruments. For example, instruments for
obtaining vitals of a user include blood pressure cuffs,
thermometers, pulse oximeters, glucose level meters, etc.
[0025] The detection of substances and measurement of concentration
level or indicators of various substances in a user's blood stream
is important in health monitoring as well. Currently, detection of
concentration levels of blood substances is performed by drawing
blood from a blood vessel using a needle and syringe. The blood
sample is then transported to a lab for analysis. This type of
monitoring is invasive, non-continuous and time consuming.
[0026] One current non-invasive method is known for measuring the
oxygen saturation of blood using pulse oximeters. Pulse oximeters
detect oxygen saturation of hemoglobin by using, e.g.,
spectrophotometry to determine spectral absorbencies and
determining concentration levels of oxygen. In addition, pulse
oximetry may use photoplethysmography (PPG) methods for the
assessment of oxygen saturation in pulsatile arterial blood flow.
The subject's skin at a `measurement location` is illuminated with
two distinct wavelengths of light and the relative absorbance at
each of the wavelengths is determined. For example, a wavelength in
the visible red spectrum (for example, at 660 nm) has an extinction
coefficient of hemoglobin that exceeds the extinction coefficient
of oxihemoglobin. At a wavelength in the near infrared spectrum
(for example, at 940 nm), the extinction coefficient of
oxihemoglobin exceeds the extinction coefficient of hemoglobin. The
pulse oximeter filters the absorbance of the pulsatile fraction of
the blood (AC components), from the constant absorbance by
nonpulsatile venous or capillary blood or tissue pigments (DC
components), to eliminate the effect of tissue absorbance to
measure the oxygen saturation of arterial blood.
[0027] For example, when the heart pumps blood to the body and the
lungs during systole, the amount of blood that reaches the
capillaries in the skin surface increases, resulting in more light
absorption. The blood then travels back to the heart through the
venous network, leading to a decrease of blood volume in the
capillaries and less light absorption. The measured PPG waveform
therefore comprises a pulsatile (often called "AC") physiological
waveform that reflects synchronous changes in the blood volume with
a cardiac cycle, which is superimposed on a much larger slowly
varying quasi-static ("DC") baseline. The use of PPG techniques as
heretofore been mainly used for measurement of the oxygen
saturation of blood in vessels.
[0028] As such, there is a need for a non-invasive health
monitoring system and method that monitors health conditions of a
user non-invasively, continuously and in real time. In particular,
there is a need for an improved system and method for detection of
glucose levels in blood flow and vascular health.
SUMMARY
[0029] In an aspect, a system includes an optical circuit
configured to obtain a plurality of PPG signals at a plurality of
wavelengths reflected from or transmitted through tissue of a user,
wherein the different wavelengths have varying penetration depths
of tissue. The system also includes one or more processing devices
configured to determine one or more PPG parameters using the
plurality of PPG signals and determine a health index using the one
or more PPG parameters, wherein the health index indicates a
vascular health of the user.
[0030] In another aspect, a device includes at least one memory
configured to store a plurality of PPG signals at a plurality of
wavelengths reflected from or transmitted through tissue of a user,
wherein the different wavelengths have varying penetration depths
of tissue. The device also includes one or more processing circuits
configured to determine one or more R values using the plurality of
PPG signals; determine one or more PPG parameters using the
plurality of PPG signals; and determine a health index of the user
using the one or more R values and the one or more PPG
parameters.
[0031] In another aspect, a method includes obtaining a plurality
of PPG signals at a plurality of wavelengths reflected from or
transmitted through tissue of a user, wherein the different
wavelengths have varying penetration depths of tissue. The method
also includes determining one or more PPG parameters using the
plurality of PPG signals and determining a health index using the
one or more PPG parameters, wherein the health index indicates a
vascular health of the user.
[0032] In one or more of the above aspects, the one or more
processing devices are configured to determine the one or more PPG
parameters by determining at least one of the following PPG
parameters: a phase delay between a first PPG signal and a second
PPG signal of the plurality of PPG signals, a correlation of phase
shape between the first PPG signal and the second PPG signal of the
plurality of PPG signals or a periodicity of first PPG signal or
the second PPG signal of the plurality of PPG signals.
[0033] In one or more of the above aspects, the one or more
processing devices are further configured to determine an insulin
release event using at least a first PPG signal of the plurality of
PPG signals, wherein the first PPG signal is obtained from a
wavelength of light in a range of 380 nm-410 nm.
[0034] In one or more of the above aspects, the one or more
processing devices are further configured to determine the one or
more PPG parameters using the first PPG signal and the second PPG
signal obtained during the insulin release event.
[0035] In one or more of the above aspects, the one or more
processing devices are further configured to determine a relative
change in diameter of vessels during the insulin release event and
determine the health index using the relative change in diameter of
vessels.
[0036] In one or more of the above aspects, the one or more
processing devices are further configured to determine the one or
more PPG parameters by determining a first AC component of a first
PPG signal of the plurality of PPG signals, wherein the first PPG
signal is obtained using a wavelength of light in a range of 380
nm-410 nm and determining a second AC component of a second PPG
signal of the plurality of PPG signals, wherein the first PPG
signal is obtained using a wavelength of light equal to or above
660 nm.
[0037] In one or more of the above aspects, the one or more
processing devices are further configured to determine the one or
more PPG parameters by determining a first ratio R value using the
first AC component and the second AC component.
[0038] In one or more of the above aspects, the one or more
processing devices are further configured to determine the health
index, wherein the health index indicates a diabetic risk in the
user.
[0039] In one or more of the above aspects, the processing circuit
implements a regression neural network processing or a classifier
neural network processing to determine the glucose level.
[0040] In one or more of the above aspects, the one or more
processing circuits are configured to determine the one or more R
values using the plurality of PPG signals by determining a first R
value using a first PPG signal and a second PPG signal of the
plurality of PPG signals, wherein the first PPG signal is obtained
using a wavelength of light in a range of 380 nm-410 nm and the
second PPG signal is obtained using a wavelength of light at or
above 660 nm and determining a second R value using a first PPG
signal and a third PPG signal of the plurality of PPG signals,
wherein the third PPG signal is obtained using a wavelength of
light in a range of 510 nm-550 nm.
[0041] In one or more of the above aspects.
[0042] In one or more of the above aspects,
BRIEF DESCRIPTION OF THE DRAWINGS
[0043] FIG. 1 illustrates a schematic block diagram of exemplary
components in an embodiment of a biosensor.
[0044] FIG. 2 illustrates a schematic block diagram of an
embodiment of the PPG circuit in more detail.
[0045] FIG. 3 illustrates a logical flow diagram of an embodiment
of a method for determining concentration level of a substance in
blood flow using Beer-Lambert principles.
[0046] FIG. 4 illustrates the spectral response obtained at the
plurality of wavelengths with the systolic points and diastolic
points aligned over a cardiac cycle.
[0047] FIG. 5 illustrates a logical flow diagram of an embodiment
of a method of the biosensor.
[0048] FIG. 6 illustrates a logical flow diagram of an exemplary
method to determine levels of a substance in blood flow using the
PPG signals at a plurality of wavelengths.
[0049] FIG. 7 illustrates a logical flow diagram of an exemplary
method to determine levels of a substance using the spectral
responses at a plurality of wavelengths in more detail.
[0050] FIG. 8 illustrates a logical flow diagram of an exemplary
embodiment of a method for measuring a concentration level of a
substance in vivo using shifts in absorbance spectra.
[0051] FIG. 9A illustrates a schematic drawing of an exemplary
embodiment of a spectral response obtained using an embodiment of
the biosensor.
[0052] FIG. 9B illustrates a schematic drawing of an exemplary
embodiment of a pressure pulse waveform.
[0053] FIG. 10 illustrates a schematic drawing of an exemplary
embodiment of results of R values determined using a plurality of
methods.
[0054] FIG. 11A illustrates a schematic drawing of an exemplary
embodiment of an empirical calibration curve for correlating oxygen
saturation levels (SpO.sub.2) with R values.
[0055] FIG. 11B illustrates a schematic drawing of an exemplary
embodiment of an empirical calibration curve for correlating NO
levels with R values.
[0056] FIG. 12 illustrates a schematic block diagram of an
embodiment of a calibration database.
[0057] FIG. 13 illustrates a logical flow diagram of an embodiment
of a method for using a machine learning neural network technique
for detection of health data.
[0058] FIG. 14 illustrates a schematic diagram of a graph of PPG
signals during a period of vasodilation in vessels.
[0059] FIG. 15 illustrates a schematic diagram of a series of
graphs illustrating the effects of vasodilation in PPG signals.
[0060] FIG. 16 illustrates a schematic diagram illustrating phase
differences and average low frequency levels during vasodilation of
PPG signals of various wavelengths.
[0061] FIG. 17A illustrates a schematic block diagram of an
arterial wall under healthy conditions.
[0062] FIG. 17B illustrates a schematic block diagram of an
arterial wall with vascular dysfunction.
[0063] FIG. 18 illustrates a schematic diagram of PPG signals
obtained during periods of insulin release in vessels.
[0064] FIG. 19 illustrates a schematic diagram of graphs comparing
phase offset and pulse shape waveform in a plurality of PPG signals
during insulin release in vivo.
[0065] FIG. 20 illustrates a schematic block diagram of an insulin
response of a young healthy male and a middle-aged male.
[0066] FIG. 21 illustrates a schematic diagram of graphs comparing
phase offset and pulse shape waveform in a plurality of PPG signals
during insulin release in an adolescent male.
[0067] FIG. 22 illustrates a schematic diagram of an insulin
response in the adolescent male in greater detail.
[0068] FIG. 23 illustrates a schematic diagram of an insulin
response in the adolescent male in greater detail.
[0069] FIG. 24 illustrates a schematic diagram of graphs comparing
phase offset and pulse shape waveform in a plurality of PPG signals
during insulin release in a middle-aged male.
[0070] FIG. 25 illustrates a schematic diagram of an insulin
response in a middle-aged male in greater detail.
[0071] FIG. 26 illustrates a schematic diagram of an insulin
response in a middle-aged male in greater detail.
[0072] FIG. 27 illustrates a schematic flow diagram of an
embodiment of a method for determining vascular health using the
biosensor.
[0073] FIG. 28 illustrates a schematic flow diagram of an
embodiment of a method for determining an efficacy balance of ET-1
and NO in smooth muscle cells of vessels.
[0074] FIG. 29 illustrates a schematic flow diagram of an
embodiment of a method for determining an insulin level in blood
flow.
[0075] FIG. 30 illustrates schematic diagrams of measurements of
glucose levels in a plurality of patients obtained using the
biosensor in a clinical trial.
[0076] FIG. 31 illustrates schematic diagrams of measurements of
glucose levels in a plurality of patients obtained using the
biosensor in a clinical trial.
[0077] FIG. 32 illustrates a schematic flow diagram of an
embodiment of a method for determining glucose levels of a patient
with atypical vascular function.
[0078] FIG. 33 illustrates a schematic flow diagram of another
embodiment of a method for determining glucose levels of a patient
with atypical vascular function.
[0079] FIG. 34 illustrates a schematic diagram of graphs of PPG
signals during deep inhalation.
[0080] FIG. 35A illustrates a schematic diagram of graphs of PPG
signals detected from a critical care patient diagnosed with
sepsis.
[0081] FIG. 35B illustrates graphical representations 3520 of PPG
signals detected from a clinical trial of patients.
[0082] FIG. 36 illustrates a schematic diagram of graphs of PPG
signals during periods of ingestion and fasting.
[0083] FIG. 37 illustrates a schematic flow diagram of an
embodiment of a method for identifying a PPG feature, such as an
insulin release pulse or deep inhalation pulse.
[0084] FIG. 38 illustrates a graphical representations of test
results obtained from an embodiment of the biosensor.
[0085] FIG. 39 illustrates graphical representations of additional
test results obtained from an embodiment of the biosensor.
[0086] FIG. 40 illustrates graphical representations of additional
test results obtained from an embodiment of the biosensor.
[0087] FIG. 41 illustrates graphical representations of additional
test results obtained from an embodiment of the biosensor.
[0088] FIG. 42 illustrates graphical representations of additional
test results obtained from an embodiment of the biosensor.
[0089] FIG. 43 illustrates a schematic flow diagram of an
embodiment of a method for performing a health screening.
[0090] FIG. 44 illustrates a schematic block diagram of an
embodiment of a processing device for processing the one or more
parameters.
[0091] FIG. 45A illustrates a graphical representation of clinical
test results for a first plurality of patients.
[0092] FIG. 45B illustrates a graphical representation of clinical
test results for a second plurality of patients.
[0093] FIG. 46 illustrates a graphical representation of a
distribution of errors between the predicted glucose levels and the
reference glucose levels.
[0094] FIG. 47 illustrates a schematic flow diagram of an
embodiment of a method for determining a concentration level of
glucose using a plurality of parameters.
[0095] FIG. 48 illustrates a schematic flow diagram of an
embodiment of a method for determining a concentration of glucose
in blood flow using a plurality of parameters in more detail.
[0096] FIG. 49 illustrates graphical representations of additional
test results obtained from an embodiment of the biosensor.
[0097] FIG. 50 illustrates graphical representations of additional
test results obtained from an embodiment of the biosensor.
[0098] FIG. 51 illustrates graphical representations of additional
test results obtained from an embodiment of the biosensor.
[0099] FIG. 52 illustrates graphical representations of additional
test results obtained from an embodiment of the biosensor.
[0100] FIG. 53 illustrates graphical representations of additional
test results obtained from an embodiment of the biosensor.
[0101] FIG. 54 illustrates a schematic flow diagram of an
embodiment of a method for determining a concentration of a
substance in blood flow using a plurality of parameters.
[0102] FIG. 55 illustrates a schematic flow diagram of an
embodiment of a method for determining a vascular health index.
[0103] FIG. 56 illustrates a schematic diagram of endothelial
dysfunction.
[0104] FIG. 57 illustrates a schematic flow diagram of an
embodiment of a method for determining the endothelial dysfunction
index.
[0105] FIG. 58 illustrates a logical flow diagram of an embodiment
of a method for determining one or more health indices.
[0106] FIG. 59 illustrates a logical flow diagram of an embodiment
of a method for determining a health condition using a plurality of
PPG parameters.
DETAILED DESCRIPTION
[0107] The word "exemplary" or "embodiment" is used herein to mean
"serving as an example, instance, or illustration." Any
implementation or aspect described herein as "exemplary" or as an
"embodiment" is not necessarily to be construed as preferred or
advantageous over other aspects of the disclosure. Likewise, the
term "aspects" does not require that all aspects of the disclosure
include the discussed feature, advantage, or mode of operation.
[0108] Embodiments will now be described in detail with reference
to the accompanying drawings. In the following description,
numerous specific details are set forth in order to provide a
thorough understanding of the aspects described herein. It will be
apparent, however, to one skilled in the art, that these and other
aspects may be practiced without some or all of these specific
details. In addition, well known steps in a method of a process may
be omitted from flow diagrams presented herein in order not to
obscure the aspects of the disclosure. Similarly, well known
components in a device may be omitted from figures and descriptions
thereof presented herein in order not to obscure the aspects of the
disclosure.
Overview
[0109] In an embodiment, a biosensor includes an optical sensor or
photoplethysmography (PPG) circuit configured to transmit light at
a plurality of wavelengths directed at skin tissue of a user. The
user may include any animal, human or non-human. The PPG circuit
detects the light reflected from the skin tissue or transmitted
through the skin tissue and generates one or more PPG signals at
one or more wavelengths. A processing circuit integrated in the
biosensor or in communication with the biosensor processes the PPG
signals to obtain a user's vitals, concentrations of substances in
blood flow and/or other health information.
[0110] In an embodiment described herein, a plurality of PPG
parameters are determined using PPG signals at a plurality of
wavelengths. The PPG parameters include R values, L values, phase
delay between two or more of the plurality of PPG signals, a
correlation of phase shape between two or more of the plurality of
PPG signals or a periodicity of one or more the plurality of PPG
signals. Other types of parameters, such as skin temperature, may
also be obtained by the biosensor. The plurality of parameters are
then analyzed to determine a glucose level in blood flow of the
user.
Embodiment of the Biosensor
[0111] FIG. 1 illustrates a schematic block diagram of exemplary
components in an embodiment of the biosensor 100. The biosensor 100
may include one or more processing circuits 102 communicatively
coupled to one or more memory devices 104. In one aspect, the
memory device 104 may include one or more non-transitory processor
readable memories that store instructions which when executed by
the one or more processing circuits 102, causes the one or more
processing circuits 102 to perform one or more functions described
herein. The processing circuit 102 may be co-located with one or
more of the other circuits of the biosensor 100 in a same physical
circuit board or located separately in a different circuit board.
The processing circuit 102 may also be communicatively coupled to
other processing circuits, such as in another user device, a
central control module or a server in a remote location, wherein
the other processing circuits perform one or more functions
described herein. The biosensor 100 may be battery operated and
include a battery 118, such as a lithium ion battery. The memory
device 104 may store spectral data 106 or health data 120 obtained
by the biosensor 100.
[0112] The biosensor 100 may include a temperature sensor 114
configured to detect a temperature of a user. For example, the
temperature sensor 108 may include an array of sensors (e.g.,
16.times.16 pixels) to detect a skin temperature of a user. The
temperature sensor 114 may also be used to calibrate the PPG
circuit 110, such as the wavelength output of LEDs or other light
sources. The biosensor 100 may include a display 116 to display
biosensor data or control interfaces for the biosensor 100.
[0113] The biosensor 100 further includes a transceiver 112. The
transceiver 112 may include a wireless or wired transceiver
configured to communicate with or with one or more devices over a
LAN, MAN and/or WAN. In one aspect, the wireless transceiver may
include a Bluetooth enabled (BLE) transceiver or IEEE 802.11ah,
Zigbee, IEEE 802.15-11 or WLAN (such as an IEEE 802.11 standard
protocol) compliant transceiver. In another aspect, the wireless
transceiver may operate using RFID, short range radio frequency,
infrared link, or other short range wireless communication
protocol. In another aspect, the wireless transceiver may also
include or alternatively include an interface for communicating
over a cellular network. The transceiver 112 may also include a
wired transceiver interface, e.g., a USB port or other type of
wired connection, for communication with one or more other devices
over a LAN, MAN and/or WAN. The transceiver 112 may include a
wireless or wired transceiver configured to communicate with a
vehicle or its components over a controller area network (CAN),
Local Interconnect Network (LIN), Flex Ray, Media Oriented Systems
Transport (MOST), (On-Board Diagnostics II), Ethernet or using
another type of network or protocol. The biosensor 100 may transmit
health data using the transceiver 112 over a wide area network,
such as a cellular network, to a third party service provider, such
as a health care provider or emergency service provider.
[0114] The biosensor 100 may also include a motion sensor 114
configured to detect motion of the biosensor 100 or patient. In an
embodiment, the motion sensor 114 includes an accelerometer. Due to
motion, a signal quality of the PPG signal may decline. In an
embodiment, an acceptable tolerance for a PPG signal quality
indicator may be set. When a motion level exceeds a threshold, then
the PPG data may be ignored to avoid measurement errors. The
biosensor may be programmed to reset after a predetermined level of
motion (e.g., a speed or an acceleration) is exceeded.
[0115] The biosensor 100 may be included in one or more different
form factors over various types of tissue, such as a watch, ring,
patch, earpiece, earbud, etc. In an embodiment, a small form factor
such as a ring or patch, may include a PPG circuit 110 and
transceiver to communicate via a wireless or wired connection with
a remote device, such as a watch, smart phone, computer, glasses,
or other user device. The remote device may include another PPG
circuit 110 and/or may include the processing circuits 102 and
memory 104 for processing of data received from the remote
sensor.
Embodiment--PPG Circuit
[0116] FIG. 2 illustrates a schematic block diagram of an
embodiment of the PPG circuit 110 in more detail. The PPG circuit
110 includes a light source 210 configured to emit a plurality of
wavelengths of light across various spectrums. The plurality of
LEDs 212a-n are configured to emit light in one or more spectrums,
including infrared (IR) light, ultraviolet (UV) light, near IR
light or visible light, in response to driver circuit 218. For
example, the biosensor 100 may include a first LED 212a that emits
visible light and a second LED 212b that emits infrared light and a
third LED 212c that emits UV light, etc. In another embodiment, one
or more of the light sources 210 may include tunable LEDs or lasers
operable to emit light over one or more frequencies or ranges of
frequencies or spectrums in response to driver circuit 218.
[0117] In an embodiment, the driver circuit 218 is configured to
control the one or more LEDs 212a-n to generate light at one or
more frequencies for predetermined periods of time. The driver
circuit 218 may control the LEDs 212a-n to operate concurrently or
consecutively. The driver circuit 218 is configured to control a
power level, emission period and frequency of emission of the LEDs
212a-n. The driver circuit 218 may also tune a wavelength output of
the LEDs 212a-n in response to a temperature or other feedback. The
biosensor 100 is thus configured to emit one or more wavelengths of
light in one or more spectrums that is directed at the surface or
epidermal layer of the skin tissue of a user. The emitted light 216
passes through at least one aperture 214 and towards the surface or
epidermal layer of the skin tissue of a user.
[0118] The PPG circuit 110 further includes one or more
photodetector circuits 230a-n. The photodetector circuits 230 may
be implemented as part of a camera 250. For example, a first
photodetector circuit 230 may be configured to detect visible light
and the second photodetector circuit 230 may be configured to
detect IR light. Alternatively, a single photodetector 230 may be
implemented to detect light across multiple spectrums. When
multiple photodetectors 230 are implemented, the detected signals
obtained from each of the photodetectors may be added or averaged.
Alternatively, a detected light signal with more optimal signal to
noise ratio may be selected from the multiple photodetector
circuits 230a-n.
[0119] The first photodetector circuit 230a and the second
photodetector circuit 230n may also include a first filter 260 and
a second filter 262 configured to filter ambient light and/or
scattered light. For example, in some embodiments, only light
reflected at an approximately perpendicular angle to the skin
surface of the user is desired to pass through the filters. The
first photodetector circuit 230a and the second photodetector
circuit 230n are coupled to a first analog to digital (A/D) circuit
236 and a second A/D circuit 238. Alternatively, a single A/D
circuit may be coupled to each of the photodetector circuits
230a-n. The A/D circuits convert the spectral responses to digital
spectral data for processing by a DSP or other processing
circuit.
[0120] The one or more photodetector circuits 230a-n include one or
more types of spectrometers or photodiodes or other types of light
detection circuits configured to detect an intensity of light as a
function of wavelength over a time period to obtain a spectral
response. In use, the one or more photodetector circuits 230a-n
detect the intensity of reflected light 240 from skin tissue of a
user that enters one or more apertures 220a-n of the biosensor 100.
In another example, the one or more photodetector circuits 230a-n
detect the intensity of light due to transmissive absorption (e.g.,
light transmitted through tissues, such as a fingertip or ear
lobe). The one or more photodetector circuits 230a-n then obtain a
spectral response (a PPG signal) of the reflected or transmissive
light by measuring an intensity of the light at one or more
wavelengths over a period of time.
[0121] In another embodiment, the light source 210 may include a
broad spectrum light source, such as a white light to infrared (IR)
or near IR LED, that emits light with wavelengths across multiple
spectrums, e.g. from 350 nm to 2500 nm. Broad spectrum light
sources with different ranges may be implemented. In an aspect, a
broad spectrum light source is implemented with a range across 100
nm wavelengths to 2000 nm range of wavelengths in the visible, IR
and/or UV frequencies. For example, a broadband tungsten light
source for spectroscopy may be used. The spectral response of the
reflected light 240 is then measured across the wavelengths in the
broad spectrum, e.g. from 350 nm to 2500 nm, concurrently. In an
aspect, a charge coupled device (CCD) spectrometer may be
configured in the photodetector circuit 230 to measure the spectral
response of the detected light over the broad spectrum.
[0122] The PPG circuit 110 may also include a digital signal
processing (DSP) circuit 270 that includes signal processing of the
digital spectral data. For example, the DSP circuit may determine
AC or DC components from the spectral responses (PPG signals) or
diastolic and systolic points or other spectral data 106. The
spectral data may then be processed by the processing circuit 102
to obtain health data 120 of a user. The spectral data 106 may
alternatively or in additionally be transmitted by the biosensor
100 to a central control module for processing to obtain health
data 120 of a user. The spectral data 106, PPG signals, etc. may be
stored in the memory device 104 of the biosensor 100.
[0123] In use, the biosensor 100 performs PPG techniques using the
PPG circuit 110 to detect the concentration levels of one or more
substances in blood flow. In one aspect, the biosensor 100 receives
reflected light or transmissive light from skin tissue to obtain a
spectral response. The spectral response (or PPG signal) includes a
spectral curve that illustrates an intensity or power or energy at
a frequency or wavelength in a spectral region of the detected
light over a period of time. The ratio of the resonance absorption
peaks from two different frequencies can be calculated and based on
the Beer-Lambert law used to obtain the levels of substances in the
blood flow.
[0124] For example, one or more of the embodiments of the biosensor
100 described herein is configured to detect a concentration level
of one or more substances within blood flow using PPG techniques.
For example, the biosensor 100 may detect nitric oxide (NO)
concentration levels and correlate the NO concentration level to a
blood glucose level. The biosensor 100 may also detect oxygen
saturation (SaO2 or SpO2) levels in blood flow. The biosensor may
also be configured to detect a liver enzyme cytochrome oxidase
(P450) enzyme and correlate the P450 concentration level to a blood
alcohol level.
[0125] The spectral response of a substance or substances in the
arterial blood flow is determined in a controlled environment, so
that an absorption coefficient .alpha..sub.g1 can be obtained at a
first light wavelength .lamda.1 and at a second wavelength
.lamda.2. According to the Beer-Lambert law, light intensity will
decrease logarithmically with path length/(such as through an
artery of length l). Assuming then an initial intensity I.sub.in of
light is passed through a path length l, a concentration C.sub.g of
a substance may be determined. For example, the concentration Cg
may be obtained from the following equations:
At the first wavelength .lamda..sub.1,
I.sub.1=I.sub.in1*10.sup.-(.alpha..sup.g1.sup.c.sup.gw.sup.+.alpha..sup.w-
1.sup.c.sup.w.sup.)*l
At the second wavelength .lamda..sub.2,
I.sub.2=I.sub.in2*10.sup.-(.alpha..sup.g2.sup.C.sup.gw.sup..alpha..sup.w2-
.sup.c.sup.w.sup.)*l
wherein:
[0126] I.sub.in1 is the intensity of the initial light at
.lamda..sub.1
[0127] I.sub.in2 is the intensity of the initial light at
.lamda..sub.2
[0128] .alpha..sub.g1 is the absorption coefficient of the
substance in arterial blood at .lamda..sub.1
[0129] .alpha..sub.g2 is the absorption coefficient of the
substance in arterial blood at .lamda..sub.2
[0130] .alpha..sub.w1 is the absorption coefficient of arterial
blood at .lamda..sub.1
[0131] .alpha..sub.w2 is the absorption coefficient of arterial
blood at .lamda..sub.2
[0132] C.sub.gw is the concentration of the substance and arterial
blood
[0133] C.sub.w is the concentration of arterial blood
[0134] Then letting R equal:
R = log .times. .times. 10 .times. ( I .times. .times. 1 Iin
.times. .times. 1 ) log .times. .times. 10 .times. ( I .times.
.times. 2 Iin .times. .times. 2 ) ##EQU00001##
[0135] The concentration of the substance Cg may then be equal
to:
C .times. g = C .times. g .times. w C .times. g .times. w + C
.times. w = .alpha. w .times. 2 .times. R - .alpha. w .times. 1 (
.alpha. w2 - .alpha. g .times. w .times. 2 ) * R - ( .alpha. w
.times. 1 - .alpha. gw .times. .times. 1 ) ##EQU00002##
[0136] The biosensor 100 may thus determine the concentration of
various substances in blood flow from the Beer-Lambert principles
using the spectral responses of at least two different
wavelengths.
[0137] FIG. 3 illustrates a logical flow diagram of an embodiment
of a method 300 for determining concentration level of a substance
in blood flow using Beer-Lambert principles. The biosensor 100
transmits light at a first predetermined wavelength and at a second
predetermined wavelength. The biosensor 100 detects the light
(reflected from the skin or transmitted through the skin) and
determines the spectral response at the first wavelength at 302 and
at the second wavelength at 304. The biosensor 100 then determines
health data, such as an indicator or concentration level of
substances in blood flow, using the spectral responses of the first
and second wavelength at 306. In general, the first predetermined
wavelength is selected that has a high absorption coefficient for
the substance in blood flow while the second predetermined
wavelength is selected that has a lower absorption coefficient for
the substance in blood flow. Thus, it is generally desired that the
spectral response for the first predetermined wavelength have a
higher intensity level in response to the substance than the
spectral response for the second predetermined wavelength.
[0138] In an embodiment, the biosensor 100 may detect a
concentration level of nitric oxide (NO) in blood flow using a
first predetermined wavelength with a high absorption coefficient
for NO in a range of 380-410 nm and in particular at 390 nm or 395
nm. In another aspect, the biosensor 100 may transmit light at the
first predetermined wavelength in a range of approximately 1 nm to
50 nm around the first predetermined wavelength. Similarly, the
biosensor 100 may transmit light at the second predetermined
wavelength in a range of approximately 1 nm to 50 nm around the
second predetermined wavelength. The range of wavelengths is
determined based on the spectral response since a spectral response
may extend over a range of frequencies, not a single frequency
(i.e., it has a nonzero linewidth). The light that is reflected or
transmitted by NO may spread over a range of wavelengths rather
than just the single predetermined wavelength. In addition, the
center of the spectral response may be shifted from its nominal
central wavelength or the predetermined wavelength. The range of 1
nm to 50 nm is based on the bandwidth of the spectral response line
and should include wavelengths with increased light intensity
detected for the targeted substance around the predetermined
wavelength.
[0139] The first spectral response (or first PPG signal) of the
light over the first range of wavelengths including the first
predetermined wavelength and the second spectral response (or
second PPG signal) of the light over the second range of
wavelengths including the second predetermined wavelengths is then
generated at 302 and 304. The biosensor 100 analyzes the first and
second spectral responses to detect an indicator or concentration
level of NO in the arterial blood flow at 306. In another
embodiment, using absorption coefficients for both Nitric Oxide and
Hemoglobin, the concentration of Nitric Oxide can be obtained in
blood. A calibration table may then correlate amounts of glucose
(mG/DL) in relation to R values 395/940 nm. The concentration level
of NO as used herein may thus include NO in gaseous form in blood
flow and/or NO attached to hemoglobin compounds in the blood
flow.
[0140] In another example, the biosensor 100 may also detect
vitals, such as heart rate, respiration rate and pulse pressure.
The biosensor 100 may also determine a level of vasodilation and a
period of vasodilation as described in more detail herein. Because
blood flow to the skin can be modulated by multiple other
physiological systems, the biosensor 100 may also be used to
monitor vascular health, such as hypovolemia or other circulatory
conditions.
[0141] Photoplethysmography (PPG) is used to measure time-dependent
volumetric properties of blood in blood vessels due to the cardiac
cycle. For example, the heartbeat affects the volume of blood flow
and the concentration or absorption levels of substances being
measured in the arterial blood flow. Over a cardiac cycle,
pulsating arterial blood changes the volume of blood flow in a
blood vessel. Incident light I.sub.O is directed at a tissue site
and a certain amount of light is reflected or transmitted and a
certain amount of light is absorbed. At a peak of blood flow or
volume in a cardiac cycle, the reflected/transmitted light I.sub.L
is at a minimum due to absorption by the increased blood volume,
e.g., due to the pulsating blood in the vessel. At a minimum of
blood volume during the cardiac cycle, the transmitted/reflected
light I.sub.H 416 is at a maximum due to lack of absorption from
the pulsating blood.
[0142] The biosensor 100 is configured to filter the
reflected/transmitted light I.sub.L of the pulsating blood from the
transmitted/reflected light I.sub.H. This filtering isolates the
light due to reflection/transmission of the pulsating blood from
the light due to reflection/transmission from non-pulsating blood,
vessel walls, surrounding tissue, etc. The biosensor 100 may then
measure the concentration levels of one or more substances from the
reflected/transmitted light I.sub.L 814 in the pulsating blood.
[0143] For example, incident light I.sub.O is directed at a tissue
site at one or more wavelengths. The reflected/transmitted light I
is detected by a photodetector or sensor array in a camera. At a
peak of blood flow or volume, the reflected light I.sub.L 414 is at
a minimum due to absorption by the pulsating blood, non-pulsating
blood, other tissue, etc. At a minimum of blood flow or volume
during the cardiac cycle, the Incident or reflected light I.sub.H
416 is at a maximum due to lack of absorption from the pulsating
blood volume. Since the light I is reflected or traverses through a
different volume of blood at the two measurement times, the
measurement provided by a PPG sensor is said to be a `volumetric
measurement` descriptive of the differential volumes of blood
present at a certain location within the user's vessels at
different times during the cardiac cycle. These principles
described herein may be applied to venous blood flow and arterial
blood flow.
[0144] In general, the relative magnitudes of the AC and DC
contributions to the reflected/transmitted light signal I may be
determined. In general, AC contribution of the reflected light
signal I is due to the pulsating blood flow. A difference function
may thus be computed to determine the relative magnitudes of the AC
and DC components of the reflected light I to determine the
magnitude of the reflected light due to the pulsating blood flow.
The described techniques herein for determining the relative
magnitudes of the AC and DC contributions is not intended as
limiting. It will be appreciated that other methods may be employed
to isolate or otherwise determine the relative magnitude of the
light I.sub.L due to pulsating blood flow (arterial and/or
venous).
[0145] In one aspect, the spectral response obtained at each
wavelength may be aligned based on the systolic 402 and diastolic
404 points in their respective spectral responses. This alignment
is useful to associate each spectral response with a particular
stage or phase of the pulse-induced local pressure wave within the
blood vessel (which roughly mimics the cardiac cycle 406 and thus
include systolic and diastolic stages and sub-stages thereof). This
temporal alignment helps to determine the absorption measurements
acquired near a systolic point in time of the cardiac cycle and
near the diastolic point in time of the cardiac cycle 406
associated with the local pressure wave within the user's blood
vessels. This measured local pulse timing information may be useful
for properly interpreting the absorption measurements in order to
determine the relative contributions of the AC and DC components
measured by the biosensor 100. So, for one or more wavelengths, the
systolic points 402 and diastolic points 404 in the spectral
response are determined. These systolic points 402 and diastolic
points 404 for the one or more wavelengths may then be aligned as a
method to discern concurrent responses across the one or more
wavelengths.
[0146] In another embodiment, the systolic points 402 and diastolic
points 404 in the absorbance measurements are temporally correlated
to the pulse-driven pressure wave within the blood vessels--which
may differ from the cardiac cycle. In another embodiment, the
biosensor 100 may concurrently measure the intensity reflected at
each the plurality of wavelengths. Since the measurements are
concurrent, no alignment of the spectral responses of the plurality
of wavelengths may be necessary. FIG. 4 illustrates the spectral
response obtained at the plurality of wavelengths with the systolic
points 402 and diastolic points 404 aligned over a cardiac cycle
406.
[0147] FIG. 5 illustrates a logical flow diagram of an embodiment
of a method 500 of the biosensor 100. In one aspect, the biosensor
100 emits and detects light at a plurality of predetermined
frequencies or wavelengths, such as approximately 940 nm, 660 nm,
390 nm, 592 nm, and 468 nm or in ranges thereof. The light is
pulsed for a predetermined period of time (such as 100 usec or 200
Hz) sequentially or simultaneously at each predetermined
wavelength. In another aspect, light may be pulsed in a wavelength
range of 1 nm to 50 nm around each of the predetermined
wavelengths. For example, for the predetermined wavelength 390 nm,
the biosensor 100 may transmit light directed at skin tissue of the
user in a range of 360 nm to 410 nm including the predetermined
wavelength 390 nm. For the predetermined wavelength of 940 nm, the
biosensor 100 may transmit light directed at the skin tissue of the
user in a range of 920 nm to 975 nm. In another embodiment, the
light is pulsed simultaneously at least at each of the
predetermined wavelengths (and in a range around the
wavelengths).
[0148] The spectral responses are obtained around the plurality of
wavelengths, including at least a first wavelength and a second
wavelength at 502. The spectral responses may be measured over a
predetermined period (such as 300 usec.) or at least over 2-3
cardiac cycles. This measurement process is repeated continuously,
e.g., pulsing the light at 10-100 Hz and obtaining spectral
responses over a desired measurement period, e.g. from 1-2 seconds
to 1-2 minutes or from 2-3 hours to continuously over days or
weeks. The spectral data obtained by the PPG circuit 110, such as
the digital or analog spectral responses, may be processed locally
by the biosensor 100 or transmitted to a central control module for
processing.
[0149] The systolic and diastolic points of the spectral response
are then determined. Because the human pulse is typically on the
order of magnitude of one 1 Hz, typically the time differences
between the systolic and diastolic points are on the order of
magnitude of milliseconds or tens of milliseconds or hundreds of
milliseconds. Thus, spectral response measurements may be obtained
at a frequency of around 10-100 Hz over the desired measurement
period. The spectral responses are obtained over one or more
cardiac cycles and systolic and diastolic points of the spectral
responses are determined. Preferably, the spectral response is
obtained over at least three cardiac cycles in order to obtain a
heart rate.
[0150] A low pass filter (such as a 5 Hz low pass filter) is
applied to the spectral response signal at 504. The relative
contributions of the AC and DC components are obtained I.sub.AC+DC
and I.sub.AC. A peak detection algorithm is applied to determine
the systolic and diastolic points at 506. If not detected
concurrently, the systolic and diastolic points of the spectral
response for each of the wavelengths may be aligned or may be
aligned with systolic and diastolic points of a pressure pulse
waveform or cardiac cycle.
[0151] Beer Lambert equations are then applied as described herein.
For example, the L.sub..lamda. values are then calculated for the
first wavelength .lamda..sub.1 at 508 and the second wavelength
.lamda..sub.2 at 510, wherein the L.sub..lamda. values for a
wavelength equals:
L .lamda. = Log .times. .times. 10 .times. .times. ( IAC + DC IDC )
##EQU00003##
[0152] wherein I.sub.AC+DC is the intensity of the detected light
with AC and DC components and I.sub.DC is the intensity of the
detected light with the AC component filtered by the low pass
filter. The value L.sub..lamda. isolates the spectral response due
to pulsating arterial blood flow, e.g. the AC component of the
spectral response. Though the L.sub..lamda. value is described in
one embodiment by this equation, the L value includes alternate
computations that represents the value of the AC component of the
spectral response. For example, the L value may be represented
alternatively by one or more of:
L .lamda. = IAC IDC .times. .times. or .times. .times. L .lamda. =
IAC + DC IDC ##EQU00004##
[0153] A ratio R of the L.sub..lamda. values at two wavelengths may
then be determined at 512. For example, the ratio R may be obtained
from the following:
Ratio .times. .times. R = L .times. .lamda. .times. 1 L .times.
.lamda. .times. 2 ##EQU00005##
[0154] The R value is thus a ratio of AC components of spectral
responses at different wavelengths. The L values and R values may
be determined continuously, e.g. every 1-2 seconds, and the
obtained L.sub..lamda. values and/or R values averaged or meaned
over a predetermined time period, such as over 1-2 minutes. The
concentration level of a substance may then be obtained from the R
value and a calibration database at 514. The bio sensor 100 may
substantially continuously monitor a user over 2-3 hours or over
days or weeks.
[0155] In one embodiment, the R390,940 value with
L.sub..lamda.1=390nm and L.sub..lamda.2=940 may be non-invasively
and quickly and easily obtained using the biosensor 100 to
determine a concentration level of nitric oxide NO in blood flow of
a user. In particular, in unexpected results, it is believed that
the nitric oxide NO levels in the blood flow is being measured at
least in part by the biosensor 100 at wavelengths in the range of
380-410 and in particular at .lamda..sub.1=390 nm. Thus, the
biosensor 100 measurements to determine the L.sub.390nm values are
the first time NO concentration levels in blood flow have been
measured directly in vivo. These and other aspects of the biosensor
100 are described in more detail herein with clinical trial
results.
Embodiment--Determination of Concentration Level of a Substance
Using PPG Signals at a Plurality of Wavelengths
[0156] FIG. 6 illustrates a logical flow diagram of an exemplary
method 600 to determine levels of a substance in blood flow using
the PPG signals at a plurality of wavelengths. The absorption
coefficient of a substance may be sufficiently higher at a
plurality of wavelengths, e.g. due to isoforms or derivative
compounds. For example, the increased intensity of light at a
plurality of wavelengths may be due to reflectance by isoforms or
other compounds in the arterial blood flow. Another method for
determining the concentration levels may then be used by measuring
the spectral responses and determining L and R values at a
plurality of different wavelengths of light. In this example then,
the concentration level of the substance is determined using
spectral responses at multiple wavelengths. An example for
calculating the concentration of a substance over multiple
wavelengths may be performed using a linear function, such as is
illustrated herein below.
LN(I.sub.1-n)=.SIGMA..sub.i=0.sup.n.mu.i*Ci
[0157] wherein,
[0158] I.sub.1-n=intensity of light at wavelengths
.lamda..sub.1-n.
[0159] .mu..sub.n=absorption coefficient of substance 1, 2, . . . n
at wavelengths .mu..sub.1-n
[0160] C.sub.n=Concentration level of substance 1, 2, . . . n
When the absorption coefficients of a substance, its isoforms or
other compounds including the substance are known at the
wavelengths then the concentration level C of the substances may be
determined from the spectral responses at the wavelengths (and
e.g., including a range of 1 nm to 50 nm around each of the
wavelengths). The concentration level of the substance may be
isolated from the isoforms or other compounds by compensating for
the concentration of the compounds. Thus, using the spectral
responses at multiple frequencies provides a more robust
determination of the concentration level of a substance.
[0161] In use, the biosensor 100 transmits light directed at skin
tissue at a plurality of wavelengths or over a broad spectrum at
602. The spectral response of light from the skin tissue is
detected at 604, and the spectral responses are analyzed at a
plurality of wavelengths (and in one aspect including a range of
+/-10 to 50 nm around each of the wavelengths) at 606. Then, the
concentration level C of the substance may be determined using the
spectral responses at the plurality of wavelengths at 608. The
concentration level of the substance may be isolated from isoforms
or other compounds by compensating for the concentration of the
compounds. For example, using absorption coefficients for Nitric
Oxide and Hemoglobin, the amount of Nitric Oxide can be obtained in
arterial blood. A calibration table using human subjects may then
correlate amounts of glucose (mG/DL) in relation to R values (NoHb)
395/940 nm.
[0162] FIG. 7 illustrates a logical flow diagram of an exemplary
method 700 to determine levels of a substance using the spectral
responses at a plurality of wavelengths in more detail. The
spectral responses are obtained at 702. The spectral response
signals include AC and DC components I.sub.AC+DC. A low pass filter
(such as a 5 Hz low pass filter) is applied to each of the spectral
response signals I.sub.AC+DC to isolate the DC component of each of
the spectral response signals I.sub.DC at 704. The AC fluctuation
is due to the pulsatile expansion of the vessels due to the volume
increase in pulsating blood. In order to measure the AC
fluctuation, measurements are taken at different times and a peak
detection algorithm is used to determine the diastolic point and
the systolic point of the spectral responses at 706. A Fast Fourier
transform (FFT) algorithm may also be used to isolate the DC
component I.sub.DC and AC component of each spectral response
signal at 706. A differential absorption technique may also be used
as described in more detail herein. The I.sub.DC component is thus
isolated from the spectral signal at 708.
[0163] The I.sub.AC+DC and I.sub.DC components are then used to
compute the L values at 710. For example, a logarithmic function
may be applied to the ratio of I.sub.AC+DC and I.sub.DC to obtain
an L value for each of the wavelengths L.sub..lamda.1-n. Since the
respiratory cycle affects the PPG signals, the L values may be
averaged over a respiratory cycle and/or over another predetermined
time period (such as over a 1-2 minute time period) or over a
plurality of cardiac cycles at 712.
[0164] In an embodiment, isoforms of a substance may be attached in
the blood stream to one or more types of hemoglobin compounds. The
concentration level of the hemoglobin compounds may then need to be
accounted for to isolate the concentration level of the substance
from the hemoglobin compounds. For example, nitric oxide (NO) is
found in the blood stream in a gaseous form and also attached to
hemoglobin compounds. Thus, the spectral responses obtained around
390 nm (+/-20 nm) may include a concentration level of the
hemoglobin compounds as well as nitric oxide. The hemoglobin
compound concentration levels must thus be compensated for to
isolate the nitric oxide. Multiple wavelengths and absorption
coefficients for hemoglobin are used to determine a concentration
of the hemoglobin compounds at 714. Other methods may also be used
to obtain a concentration level of hemoglobin in the blood flow as
well. The concentration of the hemoglobin compounds is then
adjusted from the measurements at 716. The concentration values of
the substance may then be obtained at 718. For example, the R
values are then determined at 718.
[0165] To determine a concentration level of the substance, a
calibration table or database is used that associates the obtained
R value to a concentration level of the substance at 720. The
calibration database correlates the R value with a concentration
level. The calibration database may be generated for a specific
user or may be generated from clinical data of a large sample
population. For example, it is determined that the R values should
correlate to similar NO concentration levels across a large sample
population. Thus, the calibration database may be generated from
testing of a large sample of a general population to associate R
values and NO concentration levels.
[0166] In addition, the R values may vary depending on various
factors, such as underlying skin tissue. For example, the R values
may vary for spectral responses obtained from an abdominal area
versus measurements from a wrist or finger due to the varying
tissue characteristics. The calibration database may thus provide
different correlations between the R values and concentration
levels of a substance depending on the underlying skin tissue
characteristics. The concentration level of the substance in blood
flow is then obtained using the calibration table at 722. The
concentration level may be expressed as mmol/liter, as a saturation
level percentage, as a relative level on a scale, etc.
Embodiment--Determination of Artery Width to Obtain Concentration
Levels of a Substance
[0167] To obtain an estimated amount of NO in blood flow NO levels
(mg/dl or mmol/l) from a density measurement of concentration
level, the volume of blood needs to be determined. The volume of
blood may be estimated from a diameter of the vessels and blood
flow rate over a period of time. The diameter of the blood vessels
may be estimated using various parameters obtained from PPG
signals, as described in further detail herein.
Embodiment--Determination of Concentration Levels of a Substance
Using Shifts in Absorbance Peaks
[0168] In another embodiment, a concentration level of a substance
may be obtained from measuring a characteristic shift in an
absorbance peak of hemoglobin. For example, the absorbance peak for
methemoglobin shifts from around 433 nm to 406 nm in the presence
of NO. The advantage of the measurement of NO by monitoring
methemoglobin production includes the wide availability of
spectrophotometers, avoidance of sample acidification, and the
relative stability of methemoglobin. Furthermore, as the reduced
hemoglobin is present from the beginning of an experiment, NO
synthesis can be measured relatively continuously (determining an
NO level every 1-2 seconds or every 1-30 minutes over a
predetermined time period), removing the uncertainty as to when to
sample for NO.
[0169] The biosensor 100 may detect nitric oxide in vivo using PPG
techniques by measuring the shift in the absorbance spectra curve
of reduced hemoglobin in tissue and/or arterial blood flow. The
absorbance spectra curve shifts with a peak from around 430 nm to a
peak around 411 nm depending on the production of methemoglobin.
The greater the degree of the shift of the peak of the curve, the
higher the production of methemoglobin and NO concentration level.
Correlations may be determined between the degree of the measured
shift in the absorbance spectra curve of reduced hemoglobin to a
concentration level of NO. The correlations may be determined from
a large sample population or for a particular user and stored in a
calibration database. The biosensor 100 may thus obtain an NO
concentration level by measuring the shift of the absorbance
spectra curve of reduced hemoglobin. A similar method of
determining shifts in absorbance spectra may be implemented to
determine a blood concentration level of other substances.
[0170] The biosensor 100 may obtain an NO concentration level by
measuring the shift of the absorbance spectra curve of deoxygenated
hemoglobin and/or by measuring the shift of the absorbance spectra
curve of oxygenated hemoglobin in vivo. The biosensor 100 may then
access a calibration database that correlates the measured shift in
the absorbance spectra curve of deoxygenated hemoglobin to an NO
concentration level. Similarly, the biosensor may access a
calibration database that correlates the measured shift in the
absorbance spectra curve of oxygenated hemoglobin to an NO
concentration level.
[0171] FIG. 8 illustrates a logical flow diagram of an exemplary
embodiment of a method 800 for measuring a concentration level of a
substance in vivo using shifts in absorbance spectra. The biosensor
100 may obtain a concentration of the substance by measuring shifts
in absorbance spectra of one or more substances that interact with
the substance. For example, the one or more substances may include
oxygenated and deoxygenated hemoglobin (HB). The PPG circuit 110
detects PPG signals at a plurality of wavelengths with a high
absorption coefficient of the one or more substances that interact
with the substance at 802. The biosensor 100 determines the
relative shift in the absorbance spectra for the substance at 804.
For example, the biosensor 100 may measure the absorbance spectra
curve of deoxygenated HB and determine its relative shift or peak
between the range of approximately 430 nm and 405 nm. In another
example, the biosensor 100 may measure the absorbance spectra curve
of oxygenated HB and determine its relative shift or peak between
421 nm and 393 nm.
[0172] The biosensor 100 accesses a calibration database that
correlates the relative shift in the absorbance spectra of the
substance with a concentration level of the substance at 806. The
biosensor 100 may thus obtain a concentration level of the
substance in blood flow using a calibration database and the
measured relative shift in absorbance spectra at 808.
[0173] The biosensor 100 may be configured for measurement on a
fingertip or palm, wrist, an arm, forehead, chest, abdominal area,
ear lobe, or other area of the skin or body or living tissue. The
characteristics of underlying tissue vary depending on the area of
the body, e.g. the underlying tissue of an abdominal area has
different characteristics than the underlying tissue at a wrist.
The operation of the biosensor 100 may need to be adjusted in
response to its positioning due to such varying characteristics of
the underlying tissue. The PPG circuit 110 may adjust a power of
the LEDs or a frequency or wavelength of the LEDs based on the
underlying tissue. The biosensor 100 may adjust processing of the
data. For example, an absorption coefficient may be adjusted when
determining a concentration level of a substance based on
Beer-Lambert principles due to the characteristics of the
underlying tissue.
[0174] In addition, the calibrations utilized by the biosensor 100
may vary depending on the positioning of the biosensor. For
example, the calibration database may include different table or
other correlations between R values and concentration level of a
substance depending on position of the biosensor. Due to the
different density of tissue and vessels, the R value obtained from
measurements over an abdominal area may be different than
measurements over a wrist or forehead or fingertip. The calibration
database may thus include different correlations of the R value and
concentration level depending on the underlying tissue. Other
adjustments may also be implemented in the biosensor 100 depending
on predetermined or measured characteristics of the underlying
tissue of the body part.
Embodiment--Respiration Rate, Heart Rate and Pulse Pressure
[0175] FIG. 9A illustrates a schematic drawing of an exemplary
embodiment of a PPG Signal 900 obtained using an embodiment of the
biosensor 100 from a user. The PPG Signal 900 was obtained at a
wavelength of around 395 nm and is illustrated for a time period of
about 40 seconds. The PPG Signal 900 was filtered using digital
signal processing techniques to eliminate noise and background
interference to obtain the filtered PPG Signal 900. A first
respiration cycle 902 and a second respiration cycle 904 may be
obtained by measuring a low frequency component or fluctuation of
the filtered PPG Signal 900. From this low frequency component, the
biosensor 100 may obtain a respiratory rate of a user from the PPG
Signal 900.
[0176] A heart rate may be determined from the spectral response.
For example, the biosensor 100 may determine the time between
diastolic points or between systolic points to determine a time
period of a cardiac cycle 906. In another embodiment, to estimate
the heart rate, the frequency spectrum of the PPG signal is
obtained using a FFT algorithm over a predetermined period (hamming
window). The pulse rate is estimated as the frequency that
corresponds to the highest power in the estimated frequency
spectrum. The frequency spectrum may be averaged over a time
period, such as a 5-10 second window.
[0177] A pulse pressure 908 may be determined from the PPG signal
900. The pulse pressure 908 corresponds to an amplitude of the PPG
signal 900 or a peak to peak value. The amplitude of the PPG signal
900 may be averaged over a time period to determine a pulse
pressure 908.
[0178] Thus, a PPG signal may be used to determine heart rate,
respiration rate and pulse rate. A light source in the UV range
provides a PPG signal with a lower signal to noise ratio for
determining heart rate and respiration rate in some tissue while a
light source in the IR range provides a PPG signal with a lower
signal to noise ratio in other types of tissue. The infrared range
(IR) range may include wavelengths from 650 nm to 1350 nm.
[0179] FIG. 9B illustrates a schematic drawing of an exemplary
embodiment of a pressure pulse waveform 910. After artifacts have
been removed and the PPG waveform is amplified through signal
processing, advanced algorithms may be used to extract and
interpret its features. A peak-peak interval, or the distance
between two consecutive systolic peaks, may represent a complete
heart cycle, for example. In the article, "On the Analysis of
Fingertip Photoplethysmogram Signals," by Mohamed Elgendi, Current
Cardiology Reviews, Volume 8, pages 14-25 (2012), which is hereby
incorporated by reference herein, the different characteristic
features of the PPG waveform are discussed. For example, a typical
PPG waveform 910 includes a systolic peak (SP) 912, a diastolic
peak (DP) 916, a dicrotic notch (914), trough 918 and pulse width
(tnext trough). Other characteristics include pulse pressure area
(PP), systolic area (As), diastolic area (Ad), augmented pressure
(AP), pulse interval, peak to peak interval, augmentation index
(AI=PP/(PP-AP).times.100%), crest time, etc. These or other
characteristics may be determined from a PPG waveform or a first or
second derivative of the PPG waveform. For example, various ratios
may be derived from a second derivate of the PPG waveform, e.g.,
such as the early systolic negative wave to the early systolic
positive wave (Ratio b/a). These and other characteristics may be
measured in a PPG waveform (including its derivatives).
[0180] FIG. 10 illustrates a schematic drawing of an exemplary
embodiment of results of R values 1000 determined using a plurality
of methods. The R values 1000 corresponding to the wavelengths of
395 nm/940 nm is determined using three methods. The R Peak Valley
curve 1002 is determined using the Ratio
R = L .times. 3 .times. 9 .times. 5 L .times. 9 .times. 4 .times. 0
##EQU00006##
as described hereinabove. The R FFT curve 1004 is obtained using
FFT techniques to determine the I.sub.DC values and I.sub.AC
component values of the spectral responses to determine the
Ratio
R = L .times. 3 .times. 9 .times. 5 L .times. 9 .times. 4 .times. 0
. ##EQU00007##
The R differential absorption curve 1006 is determined using the
shift in absorbance spectra as described in more detail in U.S.
Utility application Ser. No. 15/275,388 entitled, "SYSTEM AND
METHOD FOR HEALTH MONITORING USING A NON-INVASIVE, MULTI-BAND
BIOSENSOR," filed Sep. 24, 2016, now U.S. Pat. No. 9,642,578 issued
May 9, 2017, and hereby expressly incorporated by reference herein.
The various methods thus include one or more of: Peak & Valley
(e.g., peak detection), FFT, and differential absorption. Each of
the methods require different amounts of computational time which
affects overall embedded computing time for each signal, and
therefore can be optimized and selectively validated with empirical
data through large clinical sample studies. The biosensor 100 may
use a plurality of these methods to determine a plurality of values
for the concentration level of the substance. The biosensor 100 may
determine a final concentration value using the plurality of
values.
[0181] As seen in FIG. 10, the determination of the R values using
the three methods provides similar results, especially when
averaged over a period of time. A mean or average of the R values
1002, 1004 and 1006 may be calculated to obtain a final R value or
one of the methods may be preferred depending on the positioning of
the biosensor or underlying tissue characteristics.
[0182] FIG. 11A illustrates a schematic drawing of an exemplary
embodiment of an empirical calibration curve 1100 for correlating
oxygen saturation levels (SpO.sub.2) with R values. The calibration
curve 1100 may be included as part of the calibration database for
the biosensor 100. For example, the R values may be obtained for
L.sub.660nm/L.sub.940nm. In one embodiment, the biosensor 100 may
use a light source in the 660 nm wavelength or in a range of +/-50
nm to determine SpO.sub.2 levels, e.g. rather than a light source
in the IR wavelength range. The 660 nm wavelength has been
determined in unexpected results to have good results in measuring
oxygenated hemoglobin, especially in skin tissue with fatty
deposits, such as around the abdominal area.
[0183] FIG. 11B illustrates a schematic drawing of an exemplary
embodiment of an empirical calibration curve 1102 for correlating
NO levels (mg/dl) with R values. The calibration curve 1102 may be
included as part of the calibration database for the biosensor 100.
For example, the R values may be obtained in clinical trials from
measurements of L.sub.395nm/L.sub.940nm and the NO levels of a
general sample population. The NO levels may be measured using one
or more other techniques for verification to generate such a
calibration curve 1102. This embodiment of the calibration curve
1102 is based on limited clinical data and is for example only.
Additional or alternative calibration curves 1212 may also be
derived from measurements of a general population of users at one
or more different positions of the biosensor 100. For example, a
first calibration curve may be obtained at a forehead, another for
an abdominal area, another for a fingertip, another for a palm,
etc.
[0184] From the clinical trials, the L values obtained at
wavelengths around 390 nm (e.g. 380-410) are measuring nitric level
(NO) levels in the arterial blood flow. The R value for L390/L940
nm may thus be used to obtain NO levels in the pulsating blood
flow. From the clinical trials, it seems that the NO levels are
reflected in the R values obtained from L390 nm/L940 nm and
wavelengths around 390 nm such as L395 nm/L940 nm. The NO levels
may thus be obtained from the R values and a calibration database
that correlates the R value with known concentration levels of
NO.
[0185] In other embodiments, rather than L.lamda.1=390 nm, the L
value may be measured at wavelengths in a range from 410 nm to 380
nm, e.g., as seen in the graphs wherein L.lamda.1=395 nm is used to
obtain a concentration level of NO. In addition, L.lamda.2 may be
obtained at any wavelength at approximately 660 nm or above. Thus,
R obtained at approximately L.lamda.1=380 nm-400 nm and
L.lamda.2.gtoreq.660 nm may also be obtained to determine
concentration levels of NO.
[0186] FIG. 12 illustrates a schematic block diagram of an
embodiment of a calibration database 1200. The calibration database
1200 includes one or more calibration tables 1202, calibration
curves 1204 or calibration functions 1206 for correlating obtained
values to concentration levels of one or more substances A-N. The
concentration level of the substances may be expressed in the
calibration tables 1202 as units of mmol/liter, as a saturation
level or percentage, as a relative level on a scale (e.g., 0-10),
etc. For example, the concentration level of NO may be determined
and expressed as (mg/dl) as a saturation level or percentage (SpNO
%) or a relative level on a scale (e.g., 0-10).
[0187] The calibration database 1200 may also include one or more
calibration tables for one or more underlying skin tissue types. In
one aspect, the calibration database 1200 may correlate an R value
to a concentration level of a substance for a plurality of
underlying skin tissue types.
[0188] In another aspect, a set of calibration tables 1202 may
correlate an absorption spectra shift to a concentration level of
one or more substances A-N. For example, a first table may
correlate a degree of absorption spectra shift of oxygenated
hemoglobin to NO concentration levels. The degree of shift may be
for the peak of the absorbance spectra curve of oxygenated
hemoglobin from around 421 nm. In another example, the set of table
1202 may correlate a degree of absorption spectra shift of
deoxygenated hemoglobin to NO concentration levels. The degree of
shift may be for the peak of the absorbance spectra curve of
deoxygenated hemoglobin from around 430 nm.
[0189] The calibration database 1200 may also include a set of
calibration curves 1204 for a plurality of substances A-N. The
calibration curves may correlate L values or R values or degree of
shifts of spectral data to concentration levels of the substances
A-N.
[0190] The calibration database 1200 may also include calibration
functions 1206. The calibration functions 1206 may be derived
(e.g., using regressive functions) from the correlation data from
the calibration curves 1204 or the calibration tables 1202. The
calibration functions 1206 may correlate L values or R values or
degree of shifts in spectral data to concentration levels of the
substances A-N for one or more underlying skin tissue types.
Embodiment--Neural Network
[0191] One or more types of artificial neural networks (a.k.a.
machine learning algorithms) may be implemented herein to determine
health data from PPG signals. For example, neural networks may be
used to obtain a concentration level of NO or glucose or other
health data from input data derived from PPG signals. Neural
network models can be viewed as simple mathematical models defining
a function f wherein f:X.fwdarw.Y or a distribution over X or both
X and Y. Types of neural network engines or APIs currently
available include, e.g. TensorFlow.TM., Keras.TM., Microsoft.RTM.
CNTK.TM., Caffe.TM., Theano.TM. and Lasagne.TM..
[0192] Sometimes the various machine learning techniques are
intimately associated with a particular learning rule. The function
f may be a definition of a class of functions (where members of the
class are obtained by varying parameters, connection weights,
thresholds, etc.). The neural network learns by adjusting its
parameters, weights and thresholds iteratively to yield desired
output. The training is performed using defined set of rules also
known as the learning algorithm. Machine learning techniques
include ridge linear regression, a multilayer perceptron neural
network, support vector machines and random forests. For example, a
gradient descent training algorithm is used in case of supervised
training model. In case, the actual output is different from target
output, the difference or error is determined. The gradient descent
algorithm changes the weights of the network in such a manner to
minimize this error. Other learning algorithms include back
propagation, least mean square (LMS) algorithm, etc. A set of
examples or a training set is used for learning by the neural
network. The training set is used to identify the parameters [e.g.,
weights] of the network.
[0193] FIG. 13 illustrates a logical flow diagram of an embodiment
of a method 1300 for using a machine learning neural network
technique for detection of health data. In an embodiment, patient
data is obtained at 1302. The patient data may include one or more
of: age, weight, body mass index, temperature, blood pressure,
pre-existing medical conditions, trauma events, mental conditions,
injuries, demographic data, physical examinations, laboratory
tests, diagnosis, treatment procedures, prescriptions, radiology
examinations, historic pathology, medical history, surgeries, etc.
PPG signals at one or more wavelengths are obtained at 1304.
[0194] Various parameters of the PPG signals may be determined or
measured at 1306 These parameters include the diastolic and
systolic points, transfer functions, timing differences between
wavelengths, the L values, R values, pulse shape (measured by
autoregression coefficients and moving averages), characteristic
features of the shape of the PPG waveform, the average distance
between pulses, variance, instant energy information, energy
variance, etc. Other parameters may be extracted by representing
the PPG signal as a stochastic auto-regressive moving average
(ARMA). Parameters also may be extracted by modeling the energy of
the PPG signal using the Teager-Kaiser operator, calculating the
heart rate and cardiac synchrony of the PPG signal, and determining
the zero crossings of the PPG signal. These and other parameters
may be obtained using a PPG signal. The PPG input data may include
the PPG signals, and/or one or more parameters derived from the PPG
signals.
[0195] An input vector is obtained at 1308. The input vector
includes the PPG input data, such as the PPG signals at one or more
wavelengths and/or one or more parameters generated from the PPG
signals at the one or more wavelengths. Since the PPG signal is of
variable duration, a fixed dimension vector for a measurement of
the PPG signal may be obtained. The input vector may also include
patient data.
[0196] The input vector is processed by a processing device
executing a neural network (aka machine learning algorithm). The
processing device executes the machine learning algorithm or neural
network techniques using the input vector to determine health data
at 1310. The health data includes one or more of heart rate, period
of vasodilation, level of vasodilation, respiration rate, blood
pressure, oxygen saturation level, NO level, liver enzyme level,
Glucose level, Blood alcohol level, blood type, sepsis risk factor,
infection risk factor, cancer, virus detection, creatinine level or
electrolyte level. The health data may also include blood
viscosity, blood pressure, arterial stiffness, vascular health,
cardiovascular risk, atherosclerosis, etc. The health data may be
generated as an output fixed length vector.
[0197] The obtained health data may be compared to expected ranges
or thresholds in a calibration table at 1312. Alarms or warnings
may be issued based on the comparison.
Embodiment--Measurement of Vasodilation/Vasoconstriction Using PPG
Signals
[0198] Vasodilation and vasoconstriction are different from blood
volume changes due to the cardiac cycle. Vasodilation is the
widening of blood vessels due to relaxation of smooth muscle cells
within the vessel walls, in particular in the large veins, large
arteries, and smaller arterioles. For example, the release of the
Endothelium-derived relaxing factor (EDRF) causes the arteries to
expand in diameter and change elasticity. This process is the
opposite of vasoconstriction, which is the narrowing of blood
vessels due to constriction of the smooth muscle cells within the
vessel's walls. The vascular endothelium is crucially involved in
the fundamental regulation of blood flow matching demand and supply
of tissue. After transient ischemia, arterial inflow increases. As
a response to increased shear forces during reactive hyperemia,
healthy arteries dilate via release of NO or other
endothelium-derived vasoactive substances. This
endothelium-dependent flow-mediated vasodilation (FMD) is impaired
in atherosclerosis.
[0199] The capacity of blood vessels to respond to physical and
chemical stimuli in the lumen confers the ability to self-regulate
tone and to adjust blood flow and distribution in response to
changes in the local environment. Many blood vessels respond to an
increase in flow, or more precisely shear stress, by dilating. This
phenomenon is designated flow-mediated vasodilation (FMD). A
principal mediator of FMD is endothelium-derived NO--an example of
an EDRF.
[0200] Although the precise mechanism by which vasodilation occurs
during reactive hyperemia in FMD measurement has not been fully
elucidated, nitric oxide (NO) has been proposed as a principal
mediator of FMD. The NO, produced as a result of an increase of
endothelial NO synthase activity induced by shear stress, diffuses
into the tunica media, leading to relaxation of smooth muscle cells
and subsequent vasodilation. The assessment of endothelial function
by FMD, therefore, presupposes a normal structural condition.
Impaired endothelium-independent vasodilation is thought to be
associated with structural vascular alterations and alterations in
smooth muscle cells, e.g. as a result of atherosclerosis.
[0201] As the presence of endothelial dysfunction is closely
associated with cardiovascular risk and outcome, the measurement of
FMD in the brachial artery has become a standard method for the
assessment of endothelial function in patients and to evaluate
therapeutic interventions targeting atherosclerosis. For example,
in healthy humans, the relative increase in brachial artery
diameter during vasodilation is typically in the 5% to 10%
range.
[0202] Flow-mediated vasodilation measurements have been performed
in human studies and are of diagnostic and prognostic importance.
Prior techniques for measuring vasodilation require using
high-frequency ultrasound to visually inspect vessels, most
commonly the brachial artery. For example, one ultrasound technique
evaluates flow-mediated vasodilation (FMD), an
endothelium-dependent function, in the brachial artery. This
process includes applying a stimulus to provoke the endothelium to
release nitric oxide (NO) with subsequent vasodilation that is then
imaged using high resolution ultrasonography and quantitated as an
index of vasomotor function. This process of high-resolution
ultrasonography of the brachial artery to evaluate vasomotor
function has limitations. It must be performed in a clinical
setting by a medical clinician using expensive ultrasonography
equipment. Thus, there is a need for an improved system and method
for detection of vasodilation or vasoconstriction and vascular
health.
[0203] Thus, there is a need for an improved system and method for
detection of vasodilation/vasoconstriction and conditions affected
by vasodilation and conditions that affect vasodilation. The
systems and methods described herein for detection of levels of
vasodilation and periods of vasodilation may be used to determine
levels of vasoconstriction and periods of vasoconstriction.
[0204] In various embodiments described herein, vasodilation or
vasoconstriction and characteristics thereof may be measured using
PPG signals obtained by the biosensor. The effects of vasodilation
may be observed in PPG signals in one or more of a plurality of
wavelengths across different spectrums, such as IR, visible and UV.
For example, PPG signals across the spectrum may vary in shape,
intensity level and timing due to vasodilation/vasoconstriction. In
one example, the effect of vasodilation is observed from phase
differences between PPG signals of different wavelengths,
especially between wavelengths in different spectrums. Vasodilation
also causes subtle skin movement which may be observed in PPG
signals, especially in lower frequency components of PPG signals
(e.g. frequencies that do not reflect the pulsatile blow flow).
Using one or more characteristics of the PPG signals, a level of
vasodilation/vasoconstriction may be obtained. The level of
vasodilation/vasoconstriction may be measured as a relative change
in the size of vessels, such as percentage increase/decrease in a
baseline diameter or planar area, or in a range such as 1-10, or in
other manners.
[0205] In addition, an arterial stiffness or elasticity index may
be obtained using the PPG signals. The PPG signals may predict
vascular health, such as atherosclerosis. For example, a timing or
period to change from a state of vasodilation to normal width may
be obtained using phase differences between different wavelengths.
The rate of change may indicate vascular stiffness and a prediction
of vascular health.
[0206] In another embodiment, the level of vasodilation may be used
to calibrate measurement of oxygen saturation SpO.sub.2 or other
measurements of concentration of substances in blood flow. For
example, measurements of oxygen saturation levels may be in error
during periods of vasodilation. These measurements of oxygen
saturation during vasodilation may be identified and flagged and/or
the measurements may be calibrated in response to a level of
vasodilation.
[0207] The biosensor described herein obtains PPG signals and
measures a relative level of vasodilation of vessels and a period
of vasodilation. For example, the PPG signal measures the pressure
wave of blood flow through vessels. Vasodilation changes the
propagation properties of blood flow through vessels, and thus the
PPG signal changes. The changes in PPG signals due to the changing
propagation properties is reflected in a transfer function
generated from the PPG signals, e.g. time differences and wave
shape differences between PPG signals. The transfer function may be
measured to determine a level of vasodilation in real time.
[0208] FIG. 14 illustrates a schematic diagram of a graph 1400 of
PPG signals during a period of vasodilation in vessels. At "rest",
as the body processes food, insulin is dispensed, and nitric oxide
is released into the blood. The arteries expand due to the NO
causing the outer muscle of the arteries to expand temporarily.
This vasodilation is reflected in the PPG signal, and highly
visible in the signal to noise ratio.
[0209] The biosensor 100 obtained a PPG signal during vasodilation
after caloric intake for a wavelength at 940 nm, a wavelength at
880 nm and a wavelength at 660 nm as shown in the PPG Signals for
Wavelength Group 1402a. The biosensor 100 also obtained the
spectral response for a wavelength at 590 nm, a wavelength at 530
nm and a wavelength at 470 nm as shown in the PPG Signals for
Wavelength Group 1402b. The biosensor 100 further obtained the
spectral response for a wavelength at 405 nm, a wavelength at 400
nm and a wavelength at 395 nm as shown in the PPG Signals for
Wavelength Group 1402c.
[0210] As shown in the graphs, the PPG signals reflect a period of
vasodilation 1408. The vasodilation 1408 is reflected in the PPG
signals during a time period between approximately 16.11.04 secs
through approximately 16.11.17 secs. In particular, a lower
frequency component of the PPG signals changes during the period of
vasodilation 1408. This lower frequency component of the PPG
signals includes the lower frequencies not affected by the
pulsating blood flow (pressure wave) due to the cardiac cycle.
[0211] During vasodilation, the arteries and other vessels widen
changing the absorption properties of the vascular tissue. These
changes in absorption properties are due, e.g., by the increase in
blood in the vascular tissue and the compression of surrounding
tissue due to the widening vessels. The PPG signals across
wavelengths in the IR, visible and UV spectrums are affected by the
changing absorption properties of the vascular tissue due to
vasodilation.
[0212] The level of vasodilation 1410 may be obtained from the PPG
signals. For example, the amplitude changes in a low frequency
component from the PPG signal may be correlated to a level of
vasodilation. The level of vasodilation may be expressed as a
percentage change of the diameter or planar area of the vessel or
percentage increase in blood flow during the period of
vasodilation. The level of vasodilation may alternatively be
measured in a range such as 1-10, or in other manners. A level of
vasoconstriction may be similarly detected and measured.
[0213] The duration of the vasodilation may also be obtained from
the PPG signals. The beginning of vasodilation and end of
vasodilation may be identified from a change in amplitude,
especially of low frequency components, of the PPG signals. For
example, the vasodilation begins at approximately 16.11.04 secs and
ends at approximately 16.11.17 secs in Graph 1400 and so indicates
a period of vasodilation of 13 seconds.
[0214] FIG. 15 illustrates a schematic diagram of a series of
graphs illustrating the effects of vasodilation using the PPG
signals shown in FIG. 14. The first graph 1502 illustrates
R.sub.660/940 values that may be used to obtain a measurement of
oxygen saturation SpO.sub.2. The vasodilation period 1512, seen at
approximately 16.11.04 secs through approximately 16.11.17 secs,
affects the R values and thus the SpO.sub.2 measurements. Other
measurements based on R values or relative amplitudes of PPG
signals are also affected by vasodilation. In an embodiment, an
error value or calibration may be determined for measurements of
oxygen saturation SpO.sub.2 during a period of vasodilation. The
error value or calibration may depend on the level of vasodilation
or change in R values due to the vasodilation.
[0215] The second graph 1504 illustrates R values at R.sub.660/940,
R.sub.405/940 and R.sub.395/940. The vasodilation period 1512, seen
at approximately 16.11.04 secs through approximately 16.11.17 secs,
affects the R values, especially R values using PPG signals in the
UV or near UV range. The R values may be affected during the
vasodilation period since the ratio of the amplitude of different
wavelengths is used to obtain the R values. This may cause errors
in the measurement of blood component levels. The R values and/or
measurements of blood component levels may be compensated due to
the effect of vasodilation to correct errors during periods of
vasodilation.
[0216] For example, during the expansion of vessels during a
vasodilation period (e.g., due to NO or other EDRF), it may not be
practical to measure the SpO2 due to the error term present in the
940 & 660 nm PPG signals. This effect of vasodilation is likely
being observed by current SpO2 meters. Errors in the measurement of
SpO2 may be caused by undetected periods of vasodilation in current
SpO2 meters. Vasodilation may also cause errors in determinations
of other blood components using PPG signals. The measurement of the
respiratory cycle in a PPG signal is also affected during
vasodilation.
[0217] The duration of the vasodilation effect may depend on the
individual, the amount of the food ingested and the arterial
rigidity. For example, the vessels of diabetic subjects are likely
to expand less and have much less change in amplitude of PPG
signals during vasodilation/vasoconstriction due to inelasticity of
the arteries due to arterial rigidity and endothelial
dysfunction.
[0218] The graph 1506 illustrates the higher frequencies of the PPG
signal at 660 nm that may be used to determine whether the heart
rate remains relatively unaffected during the period of
vasodilation. The graph 1510 illustrates an elevated glucose level
during the vasodilation period of about 140-152 mg/dl.
[0219] The graph 1508 illustrates the lower frequencies of PPG
signals at 940 nm, 590 nm and 395 nm (e.g., the frequencies not
affected by pulsatile blood flow). The characteristics of the lower
frequencies of the PPG signals change during the vasodilation
period. The absorption properties of the vascular tissue vary due
to changes in volume of blood. In addition, the widening of the
vessels compresses the surrounding tissue. And the epidermis, the
upper layer of the skin, may expand in response to the widening
vessels during vasodilation. The PPG signals are thus affected by
this change in absorption properties of the tissue, as seen in
graph 1508.
[0220] The graph 1508 also illustrates that the PPG signals in
different spectrums exhibit a time or phase delay. For example, the
PPG signal at 940 nm in the IR range, the PPG signal at 590 nm in
the visible range, and the PPG signal at 395 nm in the UV range
have timing differences. This time delay is due in part to the
different penetration depths of the wavelengths. Preferably, to
determine this time delay, PPG signals in an infrared range (IR)
range from 650 nm to 1350 nm and PPG signals outside the IR range
are compared to determine the time or phase delay.
[0221] The pressure pulse wave propagates from deeper tissue to
shallower tissue, and thus a phase difference is generated between
the pressure pulse wave in the IR and UV signals. As the arteries
vasodilate and vasoconstrict, the resistance to the pressure pulse
wave changes and changes the propagation time from the deeper
tissue to the shallower tissue. This change in propagation time
also changes the phase difference between the pressure pulse wave
in the IR and UV signals. This phase difference provides a measure
of the effects of vasodilation and vasoconstriction. By comparing
changes in the phase difference between the UV & IR, the
effects of vasodilation and vasoconstriction may be measured.
[0222] At a same input power, light at higher wavelengths (IR
light) penetrates vascular tissue deeper than light at lower
wavelengths (UV light). The optical properties of the tissue are
affected by many factors, including but not limited to, skin-tone,
tissue hydration, and tissue chemistry. In a sensor configuration
where the light from the light source is backscattered to a sensor
on the same surface, the optical signal at the sensor includes a
sum of all light backscattered that makes it to the focal surface
after interacting with the tissue. With the optical power being the
same across all wavelengths, some of the light backscattered from
the IR light penetrates deeper into the tissue than the UV light
does. This means that the different wavelengths of light probe
different depths of tissue. Near the surface of the skin, the
density of arterial blood vessels is much higher (i.e. the amount
of arterial blood) than at the deeper tissue depths. This means
that while the IR light is affected by the arterial blood at the
shallower depths, the majority of the IR signal is reflected from
the deeper arterial blood.
[0223] When the heart beats, the arteries swell as fluid is pushed
out of the heart. The leading edge of the swelling or pressure wave
moves like a "bulge" through the arterial system. This system can
be thought of as an elastically dampened hydraulic system. The
pressure wave or bulge in the pulsatile blood flow moves from the
lower tissue to the upper tissue. Thus, the deeper penetrating
wavelengths (such as IR light) detect a pressure wave first
followed by the lesser penetrating wavelengths (such as visible
then UV light). The time delay in the "bulge" or pressure wave
moving from the lower tissue into the upper tissue thus creates a
time delay in a pressure waveform seen in the PPG signals at
different wavelengths. For example, as seen in FIG. 15, a waveform
in the UV range has a time delay compared to a waveform in the IR
range and a waveform in the visible range (390 nm to 700 nm). This
time delay in the different wavelengths is thus due to the depth of
penetration into the skin of each wavelength.
[0224] Vasodilation/vasoconstriction changes the propagation of the
pressure wave starting in the deeper, larger arteries and then
moving to the shallower, smaller ones. In addition, the UV light at
395 nm is absorbed by blood more than at 940 nm. Thus, less blood
is needed to obtain the same intensity to sample the PPG signal.
Because the deeper arteries are "closer" in the arterial structure
to the main arteries supplying blood to the tissue site, they are
less rigid than the arterioles that are closer to the surface of
the skin (where the majority of the UV signal is reflected). The
deeper arteries are more affected by vasodilation and
vasoconstriction.
[0225] In an embodiment described herein, this change in
propagation of the pressure wave can be measured in the change in
transfer function from a wavelength that penetrates the tissue
deeply (e.g. in the IR range) to a wavelength that penetrates
tissue much less deeply (e.g. in the visible or UV range). This
means that by measuring the change in shape and time delay of PPG
signals of two or more wavelengths with different penetration
depths (e.g., wherein at least one is in the near-IR window and one
is not), information about vasodilation/vasoconstriction may be
determined. Also, because the transfer function between the two
depths of penetration is affected by blood pressure, blood
viscosity, tissue absorption, and, in general, cardiovascular
health, these other parameters can be characterized as well.
Features or parameters of the PPG signal that can be examined
include, but are not limited to, the time delay between the
systolic points and diastolic points in different wavelengths and
the difference in dicrotic notch suppression between
wavelengths.
[0226] Vasoconstriction forces a greater volume of blood out of the
tissue site. This will lead to a decrease in absorption in the
field of view of the sensor because in general, blood absorbs more
light than tissue. There will be an increase in the intensity of
the reflected light detected at the biosensor because less light is
being absorbed (because there is less blood to absorb it). This
will lead to a sharp increase in the "DC" signal. Additionally,
because the surface area of the blood vessels is decreased, the
intensity of the pulsating signal due to the pressure pulse wave
(the AC signal) is decreased.
[0227] In tests, a patient consumed vasodilators and PPG signals
were measured. The PPG signals at different wavelengths exhibited a
decrease in time delay (phase difference), an increase in the DC
levels, and decreased in the AC levels. The research described
herein has thus confirmed that in general, the time delay (phase
difference) increases, the DC levels spike, and AC signals decrease
during vasoconstriction.
[0228] Vasodilation or vasoconstriction may also change the color
or hue of the skin tissue due to expansion or contraction of the
vessels. This increase or decrease of blood flow may change the hue
of the skin. By monitoring the hue of the skin, the biosensor 100
may detect vasodilation or other changes in blood circulation in
the tissue. For example, a PPG signal in a visible light range such
as at a yellow (590 nm-560 nm) or Red (564 nm-580 nm) or Blue (490
nm-450 nm) wavelength may be used to detect a change in hue of the
skin.
[0229] FIG. 16 illustrates a schematic diagram 1600 illustrating
phase differences and average low frequency levels during
vasoconstriction using the PPG signals of various wavelengths from
FIG. 14. The Graph 1602 illustrates the average phase difference
between a PPG signal at 940 nm and PPG signals of various
wavelengths during the period of vasodilation. The first time
difference equal to 0 is between 940 and itself. The last shown
time difference is between 395 nm and 940 nm. The phase difference
or the timing difference between PPG signals in graph 1602
illustrates a negative to positive timing which corresponds to the
constrictions and expansion of the arteries during
vasodilation/vasoconstriction. A change in the phase delay between
the PPG signals at different wavelengths is thus seen during a
period of vasoconstriction.
[0230] The second graph 1604 illustrates the average "DC values" in
PPG signals of various wavelengths during the period of
vasodilation. The "DC values" include DC components and/or low
frequency components not generally affected by the pulsatile blood
flow. The graph 1604 illustrates that the average DC values
I.sub.DC are above a baseline normal during the period of
vasoconstriction. The average DC values increase due to
vasoconstriction, changing tissue characteristics of contracting or
expanding muscles and is proportional to the force applied to the
muscle. So, the DC value (low frequencies not generally affected by
the pulsatile blood flow) can be used to determine periods and
levels of vasodilation/vasoconstriction.
[0231] The endothelium lines the walls of vessels and helps to
regulate vascular function. In the vasculature, insulin is released
in response to ingestion or hunger. The insulin activates two
distinct signaling path-ways in the endothelium that result in
secretion of nitric oxide (NO) and endothelin (ET-1),
respectively.
[0232] FIG. 17A illustrates a schematic block diagram of an
arterial wall under healthy conditions 1702. Smooth muscle cells
respond to NO as a vasodilator and endothelin (ET-1) as a
vasoconstrictor. ET-1 incites constriction in the smooth muscle
cells by binding to ET.sub.A and ET.sub.B receptors. In the
vasculature, the ET.sub.A receptor is mainly located on vascular
smooth muscle cells and mediates vasoconstriction. The ET.sub.B
receptor is primarily located on endothelial cells but may also be
present on vascular smooth muscle cells. Stimulation of the
endothelial ET.sub.B receptor results in release of NO and
prostacyclin which causes vasodilatation, whereas stimulation of
the vascular smooth muscle cell ET.sub.B receptor results in
vasoconstriction. Thus, the net effect produced by ET-1 is
determined on the receptor localization and the balance between
ET.sub.A and ET.sub.B receptors.
[0233] Endothelial cells also mediate rapid responses to neural
signals for blood vessel dilation, by releasing NO to make smooth
muscles relax in the vessel wall. Production of NO counteracts or
mediates the constricting effects of ET-1 in response to insulin in
vasculatures. Insulin stimulates NO production in endothelial cells
by subsequently activating the intracellular enzymes
1-phosphatidylinositol 3-kinase (PI3-ki-nase) and Akt, which
activates endothelial NO synthase. NO, stimulated by higher insulin
doses, is thought to be the underlying agent in insulin-mediated,
endothelium-dependent vasodilation. In healthy arteries, smaller
levels of ET-1 are produced in comparison to NO levels, and so the
bioavailability of NO is preserved.
[0234] FIG. 17B is a schematic block diagram of an arterial wall
with vascular dysfunction. In vascular dysfunction, there is an
increased expression of ET-1 in smooth muscle cells and
macrophages. There is also an increased expression of ET.sub.B
receptors on smooth muscle cells mediating vasoconstriction. In
addition, ET-1 may decrease endothelial NO synthase (eNOS)
expression, thereby reducing NO production. Both the ET.sub.A and
the ET.sub.B receptors on smooth muscle cells may mediate formation
of superoxide (O2) in endothelial dysfunction. Superoxide will
decrease the biological activity of NO by forming peroxynitrate
(ONOO--). This increases the effect of ET-1 and decreases the
effect of NO on smooth muscle cells. Clinical evidence in obesity
and diabetes suggest Endothelial dysfunction as a failure to
vasodilate adequately after application of an endothelium-dependent
vasodilator but also excess vasoconstrictor tone. Thus, ET-1
contributes to endothelial dysfunction both directly, through its
vasoconstrictor effects, and indirectly, through inhibitory effects
on NO production.
[0235] Collectively, the balance of these effects in endothelial
dysfunction is shifted towards more vasoconstriction, inflammation
and oxidative stress. This pathogenic role of the altered
expression and biological actions of ET-1 in vascular dysfunction
may lead to the development of cardiovascular disease,
atherosclerosis and hypertension. For example, dysfunction of the
vascular endothelium is an early finding in the development of
cardiovascular disease and is closely related to clinical events in
patients with atherosclerosis and hypertension.
[0236] As discussed above, in the vascular system, insulin
stimulates both ET-1 and NO activity. An imbalance between the
efficacy of these substances may be involved in the pathophysiology
of heart disease, hypertension and atherosclerosis. Thus, a device
and method to determine the balance of these substances in vivo
would be important in determining insulin-resistance and vascular
health.
[0237] FIG. 18 illustrates a schematic diagram of PPG signals
obtained during periods of insulin release events in vessels. At
"rest", a body responds to caloric intake by releasing insulin into
the blood stream. This insulin release stimulates ET-1 and NO
activity.
[0238] In the example of Graph 1800, the biosensor 100 obtained PPG
signals over a seven minute period between 28 mins and 35 mins
around a plurality of wavelengths at 940 nm, 630 nm, 590 nm, 530
nm, 465 nm and 395 nm. The PPG signals reflect "pulses" of insulin
in the blood flow in response to discrete release of insulin by the
pancreas in the bloodstream. The PPG signals reflect the insulin
release events at a first pulse 1804a around 29.15 mins, a second
pulse 1804b around 30.35 mins, a third pulse 1804c around 32.10
mins, and a fourth pulse 1804d around 33 mins. Vascular strain
occurs during release of localized insulin (insulin release events)
as part of the glucose regulation processes. This vascular strain
impairs the PPG signals temporarily during the interaction of the
ET-1 and NO agents released during the insulin release events.
[0239] Graph 1802 illustrates the PPG signals due to pulsatile
blood flow I.sub.AC. The I.sub.DC signal has been filtered from the
PPG signals in this example. The I.sub.AC signal reflects the ET-1
and NO response in the vessels due to the insulin release events at
a first pulse around 29.15 mins, a second pulse around 30.35 mins,
a third pulse around 32.10 mins, and a fourth pulse around 33 mins.
The smooth muscle cells of arterial walls tighten during chemical
reactions of each insulin pulse. This temporary stiffing of the
arterial structure causes a dampening effect on the PPG signals
during the insulin release event. The 630 nm & 940 nm optical
wavelengths are probing at deeper arterial/venous tissue structures
wherein the smooth muscle walls are thicker and exhibit a higher
stiffness factor under chemical induced strain such as an insulin
release. The blood flow of the outer tissues (microvacuoles)
include less smooth muscle tissue thickness and therefore respond
with a more pronounced PPG signal pulse at 395 nm, 465 nm, 530 nm
and 590 nm. Thus, the PPG pulses at these wavelengths are less
pronounced.
[0240] Due to the higher level of insulin release, the ET-1 and NO
response at the first pulse 1806a and the second pulse 1806b have a
greater constricting effect on the vessels. The vasoconstriction
decreases in the third and fourth pulses due to the decrease in
insulin release at ET-1 and NO responses 1806c and 1806d. In
addition, the NO levels may also have accumulated to further
mediate the effects of ET-1. Thus, the vasoconstriction is lessened
in response to the later insulin release events 1804c and
1804d.
[0241] The vasoconstriction in response to insulin release is thus
affected by the balance of ET-1 and NO as well as vascular disease
such as atherosclerosis. By measuring the relative vasoconstriction
or relative change in arterial diameter in response to insulin
release, vascular health may be assessed using the biosensor
100.
[0242] FIG. 19 illustrates a schematic diagram of graphs comparing
phase delay and pulse shape correlation in a plurality of PPG
signals during insulin release in vivo. As shown in Graph 1800, in
the example of Graph 1900, the biosensor 100 obtained PPG signals
over a seven minute period between 28 mins and 35 mins around a
plurality of wavelengths at 940 nm, 630 nm, 590 nm, 530 nm, 465 nm
and 395 nm. The PPG signals reflect "pulses" in response to
discrete release of insulin in the bloodstream. Graph 1902
illustrates the PPG signals due to pulsatile blood flow I.sub.AC
with low frequency signals I.sub.DC filtered therefrom.
[0243] In Graph 1904 and 1906, the R value 1908 of 395 nm/530 nm is
illustrated. In addition, a correlation is computed between the PPG
waveform at 940 nm and the PPG waveform at 395 nm to obtain a Pulse
Shape Correlation 1910 and a Phase Delay 1912. The PPG signals are
processed using, e.g., a cross correlation function or a Hilbert
transformation or another algorithm that provides a comparison of
pulse shape and temporal relationship between PPG signals. For
example, the time delay between the two PPG signals at 395 nm and
940 nm can also be calculated at each time instant from the phase
shift of their wavelet transforms.
[0244] The Pulse Shape Correlation 1910 and Phase Delay 1912
include effects of outer and inner tissue layers of vessels on the
PPG signal. When the muscle cells constrict during
vasoconstriction, the optical properties are altered. In addition,
the change in NO level affects the PPG signal around 395 nm.
[0245] In healthy persons, arterial walls are more flexible and
thus have a greater relative change in diameter in response to
insulin. The Pulse Shape Correlation 1910 and Phase Delay 1912
signals reflect a greater change in signal levels in response to
insulin. The R value pulses are correspondingly more pronounced.
The phase timing is inversely proportional to the arterial
diameters.
[0246] In patients having endothelium dysfunction, the arteries
exhibit stiffness with a decreased relative change in diameter.
Endothelium dysfunction may be found in patients with diseases such
as atherosclerosis, hypertension and diabetes. The Pulse Shape
Correlation 1910 and Phase Delay 1912 respond with a decreased
relative amplitude change during an insulin release event. The
Pulse Shape Correlation 1910 and Phase Delay 1912 may thus be used
to determine arterial stiffness and vascular health.
[0247] In an embodiment, the phase delay 1912, pulse shape
correlation 1910 and R value 1908 may also be used to determine
whether ET-1 or NO is more dominant in response to insulin. For
example, the average or mean range of one or more of these
measurements in a healthy population is measured. Then, an
individual measurement is compared to the average or mean range of
one or more of phase delay 1912, pulse shape correlation 1910 and R
value 1908. The comparison may be used to obtain whether an
imbalance is present between the effects of ET-1 and NO. An
imbalance in the effects of the two substances has an increased
vasoconstrictor effect on vessels due to an increase in ET-1
activity.
[0248] In addition, this change in propagation of the pressure wave
can be measured in the change in transfer function from a
wavelength that penetrates the tissue deeply (e.g. in the IR range)
to a wavelength that penetrates tissue much less deeply (e.g. in
the visible or UV range). This means that by measuring the change
in pulse shape and phase delay of the PPG signals at two or more
wavelengths with different penetration depths (e.g., wherein at
least one is in the near-IR window and one is not), information
about a level of vasoconstriction/vasodilation may be
determined.
Embodiment--Biosensor Detection of Vascular Health During an
Insulin Release Event
[0249] FIG. 20 illustrates a schematic block diagram of an insulin
response of a young healthy male and a middle-aged male. The Graph
2000 illustrates an R value 2004 of 395 nm/530 nm during an insulin
response in a middle-aged male patient obtained using the biosensor
100. In the ET-1 and NO response 2010, the R values 2004 shows a
subdued response due to an increased arterial stiffness and/or ET-1
prominence. The ET-1/NO response 2010 is more typical of
vasoconstriction.
[0250] The Graph 2002 illustrates an R value 2006 of 395 nm/530 nm
during an insulin response in a young male patient. In the ET-1 and
NO response 2012, the R values 2006 have a relatively greater range
due to a healthy vascular system. The ET-1/NO response 2012 is more
typical of vasodilation. Thus, by comparing the R value data of
healthy persons in a general population with an individual's
measurement, the presence of an increased arterial stiffness and/or
ET-1 prominence may be determined.
[0251] FIG. 21 illustrates a schematic diagram of graphs comparing
phase offset and pulse shape waveform in a plurality of PPG signals
during insulin release in an adolescent male. In the example of
Graph 2100, the biosensor 100 obtained PPG signals over an
approximately three minute period around a plurality of wavelengths
at 940 nm, 630 nm, 590 nm, 530 nm, 465 nm and 395 nm. The PPG
signals reflect a discrete insulin release event 2108 in the
bloodstream. The insulin release 2108 includes a marked PPG pulse
in a first wavelength having a high absorption coefficient for NO,
(e.g. 395 nm) wherein the amplitude of the pulse is at least
greater than twice expected from a heart rate pulse.
[0252] Graph 2102 illustrates the PPG signals due to pulsatile
blood flow I.sub.AC. The I.sub.AC signal reflects an ET-1 and NO
response 2110. The I.sub.AC signal has at least a 50% decrease in
amplitude during the insulin release event 2108. One reason for the
decrease in amplitude includes the constriction of the smooth
muscle cells in vessels that affects the absorption properties of
the tissue.
[0253] In Graph 2104 and 2106, the R value 2112 of 395 nm/530 nm is
illustrated. In addition, a correlation between the PPG waveform at
940 nm and the PPG waveform at 395 nm is also illustrated. The
correlation includes a Phase Delay 2114 and a Pulse Shape
Correlation 2116. The PPG signals are processed using a cross
correlation function or a Hilbert transformation or another
algorithm that determines similarities in pulse shape and temporal
relationship between the PPG signals. For example, the time delay
between the two signals can also be calculated at each time instant
from the phase shift of their wavelet transforms.
[0254] The Phase Delay 2114 and a Pulse Shape Correlation 2116
includes effects of outer and inner tissue layers of vessels on the
PPG signal, e.g. muscle cells during vasoconstriction. The Phase
Delay 2114 and a Pulse Shape Correlation 2116 may be mapped to a
vessel diameter or level of vasoconstriction/vasodilation.
[0255] FIG. 22 illustrates a schematic diagram of an insulin
response in the adolescent male in greater detail. Graph 2200
illustrates the PPG signals due to pulsatile blood flow I.sub.AC
during the period of insulin release 2108. The ET-1 and NO response
2110 creates a chemical stress in the endothelium from competing
forces of arterial dilation and constriction. With a healthy ET-1
and NO efficacy, the endothelium should exhibit arterial stiffening
and/or vasoconstriction. The constricting shallow muscle cells
affect the optical properties of the PPG signals during this
interval. Thus, the period of vasoconstriction 2204 may include one
or more of increased arterial stiffness and/or vasoconstriction due
to the effects of ET-1 and NO activity.
[0256] The period of vasoconstriction 2204 may be determined based
on the amplitude changes of the PPG signals. At the beginning of
the period of vasoconstriction 2204, the amplitudes of the I.sub.AC
signal begin to decrease and then to slowly increase until the
amplitudes of the I.sub.AC signal return to average at the end of
the period of vasoconstriction 2204. The period of vasoconstriction
2304 is approximately between 21 sec and 31 sec. in this
example.
[0257] A level of vasoconstriction 2202 may be determined, e.g.,
from an average peak to peak amplitude of the PPG signals prior to
or after the period of vasoconstriction and the lowest peak to peak
amplitude of the PPG signals during the period of vasoconstriction.
The level of vasoconstriction may be measured in other manners,
such as average peak value to lowest peak value during the period
of vasoconstriction.
[0258] FIG. 23 illustrates a schematic diagram of an insulin
response in the adolescent. Graph 2300 illustrates the PPG signals
during the insulin release event 2108 in greater detail. The Graph
2300 illustrates that the insulin release generates a constricting
response in the vessels over an approximately 10 second interval
during the period of vasoconstriction 2204. The period of
vasoconstriction 2204 may include one or more of increased arterial
stiffness and vasoconstriction due to the effects of the ET-1 and
NO response 2110.
[0259] FIG. 24 illustrates a schematic diagram of graphs comparing
phase offset and pulse shape waveform in a plurality of PPG signals
during an insulin release event in a middle-aged male. In the
example of Graph 2400, the biosensor 100 obtained PPG signals over
a 2:49 minute period around a plurality of wavelengths at 940 nm,
630 nm, 590 nm, 530 nm, 465 nm and 395 nm. The PPG signals reflect
a pulse in response to a discrete insulin release 2408 in the
bloodstream. The insulin release 2108 includes a marked PPG pulse
in a first wavelength having a high absorption coefficient for NO,
wherein the amplitude of the pulse is at least greater than twice
expected from a heart rate pulse.
[0260] Graph 2402 illustrates the PPG signals due to pulsatile
blood flow I.sub.AC. The I.sub.AC signal reflects an ET-1 and NO
response 2410 in the vessels due to the insulin release 2408. The
I.sub.AC signal has at least a 50% decrease in amplitude during the
insulin release event 2408.
[0261] In Graph 2404 and 2406, the R value 2412 of 395 nm/530 nm is
illustrated. In addition, a correlation between the PPG waveform at
940 nm and the PPG waveform at 395 nm is illustrated as Phase Delay
2414 and Pulse Shape Correlation 2416. The PPG signals are
processed using a cross correlation function or a Hilbert
transformation or another algorithm that determines similarities in
pulse shape and temporal relationship between PPG signals. For
example, the time delay between the two signals can also be
calculated at each time instant from the phase shift of their
wavelet transforms.
[0262] The Phase Delay 2414 and a Pulse Shape Correlation 2416
includes effects of outer and inner tissue layers of vessels on the
PPG signal, e.g. muscle cells during vasoconstriction. The Phase
Delay 2414 and a Pulse Shape Correlation 2416 may be mapped to a
vessel diameter or level of vasoconstriction/vasodilation.
[0263] FIG. 25 illustrates a schematic diagram of an insulin
response in a middle aged male in greater detail. From FIG. 24,
graph 2402 illustrates the PPG signals due to pulsatile blood flow
I.sub.AC during the period of insulin release 2408. The Graph 2500
illustrates that the ET-1 and NO response 2410 from graph 2402 in
greater detail. Graph 2500 reflects the decrease in amplitude of
the I.sub.AC signal due to vasoconstriction. When smooth muscles
cells tighten causing vasoconstriction, the I.sub.AC signal
amplitude decreases in magnitude. The constricting shallow muscle
cells affect the optical properties of the PPG signals during this
interval.
[0264] The period of vasoconstriction in this example is from about
20 seconds to at least 28 seconds, e.g. the period of
vasoconstriction 2504. The period of vasoconstriction 2504 may
include one or more of increased arterial stiffness and
vasoconstriction due to the effects of the ET-1 and NO response. A
level of vasoconstriction 2502 may be determined, e.g., from an
average peak to peak amplitude of the PPG signals prior to or after
the period of vasoconstriction and the lowest peak to peak
amplitude of the PPG signals during the period of vasoconstriction.
The level of vasoconstriction 2502 may be measured in other
manners, such as average peak value to lowest peak value during the
period of vasoconstriction.
[0265] FIG. 26 illustrates a schematic diagram of an insulin
response in a middle aged male in greater detail. Graph 2600
illustrates the PPG signals during the period of insulin release
2408. The Graph 2600 illustrates that the insulin release generates
a constricting response in the vessels during the period of
vasoconstriction. The constricting shallow muscle cells affect the
optical properties of the PPG signals during this interval.
[0266] Comparing the PPG signals detected during the insulin
release between the adolescent male and the middle aged male, the
PPG signals indicate that the vasoconstriction is relatively less
in the middle aged male. The decrease in vasoconstriction is
expected due to age related arterial stiffness and
arteriosclerosis. This age-related difference in vasoconstriction
can be due to decreased elastic production from fibrinogen,
associated with ageing, or hypertension or pathological conditions
such as atherosclerosis. The smooth muscle cells of the adolescent
male may also be stronger, and the elastic lamina that surrounds
the lumen of the artery may be more resilient and flexible at that
age. This demonstrates that a level of vasoconstriction may be
determined from the PPG signals and compared to healthy values
(such as in the adolescent male) to determine vascular health.
[0267] FIG. 27 illustrates a schematic flow diagram of an
embodiment of a method 2700 for determining vascular health using
the biosensor 100. The biosensor 100 detects PPG signals at a
plurality of wavelengths reflected from skin tissue at 2702.
Preferably, the first wavelength has a high absorption coefficient
for NO and is approximately 395 nm or in a range from 380 to 410
and a lower depth of penetration into the tissue. The second
wavelength has a lower absorption coefficient for NO and is
approximately in a range from 510 nm to 550 nm or is in an IR range
such as 940 nm and has a greater depth of penetration into the
tissue.
[0268] The PPG signals are measured over a period of time that
preferably includes one or more insulin release events, such as
after ingestion, wherein insulin is released into the blood stream.
The insulin release is reflected by a marked PPG pulse in the first
wavelength having a high absorption coefficient for NO. The pulse
has a 5-10 second duration, wherein the amplitude of the pulse is
at least greater than twice expected from a heart rate pulse. The
pulses due to insulin release also have a much lower frequency than
a heart rate. The insulin release event may thus be identified in
the PPG signals using one or more of these characteristics.
[0269] One or more parameters derived using the PPG signals during
the insulin release event is determined and compared at 2704. For
example, a cross correlation function may determine a phase offset
between the PPG signals and/or pulse shape correlations during the
insulin release event. The PPG signals may also be processed using
other cross correlation functions or a Hilbert transformation or
another algorithm that determine similarities in pulse shape and
temporal relationship between PPG signals.
[0270] A measurement of vascular health is obtained using the one
or more parameters at 2706. For example, a measurement of
vasoconstriction or vasodilation may be obtained, such as a vessel
diameter or percentage of change in diameter. The relative efficacy
of ET-1 and NO may be estimated based on the measurement of
diameter change and level of insulin release. A level of arterial
stiffness may be determined using the measurement of the diameter
change and level of insulin release and comparing to such
measurements in a general sampling of healthy persons without
vascular dysfunction.
[0271] FIG. 28 illustrates a schematic flow diagram of an
embodiment of a method 2800 for determining an efficacy balance of
ET-1 and NO in smooth muscle cells of vessels. The vasoconstriction
or vasodilation in response to insulin release is affected by the
balance of ET-1 and NO as well as vascular disease such as
atherosclerosis. By measuring the relative vasoconstriction or
change in arterial diameter or stiffness in response to insulin
release, the relative efficacy and balance of ET-1 and NO may be
assessed using the biosensor 100.
[0272] The phase offset and/or correlation of pulse shape of two or
more PPG signals is determined over the period of time including
the insulin release at 2802. For example, the first wavelength has
a high absorption coefficient for NO and a lower penetration depth
into tissue, and the second wavelength has a lower absorption
coefficient for NO and a higher penetration depth into tissue. A
cross correlation function may be used to determine the phase
offset and/or pulse shape correlations or a Hilbert transformation
or another algorithm that determine similarities in pulse shape and
temporal relationship between PPG signals.
[0273] An imbalance in the effects of the two substances has an
increased vasoconstrictor effect on vessels due to an increase in
ET-1 activity and suppression of NO efficacy. The change in
diameter of vessels during insulin release may be determined at
2804 and compared to a healthy individual of similar age with no
vascular dysfunction at 2804. Increased relative levels of
vasoconstriction may be indicative of increased ET-1 activity due
to an imbalance of ET-1 and NO efficacy caused by
insulin-resistance disease such as diabetes.
[0274] The phase delay may also provide an indication of the
balance of ET-1 and NO in response to insulin. For example, the R
value is compared to systolic peaks of the phase delay to determine
a relative level of vasoconstriction or change in diameter of
vessels. The phase offset between two or more of the PPG signals in
different spectrums, or having different depths of penetration of
tissue, is measured. The phase offset may be used to determine
presence of vasodilation/vasoconstriction in the tissue. For
example, in normal tissue, the PPG signals exhibit only a slight
difference in phase or timing when nominal vasodilation is
occurring in the tissue. When the PPG signals have a greater
difference in phase or timing, this indicates that blood flow in
the tissue near the surface is decreased, e.g. due to
vasoconstriction, due to low blood circulation level or an
imbalance of NO and ET-1 or arterial stiffness. When blood flow is
increased to the tissue, the PPG signals at UV and IR wavelengths
exhibit a lower variance in pulse shape and a higher correlation
value. This decrease in the difference in the pulse shape of the
PPG signals at the different wavelengths indicates an increase of
blood flow, e.g. due to vasodilation.
[0275] The phase offset and pulse shape correlation may be mapped
to a level of vasodilation/vasoconstriction, e.g. using a
calibration table or function. The level of
vasodilation/vasoconstriction and a period of
vasodilation/vasoconstriction may thus be determined using the
phase differences and pulse shape correlations between the PPG
signals at the different wavelengths. The above described
parameters of the PPG signals may also be used to determine a
period of vasoconstriction using similar methods.
[0276] In another aspect, R values are determined using the PPG
signals at least two wavelengths, such as R.sub.660nm/940nm or
R.sub.405/940 or R.sub.395nm/940nm. The level of vasodilation or
period of vasodilation may be determined using changes in amplitude
of one or more R values.
[0277] The level of vasoconstriction/vasodilation may be compared
to an insulin level to determine the balance of the effects of ET-1
and NO at 2808. For example, the level of
vasoconstriction/vasodilation for a known insulin level or during
an average insulin release event may be determined in individuals
with healthy vascular function. A calibration table or function may
store a mapping of a range of vasoconstriction/vasodilation and/or
an average period of vasoconstriction/vasodilation for one or more
levels of insulin release by testing a general population of
healthy individuals. The level of vasodilation may be represented
as a measurement of one or more of: a percentage of change in
arterial width, diameter or planar area or a change in blood flow
or volume, etc. These comparisons may thus indicate a balance of
efficacy between ET-1 and NO at 2808.
[0278] In addition, arterial stiffness may decrease a relative
level of vasodilation compared to an average or normal range. The
rate of change of the width of the artery at a beginning or end of
vasodilation may be used as an indicator of arterial stiffness. A
reduction in elasticity of arteries may decrease the rate of change
in the width of the artery and thus the rate of change in the level
of vasodilation. These comparisons of the rate of change of the
width of vessels may also be used to indicate a measurement of
arterial stiffness. These determinations may also factor in the
determination of whether the cause of reduced
vasoconstriction/vasodilation is due to an imbalance of ET-1 and NO
or due to arterial stiffness during an insulin release event.
Embodiment--Measurement of Insulin Levels
[0279] FIG. 29 illustrates a schematic flow diagram of an
embodiment of a method 2900 for determining an insulin level in
blood flow. The biosensor 100 monitors PPG signals at a plurality
of wavelengths reflected from skin tissue over a period of time,
such as 5 minutes to 24 hours. Preferably, a first wavelength has a
high absorption coefficient for NO and is approximately 395 nm or
in a range from 380 to 410. A second wavelength has a lower
absorption coefficient for NO and is approximately 530 nm or in a
range from 510 nm to 550 nm or is in an IR range such as 940
nm.
[0280] The PPG signals are analyzed to identify one or more insulin
release events at 2902. For example, after ingestion, insulin is
naturally released into the blood stream. The insulin release
effects a marked PPG pulse in the first wavelength having a high
absorption coefficient for NO. The PPG pulse, e.g., has a longer
duration than a PPG pulse of a heart rate. For example, the PPG
pulse during an insulin release event has an approximately 5-10
second duration, wherein the change in amplitude of the PPG pulse
is at least greater than twice expected from a heart rate pulse.
Signal analysis using pattern recognition may be employed with the
PPG signals to identify the insulin pulse.
[0281] An R value curve is obtained over the period of the insulin
release event, using PPG signals having the first and second
wavelength, such as an R value of 395 nm/530 nm or 395 nm/940 nm at
2904. The R value curve during the insulin release event is
analyzed to determine an insulin level at 2906. For example, an
area under the R value curve is determined during the insulin
release event. A calibration table or curve is tabulated that
associates the area to the insulin level. The calibration may be
performed on an individual using a blood test to determine insulin
levels during a calibration phase of the biosensor. Alternatively,
the calibration may be predetermined from testing of a general
population. Though an area under the R value curve is described for
the calibration, other parameters obtained from the pulse of the
PPG signals during the insulin release event may be used to
determine insulin levels, such as an average R value or I.sub.AC
value.
[0282] Insulin is usually secreted in discrete amounts one or more
times depending on the stage of digestion. Thus, multiple insulin
release events may be detected within a short time period after
ingestion. The insulin level may be determined for additional
insulin release events using the R value curve and calibration
table at 2908. The cumulative insulin released over a time period
may then be determined at 2910 by summation of the individual
insulin release events during the time period.
[0283] The stage of digestion may also be determined using
identification of the insulin release events from the PPG signals.
For example, the insulin release events are more frequent after
ingestion during stage 1 and stage 2 of digestion and are less
frequent when hungry. Correspondingly, the frequency of PPG pulses
due to insulin release events increases in response to different
stages of digestion. In contrast, the frequency of the PPG pulses
due to insulin release events decreases in response to fasting or
hunger. Thus, by measuring the frequency or time between insulin
release events using the PPG signals, a stage of digestion may be
identified or a level of fasting or hunger may be identified at
2912.
Embodiment--Measurement of Glucose Levels
[0284] As described herein, the biosensor may determine a glucose
level by averaging an R value over a short period of time (e.g.,
around 2-3 minutes) and using a calibration to obtain a glucose
level associated with the R value. This method has predictable
results for healthy persons with little to no vascular dysfunction.
However, for persons with certain diseases, e.g. affecting arterial
health, this method may not provide accurate results due to
unhealthy vasoconstriction of arterioles near the surface of the
skin or tissue. For example, diabetes creates extreme
vasoconstriction that affects the R value and results in inaccurate
correlations to NO and glucose levels.
[0285] FIG. 30 illustrates schematic diagrams of measurements of
glucose levels in a plurality of patients using the biosensor in a
clinical trial. In this example, the patients ingested a caloric
intake, and then a reference glucose was tested at discrete points
using a blood test. In addition, the biosensor 100 detected an R
value at 395 nm/940 nm at the discrete points. The patients in
graphs 3000, 3002 and 3004 had a seemingly healthy vascular
function and NO response. The R value approximately tracked the
trend in the reference glucose. Thus, the R value provides a
predictable tracking of trends in glucose, and a universal
calibration table or curve may be compiled to correlate R values
and glucose levels in these patients.
[0286] However, the patients in graphs 3006 and 3008 exhibited
vascular dysfunction. The R value diverged from the reference
glucose at one or more of the discrete points. For example, the
vasodilation effect during phase 2 of digestion created unexpected
results in the R values. Thus, in patients with atypical vascular
responses, individual calibration of glucose levels to R values may
need to be performed.
[0287] FIG. 31 illustrates schematic diagrams of measurements of
glucose levels in a plurality of patients using the biosensor 100
in a clinical trial. In this example, the reference glucose is
displayed with a predicted glucose value that is obtained using the
R.sub.395nm/940nm values shown in FIG. 30. The R values for
patients in graphs 3000, 3002 and 3004 with a seemingly healthy
vascular function and NO response were correlated to the predicted
glucose values using a universal calibration. The universal
calibration correlates R values and glucose values based on a
clinical testing from a general sample population of persons with
healthy vascular systems. The universal calibration may include a
table, equation, factor or curve. Thus, the R value provides a
predictable tracking of trends in glucose for patients with a
relatively healthy vascular response, and a universal calibration
may be compiled to correlate R values and glucose levels in these
patients.
[0288] However, the R values for patients in graphs 3106 and 3108
are correlated to the predicted glucose values using individual
calibrations. For example, the R value is obtained, and an interim
glucose value is estimated using the universal calibration. The
interim glucose value is then adjusted using an individual
calibration. A difference or other correlation between the interim
glucose value and the reference glucose is determined at one or
more points of time. The difference or other correlation is used as
an individual calibration to adjust the interim glucose value to
the predicted glucose levels shown in Graphs 3106 and 3108. Thus,
for patients with vascular dysfunction, an individual calibration
is used to obtain the predicted glucose levels from the R
values
[0289] In another embodiment, the individual calibration directly
correlates the R values to the predicted glucose level for patients
with vascular dysfunction. The reference glucose at one or more
discrete points is compared to the R.sub.395nm/940nm values at the
same discrete points, and the individual calibration is
obtained.
[0290] The individual calibration should be recalculated at least
every 2-3 months due to potential change in vascular function. For
example, arteriolosclerosis or insulin resistance may further
deteriorate the vascular health such that the vessels exhibit
increased vasoconstriction. This deterioration may affect the level
of vasoconstriction in vessels and the correlation between R values
and glucose levels.
[0291] FIG. 32 illustrates a schematic flow diagram of an
embodiment of a method 3200 for determining glucose levels of a
patient with atypical vascular function. The biosensor 100
determines that a user has vascular dysfunction or a disease that
typically leads to vascular dysfunction, such as diabetes, heart
disease or arteriolosclerosis, at 3202. The user may input or
request individual calibration. The PPG signals are obtained,
preferably at a first wavelength with a high absorption coefficient
for NO, such as 395 nm or in a range around 380 nm to 410 nm and
determining a measurement value using the PPG signals at 3204. The
measurement value may include, e.g., an R value at 395/940 or
395/530 wavelength ratios.
[0292] The biosensor 100 may then determine a level of glucose
using the measurement value and an individual calibration at 3206.
For example, the R value is obtained, and an interim glucose value
is estimated using the universal calibration. The interim glucose
value is then adjusted using an individual calibration. In another
embodiment, the individual calibration directly correlates the R
values to the predicted glucose level for patients with vascular
dysfunction. Thus, for patients with vascular dysfunction, an
individual calibration is used to obtain the predicted glucose
levels from the R values.
[0293] The individual calibration should be re-evaluated
periodically at 3208. For example, the individual calibration
should be updated at least every 2-3 months due to potential
changes in vascular function.
[0294] FIG. 33 illustrates a schematic flow diagram of another
embodiment of a method 3300 for determining glucose levels of a
patient with atypical vascular function. As shown in the example of
Graph 1900, the biosensor 100 obtains PPG signals over a time
period between around a plurality of wavelengths at 940 nm, 630 nm,
590 nm, 530 nm, 440 nm and 395 nm. The "pulses" in response to
discrete release of insulin in the bloodstream are identified in
the PPG signals. Then a correlation is computed between the PPG
waveform with a low absorption coefficient for NO (e.g., 440 nm,
530 nm or another wavelength in the visible range or in the IR
range) and the PPG waveform with a high absorption coefficient for
NO (e.g., at 395 nm or in a range of +/-10 nm of 395 nm) during the
period of release of insulin to obtain a Pulse Shape Correlation
and a Phase Delay at 3302. The PPG signals are processed using,
e.g., a cross correlation function or a Hilbert transformation or
another algorithm that determines similarities in pulse shape and
temporal relationship between the PPG signals.
[0295] The phase offset or waveform correlation may then be used to
determine a factor to "normalize" an R value to obtain a normalized
R value at 3306. Thus, the normalization factor may account for
increased vasoconstriction due to vascular dysfunction. For
example, the R value may be divided by an averaged phase offset
factor or an averaged pulse shape correlation to determine the
"normalized" R value. The normalized R value is then correlated to
a glucose level using a universal calibration table or curve at
3308. The normalization factor compensates the R value in patients
with vascular dysfunction.
[0296] In another embodiment, a plurality of calibrations may be
implemented, each assigned to one or more different normalization
factors. The glucose level is determined using the calibration
table associated with the determined normalization factor.
Embodiment--Identification of Deep Inhalation
[0297] FIG. 34 illustrates a schematic diagram of graphs of PPG
signals during deep inhalation. A rapid, deep inspiration is also
known to induce vasoconstriction of skin arterioles. In particular,
a deep inhalation may vastly reduce the amplitude of PPG pulse
waveforms and also introduce marked low-frequency components as a
consequence of vasoconstriction and subsequent vasodilatation.
These changes due to deep inspiration may create difficulties in
accurately identifying PPG waveform features, such as insulin
release periods. This also increases the error when computing
physiological measures.
[0298] Graph 3400 illustrates PPG signal obtained during a deep
inhalation 3402 around a plurality of wavelengths at 940 nm, 630
nm, 590 nm, 530 nm, 465 nm and 395 nm. The deep inhalation caused a
decrease in the PPG pulse amplitude along with a characteristic
low-frequency trend as seen in Graph 3404. Graph 3404 shows the
I.sub.AC signal due to pulsatile blood flow. Because of the
excessively low amplitude indicative of vasoconstriction 3406, the
deep inhalation may be mistaken for an insulin release event.
[0299] Graph 3408 illustrates the R value 3410 of 395 nm/530 nm is
illustrated. In addition, a correlation is computed between the PPG
waveform at 940 nm and the PPG waveform at 395 nm to obtain a Phase
Delay 3412. The PPG signals are processed using, e.g., a cross
correlation function or a Hilbert transformation or another
algorithm that determines similarities in pulse shape and temporal
relationship between PPG signals. For example, the time delay
between the two signals can also be calculated at each time instant
from the phase shift of their wavelet transforms.
[0300] The R value 3410 has a low amplitude indicative of
vasoconstriction 3406, such as in insulin release or deep
inhalation. However, the phase delay 3412 does not indicate an
insulin release. As seen in Graph 2404 in FIG. 24, the phase delay
2414 in response to an insulin release has a corresponding pulse
with a large amplitude change. The phase delay 3412 in response to
the deep inhalation 3402 fails to include such a pulse at a time
corresponding to the vasoconstriction 3406. Thus, the
vasoconstriction 3406 may be identified as an inhalation or other
vasoconstriction causing event and not due to insulin release. Such
pattern recognition may be performed to identify insulin release
events recorded by the PPG signals.
Embodiment--Detection of a Risk of Sepsis or Other Infection Based
on NO Levels
[0301] In an embodiment, the biosensor 100 may detect a risk of
sepsis using NO concentration levels. In this embodiment, an R
value derived from L.sub.395 and L.sub.940 is used to determine a
NO measurement though other parameters may be obtained, such as
R.sub.390/940 or L.sub.390. In the clinical trials herein, the
R.sub.395/940 value for a person without a sepsis condition was in
a range of 0.1-8. In addition, it was determined that the
R.sub.395/940 value of 30 or higher is indicative of a patient with
a sepsis condition and that the R.sub.395/940 value of 8-30 was
indicative of a risk of sepsis in the patient. In general, the
R.sub.395/940 value of 2-3 times a baseline of the R.sub.395/940
value was indicative of a risk of sepsis in the patient. These
ranges are based on preliminary clinical data and may vary. In
addition, a position of the biosensor, pre-existing conditions of a
patient or other factor may alter the numerical values of the
ranges of the R.sub.395/940 values described herein.
[0302] The R values are determined by using a wavelength in the UV
range with high absorption coefficient for NO, e.g. in a range of
380 nm-410 nm. These R values have a large dynamic range from 0.1
to 300 and above. The percentage variance of R values in these
measurements is from 0% to over 3,000%. The R values obtained by
the biosensor 100 are thus more sensitive and may provide an
earlier detection of septic conditions than blood tests for serum
lactate or measurements based on MetHb.
[0303] For example, an optical measurement of MetHb in blood
vessels is in a range of 0.8-2. This range has a difference of 1.1
to 1.2 between a normal value and a value indicating a septic risk.
So, these measurements based on MetHb have less than a 1%
percentage variance. In addition, during a septic condition, MetHb
may become saturated due to the large amount of NO in the blood
vessels. So, an optical measurement of MetHb alone or other
hemoglobin species alone is not able to measure these excess
saturated NO levels. The R values determined by measuring NO level
directly using a wavelength in the UV range are thus more
sensitive, accurate, have a greater dynamic range and variance, and
provide an earlier detection of septic conditions.
[0304] A baseline NO measurement in blood vessels of a healthy
general population is obtained. For example, the biosensor 100 may
obtain R values or other NO measurements using the biosensor 100.
For example, the biosensor 100 may measure an L395 value or
determine SpNO % based on an R value for a general population over
a period of time, such as hours or days. These NO measurements are
then averaged to determine a baseline NO measurement. The NO
measurement in blood vessels is then obtained for a general
population with a diagnosis of sepsis. For example, the biosensor
100 may obtain R values or other NO measurements (such as an L395
value or SpNO %) for patients diagnosed with sepsis using
traditional blood tests, such as serum lactate blood tests. The
biosensor 100 may monitor the patients throughout the diagnosis and
treatment stages. The NO measurements are then averaged to
determine a range of values that indicate a septic condition.
[0305] Predetermined thresholds may then be obtained from the NO
measurements. For example, a threshold value indicative of a
non-septic condition may be obtained. A threshold value for a
septic condition may also be obtained. The biosensor 100 is then
configured with the predetermined thresholds for the NO
measurement.
[0306] The predetermined thresholds may be adjusted based on an
individual patient's pre-existing conditions. For example, a
patient with diabetes may have lower R values. A baseline NO value
for a patient may also be determined based on monitoring of the
patient during periods without infections. The predetermined
thresholds stored in the biosensor 100 may then be adjusted based
on any individual monitoring and/or pre-existing conditions.
[0307] In addition, the predetermined thresholds may be determined
and adjusted based on positioning of the biosensor 100. For
example, different R values or other NO measurements may be
obtained depending on the characteristics of the underlying tissue,
such as tissue with high fatty deposits or with dense arterial
blood flow. The thresholds and other configurations of the
biosensor 100 may thus be adjusted depending on the underlying skin
tissue, such as a forehead, chest, arm, leg, finger, abdomen,
etc.
[0308] FIG. 35A illustrates graphical representations of PPG
signals detected from a critical care patient diagnosed with
sepsis. The biosensor 100 obtained PPG signals over a time period
of approximately two minutes around a plurality of wavelengths at
940 nm, 630 nm, 590 nm, 530 nm, 440 nm and 390 nm. Graph 3500
illustrates the I.sub.DC signal 3508 of low frequency signals with
the I.sub.AC signal filtered. The I.sub.AC signal has erratic
frequency pulses with high amplitude peaks, especially at the 390
nm with a high absorption coefficient for NO. Graph 3502
illustrates the R value 3510 obtained for 390 nm/940 nm. The R
value 3510 also has an erratic signal that fluctuates between
positive and negative values with extremely high amplitude peaks.
The R value in this example exceeds 200.
[0309] These large peaks in sepsis patients may initially create
difficulties in accurately identifying PPG waveform features, such
as insulin release periods. However, in patients with sepsis, the
PPG responses are erratic in frequency with peaks exceeding
amplitudes typically seen in insulin release periods. In addition,
the R values for 390 nm/940 nm has abnormally high values exceeding
10 times normal and then may also have negative values.
[0310] FIG. 35B illustrates graphical representations 3520 of PPG
signals detected from a clinical trial of patients. A first set of
patients were diagnosed as septic using an industry accepted blood
test. A second set of patients were diagnosed as non-septic using
an industry accepted blood test. PPG signals at a plurality of
wavelengths (940 nm, 660 nm, 530 nm, 465 nm, 395 nm) are obtained
from patients in a clinical trial. PPG signals were concurrently
obtained from the first set and second set of patients. Various R
values are then obtained from the plurality of wavelengths and then
normalized. Graph 3522 illustrates the R values at 660 nm/940 nm
for the first set of patients and second set of patients. Graph
3524 illustrates the R values at 530 nm/940 nm for the first set of
patients and the second set of patients. Graph 3526 illustrates the
R values at 465 nm/940 nm for the first and second set of patients.
Graph 3528 illustrates the R values at 395 nm/940 nm for the first
and second set of patients. Graph 3530 illustrates the R values at
940 nm/660 nm for the first and second set of patients. Graph 3532
illustrates the R values at 530/660 nm for the first and second set
of patients.
[0311] Graph 3534 illustrates the R values at 465 nm/660 nm for the
first set of patients and second set of patients. Graph 3536
illustrates the R values at 395 nm/660 nm for the first and second
set of patients. Graph 3538 illustrates the R values at 940 nm/530
nm for the first set of patients and the second set of patients.
Graph 3540 illustrates the R values at 660 nm/530 nm for the first
and second set of patients. Graph 3542 illustrates the R values at
465 nm/530 nm for the first set of patients and the second set of
patients. Graph 3544 illustrates the R values at 395/530 nm for the
first and second set of patients.
[0312] Graph 3546 illustrates the R values at 940 nm/465 nm for the
first set of patients and the second set of patients. Graph 3548
illustrates the R values at 660 nm/465 nm for the first set of
patients and the second set of patients. Graph 3550 illustrates the
R values at 530 nm/465 nm for the first and second set of patients.
Graph 35 illustrates the R values at 395 nm/940 nm for the first
and second set of patients. Graph 3552 illustrates the R values at
395 nm/6465 nm for the first and second set of patients.
[0313] Graph 3554 illustrates the R values at 940/395 nm for the
first and second set of patients. Graph 3556 illustrates the R
values at 660 nm/395 nm for the first and second set of patients.
Graph 3558 illustrates the R values at 530 nm/395 nm for the first
and second set of patients. Graph 3560 illustrates the R values at
465 nm/395 nm for the first and second set of patients.
[0314] If the variance in R values in a graph are not statistically
significant for the first set of patients and the second set of
patients, then the R value is not a good measure of sepsis. Graph
3528 and the inverse Graph 3554 illustrates a good variance in R
values between the first set of patients and second set of patients
using PPG signals at 395 nm and 940 nm. In addition, Graph 3536
(and the inverse Graph 3556) illustrate a good variance in R values
between the first set of patients and second set of patients using
PPG signals at 395 nm and 660 nm. Thus, the R values of PPG signals
at 395 nm and 940 nm or R values of PPG signals at 395 and 660 nm
may be used to determine a septic condition in a patient.
Embodiment--Detection of Digestion or Hunger
[0315] FIG. 36 illustrates a schematic diagram of graphs of PPG
signals during periods of ingestion and fasting. Graph 3600
illustrates an R value obtained from PPG signals at 395 nm and 940
nm over an approximate 88 minute time period. The patient ingested
food at approximately 19 minutes. The Graph 3600 shows insulin
release pulses with a frequency of approximately every 2-3 minutes
after ingestion. In contrast, Graph 3602 shows PPG signal response
over an approximately 102 minute period. The patient has not
ingested caloric intake. The insulin release pulses have a
frequency of approximately every 10-20 minutes. Thus, by
determining a frequency or average period between insulin release
pulses, an ingestion time or digestion stage may be determined. In
addition, a hunger level or time from ingestion may also be
determined from the time between insulin release pulses.
[0316] The stage of digestion may thus be determined using
identification of the insulin release events from the PPG signals.
For example, the insulin release events are more frequent after
ingestion during stage 1 and stage 2 of digestion and are less
frequent in response to fasting or hunger. Thus, by measuring the
frequency or time between insulin release events using the PPG
signals, a stage of digestion may be identified or a level of
fasting or hunger may be identified.
Embodiment--Calibration During Ingestion Periods
[0317] During ingestion, a greater frequency of insulin release
pulses may affect the PPG signals. The walls of the blood vessels
are constricting and harden due to muscle tension and may generate
false readings of arterial stiffness or blood flow. The calibration
for determining glucose levels may need to be adjusted during such
ingestion periods.
[0318] In addition, during insulin release, vascular imaging or
tests, such as a CT Scan or ultrasound or MRI of the blood flow of
the vascular system should be avoided. The walls of the vessels may
not exhibit normal behavior during insulin release. By measuring
the frequency or time between insulin release events using the PPG
signals, a stage of digestion may be identified. Depending on the
stage of digestion and frequency of insulin release events, the
vascular imaging or tests may be performed or delayed.
[0319] FIG. 37 illustrates a schematic flow diagram of an
embodiment of a method 3700 for identifying a PPG feature, such as
an insulin release event. A pulse or amplitude peak is detected in
PPG signals at one or more wavelengths at 3702. The frequency,
amplitude and period of the PPG feature are compared to typical or
average responses or characteristics of PPG signals during an
insulin release event at 3704. For example, PPG pulses due to an
insulin release have a much lower frequency than a heart rate. The
frequency increases after ingestion and then decreases with hunger.
The period of the pulse for an insulin pulse is longer than a
typical heartbeat, for example lasting over 4-10 seconds and have
an IAC amplitude that is at least 50% less than a heartbeat pulse.
Thus, the biosensor 100 may determine a change in amplitude of the
PPG signals and compare the change in the amplitude of the PPG
signals to a predetermined range of the amplitude of PPG signals
during an insulin release event. The predetermined range may
include an average or mean of the amplitude or a percentage of
change during the insulin release event. The predetermined range of
the amplitude may be obtained from testing of a general population
with a healthy vascular system.
[0320] Additionally, the biosensor 100 may determine a period of
the pulse and compare to a predetermined range of periods of PPG
pulses during an insulin release event. The predetermined range may
include an average or mean of the period of the pulse during an
insulin release event. The predetermined range of the period may be
obtained from testing of a general population with a healthy
vascular system.
[0321] Furthermore, a frequency or time between pulses may also be
determined and compared to predetermined frequencies or a count of
a number of pulses typically found during digestion or hunger. This
comparison may be used to determine a stage of digestion or level
of hunger or estimated time since ingestion of caloric intake.
[0322] The frequency, amplitude and period of the PPG feature may
also be compared to typical responses of PPG signals during other
events, such as deep inhalation, sepsis or other types of features.
Thus, other types of PPG responses may also be identified.
[0323] In addition, one or more parameters derived from the PPG
signals may be compared to known patterns or characteristics to
identify an insulin release pulse at 3706. For example, the IAC
signal, an L value curve or an R value curve (such as 390 nm/940
nm) is determined from the PPG signals. These parameters are then
compared to predetermined ranges for the corresponding parameter
during an insulin release event. For example, an R value for an
insulin pulse is much lower than R values in a sepsis patient. In
addition, the R value has a similar pulse shape and timing as the
PPG signal of IAC for an insulin release event while there is less
correlation between the R value and the IAC signal with deep
inhalation. Other parameters such as integrals or derivatives or
wavelet transforms or correlations between PPG signals may be
determined and compared to predetermined normal ranges during
insulin release events. The PPG feature is then identified at 3708
as an insulin release event or may be identified as a sepsis
condition, deep inhalation or other feature.
[0324] When the PPG feature is identified as an insulin release
event, the frequency or time between insulin release events may be
measured using the PPG signals to determine a stage of digestion or
a level of hunger at 3710. A time since ingestion of caloric intake
may also be estimated.
[0325] In an embodiment, substantially continuous detection of PPG
signals during a time period, e.g. over a plurality of hours, is
more likely to capture and identify an insulin release event in a
patient. Periodic measurements, such as a sampling window of PPG
signals for one to three minutes, during the same time period is
less likely to capture an insulin release event in the patient.
Since the substantially continuous measurements are more likely to
capture the occurrence of insulin release events, subsequent
glucose level measurements are more accurate as well.
Embodiment--Measurement of Heart Rate Variations Due to Insulin
Pulses
[0326] FIG. 38 illustrates a graphical representations 3800 of test
results obtained from an embodiment of the biosensor 100. In this
experiment, the PPG signals were obtained from a biosensor 100
configured on a finger of a healthy patient with no known
pre-conditions affecting vascular health. The graph 3802 depicts
normalized PPG signals 3816 obtained from a healthy patient at a
plurality of wavelengths including 940 nm, 630 nm, 590 nm, 530 nm,
465 nm, and 405 nm over a period of about three minutes. The PPG
signals have been mean removed and normalized so that the
wavelengths may be displayed on the same graph to compare relative
changes in the shape of the signals.
[0327] The second graph 3804 depicts an AC component of the PPG
signals 3816 with a DC component filtered over an approximate 15
second period. The pressure pulse wave (HR pulse) pattern in
vessels may be seen during an insulin release event or period 3808.
The insulin release period 3808 is due to the release of insulin
into the bloodstream. The pancreas releases insulin into the blood
stream in discrete pulses. These pulses of insulin in the blood
stream affect the vasodilation of the vessels. The insulin release
event 3808 includes a time period having a pulse of insulin in the
blood stream or a marked increase of insulin in the blood stream.
As seen in the graph 3804, the pressure pulse wave pattern and thus
cardiac cycle, is affected at least in part due to the insulin
release event 3808.
[0328] The third graph 3806 illustrates an R value 3812 obtained
from the PPG signals 3816 at a first wavelength of 395 nm and a
second wavelength 940 nm. A phase difference 3814 is also depicted
between the PPG signals 3816 at the first wavelength of 395 nm and
the second wavelength 940 nm. The R value 3812 is affected by the
insulin release event or period 3810 as shown in the graph 3808.
The R value 3812 increases during the insulin release period 3810
perhaps in part due to the vasodilation of the vessels caused by
the insulin pulse.
[0329] FIG. 39 illustrates graphical representations 3900 of
additional test results obtained from an embodiment of the
biosensor 100. In this experiment, the PPG signals were obtained
from a biosensor 100 configured on a finger of a second healthy
patient with no known pre-conditions affecting vascular health. The
graph 3902 depicts normalized PPG signals 3916 obtained from a
healthy patient at a plurality of wavelengths including 940 nm, 630
nm, 590 nm, 530 nm, 465 nm, and 405 nm over a period of about five
minutes.
[0330] The second graph 3904 depicts an AC component of the PPG
signals 3916 with a DC component filtered over an approximate 12
second period. The pressure pulse wave (HR pulse) pattern in
vessels may be seen during an insulin release event or period 3908.
The insulin release period 3908 is due to the release of insulin
into the bloodstream. The insulin release event 3908 includes a
time period having a pulse of insulin in the blood stream or a
marked increase of insulin in the blood stream. As seen in the
graph 3904, the pressure pulse wave pattern and thus cardiac cycle,
is affected at least in part due to the insulin release event
3908.
[0331] The third graph 3906 illustrates an R value 3912 obtained
from the PPG signals 3916 at a first wavelength of 395 nm and a
second wavelength 940 nm. A phase difference 3914 is also depicted
between the PPG signals 3916 at the first wavelength of 395 nm and
the second wavelength 940 nm. The R value 3912 is affected by the
insulin release event or period 3910 as shown in the graph 3908.
The R value 3912 increases during the insulin release period 3910
perhaps in part due to the vasodilation of the vessels caused by
the insulin pulse.
[0332] FIG. 40 illustrates graphical representations 4000 of
additional test results obtained from an embodiment of the
biosensor 100. In this experiment, the PPG signals were obtained
from a biosensor 100 configured on a finger of a third healthy
patient with no known pre-conditions affecting vascular health. The
graph 4002 depicts normalized PPG signals 4016 obtained from the
third healthy patient at a plurality of wavelengths including 940
nm, 630 nm, 590 nm, 530 nm, 465 nm, and 405 nm over a period of
about twenty minutes.
[0333] The second graph 4004 depicts an AC component of the PPG
signals 4016 with a DC component filtered over an approximate 16
second period. The pressure pulse wave (HR pulse) pattern in
vessels may be seen during an insulin release event or period 4008.
The insulin release period 4008 is due to the release of insulin
into the bloodstream. The insulin release event 4008 includes a
time period having a pulse of insulin in the blood stream or a
marked increase of insulin in the blood stream. As seen in the
graph 4004, the pressure pulse wave pattern, and thus cardiac
cycle, is affected at least in part due to the insulin release
event 4008.
[0334] The third graph 4006 illustrates an R value 4012 obtained
from the PPG signals 4016 at a first wavelength of 395 nm and a
second wavelength 940 nm. A phase difference 4014 is also depicted
between the PPG signals 4016 at the first wavelength of 395 nm and
the second wavelength 940 nm. The R value 4012 is affected by the
insulin release event or period 4010 as shown in the graph 4006.
The R value 4012 increases during the insulin release period 4010
perhaps in part due to the vasodilation of the vessels caused by
the insulin pulse.
[0335] Using the PPG signals, the biosensor 100 may thus identify
an insulin release event in vessels in a healthy patient. The
biosensor 100 may use one or more PPG parameters, such as a
comparison (such as a cross correlation) of the pressure pulse wave
pattern in an AC component of a PPG signal or changes in the R
value to identify the insulin release event. The biosensor 100 may
also use other PPG parameters derived from the PPG signals to
identify an insulin release period, such a phase difference between
PPG signals, L values, etc. For example, a typical PPG waveform 910
includes a systolic peak (SP) 912, a diastolic peak (DP) 916, a
dicrotic notch (914), trough 918 and pulse width (tnext trough).
Other characteristics include pulse pressure area (PP), systolic
area (As), diastolic area (Ad), augmented pressure (AP), pulse
interval, peak to peak interval, augmentation index
(AI=PP/(PP-AP).times.100%), crest time, etc. These or other
characteristics may be determined from a PPG waveform or a first or
second derivative of the PPG waveform. For example, various ratios
may be derived from a second derivate of the PPG waveform, e.g.,
such as the early systolic negative wave to the early systolic
positive wave (Ratio b/a). These and other characteristics may be
measured in a PPG waveform (including its derivatives) and be
included as PPG parameters.
[0336] FIG. 41 illustrates graphical representations 4100 of
additional test results obtained from an embodiment of the
biosensor 100. In this experiment, the PPG signals 4116 were
obtained from a biosensor 100 configured on a finger of a patient
with Type I Diabetes. The graph 4102 depicts normalized PPG signals
4116 obtained from the patient at a plurality of wavelengths
including 940 nm, 630 nm, 590 nm, 530 nm, 465 nm, and 405 nm over a
period of about two minutes.
[0337] The second graph 4104 depicts an AC component of the PPG
signals 4116 with a DC component filtered over an approximate fifty
second period. The pressure pulse wave (HR pulse) pattern in
vessels may be seen during an insulin release event or period 4108.
The insulin release event 4108 includes a time period having a
pulse of insulin in the blood stream or a marked increase of
insulin in the blood stream. As seen in the graph 4104, the
pressure pulse wave pattern and thus cardiac cycle, is affected at
least in part due to the insulin release event 4108. Thus, the
insulin release event may affect the HR pulse pattern during the
insulin release period in a patient with Type 1 Diabetes.
[0338] The third graph 4106 illustrates an R value 4112 obtained
from the PPG signals 4116 at a first wavelength of 395 nm and a
second wavelength 940 nm. A phase difference 4114 is also depicted
between the PPG signals 4116 at the first wavelength of 395 nm and
the second wavelength 940 nm. The R value 4112 is affected by the
insulin release event or period 4108 as shown in the graph 4006.
The R value 4112 increases during the insulin release period 4010
perhaps in part due to the vasodilation of the vessels caused by
the insulin pulse. In addition, the phase difference 4114 increases
during the insulin release period 4010.
[0339] FIG. 42 illustrates graphical representations 4200 of
additional test results obtained from an embodiment of the
biosensor 100. In this experiment, the PPG signals 4216 were
obtained from a biosensor 100 configured on a finger of a patient
with Type II Diabetes. The graph 4202 depicts normalized PPG
signals 4216 obtained from the patient at a plurality of
wavelengths including 940 nm, 630 nm, 590 nm, 530 nm, 465 nm, and
405 nm over a period of about sixteen seconds.
[0340] The second graph 4204 depicts an AC component of the PPG
signals 4216 with a DC component filtered over an approximate
twenty second period. The pressure pulse wave (cardiac cycle)
pattern in vessels may be seen during an insulin release event or
period 4208. The insulin release event 4208 includes a time period
having a pulse of insulin in the blood stream or a marked increase
of insulin in the blood stream. As seen in the graph 4204, the
pressure pulse wave pattern and thus cardiac cycle, is affected at
least in part due to the insulin release event 4208. Thus, the
insulin release event may affect the HR pulse pattern during the
insulin release period in a patient with Type 1I Diabetes.
[0341] The third graph 4206 illustrates the heart rate 4212
determined from the PPG signals 4216. The heart rate during the
insulin release period 4210 fluctuates as well. The insulin release
event 4210 affects the pressure pulse wave pattern, cardiac cycle
and heart rate in the patient with Type 2 Diabetes.
[0342] A comparison of the variability of the cardiac cycle or
pressure pulse wave pattern in PPG signals during an insulin
release event was performed. In general, healthy patients who are
NOT diabetic exhibit greater variations in pressure pulse wave
patterns during insulin release events. A comparison of periodicity
of the PPG signals in graph 4100 of the patient with Type 1
Diabetes was made with the periodicity of the PPG signals in graphs
3800, 3900, 4000 of healthy patients. The pressure pulse wave
pattern exhibited less variability during the insulin release event
in the patient with Type I Diabetes than the pressure pulse wave
patterns for the healthy patients.
[0343] As diabetes progresses in patients, the variability in the
heart rate and/or pulse wave pattern decreases during insulin
release events. It is assumed that the endothelium dysfunction
increases due to the disease progression, and that the blood
vessels are hardening. This atherosclerosis decreases the
vasodilation in vessels which in turn decreases the variability in
PPG signals during an insulin release event. Patients with Type 1
diabetes who are long time insulin users have the least amount of
heart rate variability. The variability of the heart rate and/or
pulse wave pattern during an insulin release event may thus be used
in evaluating vascular health.
[0344] Eventually though, as diabetes progresses in patients to
later stages, the heart itself tends to have much more heart
irregularities and non-rhythmic patterns that may affect pressure
pulse waves. When a patient with Type I and Type II diabetes is
affected by such heart disease, then the extent of variability in
PPG signals may increase (rather than decrease) during an insulin
release event.
[0345] An insulin release event may affect both the vessels and the
heart in the vascular system. During insulin release events, a
chemical reaction releases ET-1 & NO from the endothelium cells
lining walls of vessels in the vascular system. These endothelium
cells also line the inner heart chambers and generate insulin
releases in the heart. The heart exhibits altered electrical
activity during insulin release events that affects the cardiac
cycle and heart rate (including the bpm and/or heart rate pulse
pattern) and pressure pulse wave pattern in vessels. So insulin
release events are affecting both the heart and vascular
structures.
[0346] Insulin release events may occur from 1 minute to 20 minutes
apart depending on the person, activity, and caloric intake. Thus,
insulin release events may occur during a health screening,
physical exam or diagnostic test. These tests may be adversely
affected by the variability in the pressure pulse wave and heart
rate during an insulin release event. For example, a blood pressure
measurement during an insulin release event may indicate a false
reading of high blood pressure. In another example, an ECG (also
known as an EKG) or stethoscopic exam performed during an insulin
release event may result in a false indication of a heart
arrhythmia. Other tests that may be affected by an insulin release
event include an echocardiogram, heart rate measurement, MRI of the
heart or vessels, electromyography (EMG), etc.
[0347] Thus, in an embodiment, to prevent errors in these health
tests or screenings, insulin release events in vessels or the heart
are identified and monitored, e.g. using PPG signals as described
herein or by other means. During an insulin release event, the
health testing may be suspended, or test results occurring during
such an insulin release event may be highlighted as such.
[0348] FIG. 43 illustrates a schematic flow diagram of an
embodiment of a method 4300 for performing a health screening. The
health screening may include a test affected by an insulin release
event, such as one or more of an ECG, EKG, EMG, stethoscopic exam,
blood pressure measurement, heart rate measurement, MRI, CATSCAN,
echocardiogram, etc. During all or part of the health screening,
the heart and/or other vascular tissue is monitored for an insulin
release event at 4302. The biosensor 100 described herein may
monitor for the insulin release event using PPG signals or other
means may be employed (such as using ECG readings). The health
screening is performed at 4304. The health screening may be for a
short period (such as a 1 minute ECG) or continuous monitoring over
hours or days (such as a heart monitor). An insulin release event
is identified at 4306, and an indication of the insulin release
event is generated at 4308. The indication may include an audible
and/or visual alert. The health provider performing the health
screening is then aware that the testing may not be accurate due to
the insulin release event. The health provider may choose to
reperform the health screening or ignore the test results for the
period during the insulin release event.
[0349] In another embodiment, the indication may alternatively or
additionally include halting the health screening, e.g. such as an
ECG. The health screening may resume after the insulin release
event. In another embodiment, the indication of the insulin release
event may alternatively or additionally be generated as part of the
testing results, e.g. an indication that the results may not be
accurate due to the insulin release event or an indication of the
period of the insulin release event on the test results.
[0350] This process helps to prevent errors in health screenings
due to anomalies in heart rate and pressure pulse wave patterns
during an insulin release event. The insulin release event in
vessels or the heart are identified and monitored, e.g. using PPG
signals as described herein or by other means. During an insulin
release event, the health testing may be suspended or indicated as
occurring during such insulin release event. The insulin release
event monitoring may also be performed to help control pacemakers
or other devices.
Embodiment--Determination of Glucose Levels in Blood Flow Using a
Plurality of Parameters
[0351] As previously discussed, an R value obtained using
L.lamda.1=380 nm-400 nm and L.lamda.2.gtoreq.660 nm may be used to
determine concentration levels of NO in blood flow. In an
embodiment, the concentration level of NO m ay be used to determine
a diabetic risk or a blood glucose level. In another example, the R
value obtained at L.lamda.1=380 nm-400 nm and L.lamda.2.gtoreq.660
nm is determined over a period of time and used with a calibration
table to determine the level of glucose in blood flow, e.g. without
determining a level of NO.
[0352] In addition to the R value obtained using L.lamda.1=380
nm-400 nm and L.lamda.2.gtoreq.660 nm, other parameters may be
considered in addition to and/or alternatively to this R value in
determining a glucose level in blood flow. For example, one or more
of the following parameters may be used in determining a glucose
level in blood flow:
[0353] R value obtained using PPG signals at 395 nm (or in a range
of 380 nm-400 nm) and at 940 nm (or equal to or above 660 nm)
[0354] R value obtained using PPG signals at 395 nm (or in a range
of 380 nm-400 nm) and at 530 nm (or in a range of 510 nm-550
nm)
[0355] R value obtained using PPG signals at 530 nm (or in a range
of 510 nm-550 nm) and at 940 nm (or equal at or above 660 nm)
[0356] L value determined using PPG signals around 395 nm (or in a
range of 380 nm-400 nm)
[0357] L value determined using PPG signals around 940 nm (or equal
at or above 660 nm)
[0358] Measurement of a Time or Phase Delay between PPG signals at
395 nm (or in a range of 380 nm-400 nm) and at 940 nm (or equal at
or above 660 nm)
[0359] Measurement of Correlation of Phase Shape between PPG
signals at 395 nm (or in a range of 380 nm-400 nm) and at 940 nm
(or equal at or above 660 nm)
[0360] Periodicity of a PPG signal at 395 nm (or in a range of 380
nm-400 nm) or at 940 nm (or equal at or above 660 nm)
[0361] Skin Temperature
[0362] The above parameters are exemplary and additional or
alternate parameters may also be considered in determining a
glucose level in blood flow.
[0363] In an embodiment, the parameters include L values and/or R
values obtained using wavelengths having different depths of
penetration into the tissue, e.g. 395 nm, 530 nm, 940 nm. The R and
L values may thus reflect the level of circulation at various
layers of tissue. Poor circulation results in varying R and L
values measured using the different wavelengths while good
circulation results in less variable R and L values. The
differences in good and bad circulation affect the R and L values,
and thus the glucose level readings.
[0364] Other parameters may also include a time delay and/or pulse
shape correlation between PPG signals at different depths of
tissue, e.g. between PPG signals at 395 nm and 940 nm. For example,
the PPG signals may be processed using a cross correlation function
or a Hilbert transformation or another algorithm that determines
similarities in pulse shape and temporal relationship between the
PPG signals. The time delay between the two PPG signals may also be
calculated from the phase shift of their wavelet transforms. The
Phase Delay and Pulse Shape Correlation provides a measurement of
the effects of outer and inner tissue layers of vessels on the PPG
signal, e.g. muscle cells during vasoconstriction. The Phase Delay
and a Pulse Shape Correlation provide information on a level of
vasoconstriction or vasodilation, circulation and arterial
stiffness.
[0365] When the PPG signals have a greater difference in phase or
timing, this indicates that blood flow in the tissue near the
surface is decreased, e.g. due to vasoconstriction, due to low
blood circulation level or an imbalance of NO and ET-1 or arterial
stiffness. When blood flow is increased to the tissue, the PPG
signals at the UV and IR wavelengths exhibit a lower variance in
pulse shape and a higher correlation value. This decrease in the
difference in the pulse shape of the PPG signals at the different
wavelengths indicates an increase of blood flow, e.g. due to
vasodilation. The vascular flow at the different tissue depths thus
provides information on circulation. In addition, when the
correlation between pulse shapes decreases, it may indicate an
activity such as digestion or circulation issues are occurring.
[0366] Another parameter may also include a measurement of
periodicity of a PPG signal, e.g. at 395 nm and/or at 940 nm. For
example, the periodicity of a PPG signal may include a frequency
domain analysis, using a Discrete Fourier Transform
(DFT)/determining the periodogram of a signal or using an
autocorrelation (cross-product measures similarity across time).
Specific measurements or the PPG signal may be determined and input
as parameters or compared, e.g. a time between systolic and
diastolic points of the PPG signal, e.g. a stroke length, stroke
period, amplitude, etc. During moments of stress, the PPG signal
exhibits decreased periodicity or similarity. Blood volume may
change with heart rate as well.
[0367] Temperature of the patient, such as skin temperature, during
PPG signal readings may also be used as an input. The skin
temperature is associated with circulation and arterial diameter.
For example, a cold skin temperature of extremities in a normal
temperature room with a normal heart rate, may indicate low
circulation.
[0368] One or more of these parameters may be used to obtain
glucose levels or other health data. Additional parameters may also
be employed in such determinations.
[0369] FIG. 44 illustrates a schematic block diagram of an
embodiment of a processing device for processing the one or more
parameters. The processing device 4400 performs one or more of the
functions described herein in response to instructions stored in a
memory device 4402 and/or other storage devices, either local or
remote.
[0370] In an embodiment, one or more types of artificial
intelligence or neural network processing models may be implemented
by the processing device 4300 to determine health data from one or
more of the parameters. For example, the processing device 4300 may
implement a regression model or classifier type model. A regression
module neural network may be trained using one or more learning
vectors with similar types of input parameters and known outputs as
described further hereinabove. A classifier neural network may be
applied to the one or more parameters to determine a glucose level
or other health data. The glucose level may be expressed as within
one or more ranges, such as normal, above normal, below normal,
etc. (Region 1, 2, 3, 4 of glucose range--normal, below or
above).
[0371] In another embodiment, a custom algorithm or correlation may
be applied to one or more of the parameters to determine a glucose
level or other health data. Additional or alternate parameters may
be included in these determinations. In addition, other types of AI
processing, custom algorithms or quantum processing may be applied
to determine health data from one or more of these parameters.
[0372] FIG. 45A illustrates a graphical representation 4500 of
clinical test results for a first plurality of patients. The
predicted glucose levels are compared with reference glucose levels
that were obtained using a standard blood test. As seen in the
graphs 4502, 4504, 4506, 4508, 4510 and 4512 for each of the first
plurality of patients, the predicted glucose levels in testing are
very similar to the reference glucose levels. FIG. 45B illustrates
the graphical representation 4500 of clinical test results for a
second plurality of patients. The predicted glucose levels are
again compared with reference glucose levels that were obtained
using a standard blood test. As seen in the graphs 4514, 4516,
4518, 4520, 4522 and 4524 for each of the second plurality of
patients, the predicted glucose levels in testing are very similar
to the reference glucose levels.
[0373] FIG. 46 illustrates a graphical representation of a
distribution of errors between the predicted glucose levels and the
reference glucose levels from FIG. 45A and FIG. 45B. The graph 4600
illustrates Parke's consensus error grid analysis between the
predicted glucose levels and the reference glucose levels for the
plurality of patients 4602. The graph 4600 shows that 89.39% of the
error was within region A, 10.30% in region B, and 0.30% in region
C. The average error is 9.78%. Thus, the predicted glucose levels
obtained using the plurality of parameters were under an average
10% error from the reference glucose levels.
[0374] FIG. 47 illustrates a schematic flow diagram of an
embodiment of a method 4700 for determining glucose levels using a
plurality of parameters. PPG signals are obtained at a plurality of
wavelengths having different depths of penetration in tissue at
4702. For example, optical signals are reflected from tissue of a
patient at wavelengths in a range of 380 nm-400 nm, and/or in a
range of 510 nm-550 nm and/or at a wavelength greater than 660 nm.
The optical signals are detected and converted to electrical PPG
signals. One or more PPG parameters are determined using the
plurality of wavelengths at 4704. For example, the following PPG
parameters may be determined:
[0375] R value obtained using PPG signals at 395 nm (or in a range
of 380 nm-400 nm) and at 940 nm (or equal to or above 660 nm)
[0376] R value obtained using PPG signals at 395 nm (or in a range
of 380 nm-400 nm) and at 530 nm (or in a range of 510 nm-550
nm)
[0377] R value obtained using PPG signals at 530 nm (or in a range
of 510 nm-550 nm) and at 940 nm (or equal to or above 660 nm)
[0378] L value determined using PPG signals around 395 nm (or in a
range of 380 nm-400 nm)
[0379] L value determined using PPG signals around 940 nm (or equal
to or above 660 nm)
[0380] The above L values and R values are exemplary only. As
described below with respect to FIG. 49 and FIG. 50, PPG signals at
other wavelengths may be employed to determine L values and R
values that are used to determine glucose levels.
[0381] In addition, one or more other PPG parameters may be
determined, such as a phase delay between a plurality of the PPG
signals at different wavelengths, a correlation of phase shape
between a plurality of PPG signals at different wavelengths or a
periodicity of one or more of the PPG signals at 4706. For example,
the following may be determined: [0382] A measurement of a time or
phase delay between PPG signals at 395 nm (or in a range of 380
nm-400 nm) and at 940 nm (or equal to or above 660 nm), [0383] A
measurement of correlation of Phase Shape between PPG signals at
395 nm (or in a range of 380 nm-400 nm) and at 940 nm (or equal to
or above 660 nm), or [0384] A periodicity of a PPG signal at 395 nm
(or in a range of 380 nm-400 nm) or at 940 nm (or equal to or above
660 nm).
[0385] These PPG parameters obtained using the PPG signals at one
or more wavelengths may be used in the determination of the
concentration level of glucose in blood flow. The above PPG
parameters are exemplary and additional or alternate PPG parameters
may also be obtained. For example, a typical PPG waveform 910
includes a systolic peak (SP) 912, a diastolic peak (DP) 916, a
dicrotic notch (914), trough 918 and pulse width (tnext trough).
Other characteristics include pulse pressure area (PP), systolic
area (As), diastolic area (Ad), augmented pressure (AP), pulse
interval, peak to peak interval, augmentation index
(AI=PP/(PP-AP).times.100%), crest time, etc. Additional PPG
parameters may include the pulse shape (measured by autoregression
coefficients and moving averages), variance, instant energy
information, energy variance, etc. Other parameters may be
extracted by representing the PPG signal as a stochastic
auto-regressive moving average (ARMA). Parameters also may be
extracted by modeling the energy of the PPG signal using the
Teager-Kaiser operator, calculating the heart rate and cardiac
synchrony of the PPG signal, or determining the zero crossings of
the PPG signal. These or other characteristics may be determined
from a PPG waveform or a first or second derivative of the PPG
waveform, e.g., such as the early systolic negative wave to the
early systolic positive wave (Ratio b/a) and be included as PPG
parameters.
[0386] In addition to the PPG parameters obtained using PPG
signals, other parameters may also be used, such as a user's vitals
(skin temperature, blood pressure, etc.) and user data (such as
age, pre-existing conditions, etc.) at 4708. The above parameters
are exemplary and additional or alternate parameters may also be
obtained.
[0387] In 4710, a concentration level of glucose is obtained using
the PPG parameters. The concentration level of glucose may also be
obtained using the user's vitals and user data.
[0388] FIG. 48 illustrates a schematic flow diagram of an
embodiment of a method 4800 for determining a concentration of
glucose in blood flow using a plurality of parameters in more
detail. At 4802, a first PPG signal is obtained at a first
wavelength with a high absorption coefficient for NO in a range of
380-410 nm, preferably 390-395 nm. A second PPG signal is obtained
at a second wavelength with a low absorption coefficient for NO,
such as equal to or above 660 nm and preferably 940 nm. One or more
additional PPG signals are obtained at one or more additional
wavelengths with a lower absorption coefficient for NO, preferably
with a different penetration depth than the first and second
wavelength, such as in a range of 510 nm-550 nm or otherwise below
660 nm.
[0389] At 4806, the PPG signal at a wavelength with a high
absorption coefficient for NO is used to obtain a first L value,
and a PPG signal at a wavelength with a low absorption coefficient
for NO is used to obtain a second L value. Additional L values may
be obtained, such as using a PPG signal in a range of 510 nm-550
nm. The first and second L values are used to determine a first R
value. A second R value may be obtained using PPG signals at 395 nm
(or in a range of 380 nm-400 nm) and at 530 nm (or in a range of
510 nm-550 nm). A third R value may be obtained using PPG signals
at 530 nm (or in a range of 510 nm-550 nm) and at 940 nm (or equal
to or above 660 nm). As described below with respect to FIG. 49 and
FIG. 50, PPG signals at other wavelengths may be obtained to
determine L values and R values that are then used to determine
glucose levels.
[0390] One or more other PPG parameters may be obtained using the
PPG signals at the plurality of wavelengths at 4808. For example,
other PPG parameters may include a measurement of a time or phase
delay between PPG signals at 395 nm (or in a range of 380 nm-400
nm) and at 940 nm (or equal to or above 660 nm), a measurement of
correlation of phase shape between PPG signals at 395 nm (or in a
range of 380 nm-400 nm) and at 940 nm (or equal to or above 660
nm), or a periodicity of a PPG signal at 395 nm (or in a range of
380 nm-400 nm) or at 940 nm (or equal to or above 660 nm). The
above PPG parameters are exemplary and additional or alternate PPG
parameters may also be obtained. For example, a typical PPG
waveform 910 includes a systolic peak (SP) 912, a diastolic peak
(DP) 916, a dicrotic notch (914), trough 918 and pulse width (tnext
trough). Other characteristics include pulse pressure area (PP),
systolic area (As), diastolic area (Ad), augmented pressure (AP),
pulse interval, peak to peak interval, augmentation index
(AI=PP/(PP-AP).times.100%), crest time, etc. Additional PPG
parameters may include the pulse shape (measured by autoregression
coefficients and moving averages), variance, instant energy
information, energy variance, etc. Other parameters may be
extracted by representing the PPG signal as a stochastic
auto-regressive moving average (ARMA). Parameters also may be
extracted by modeling the energy of the PPG signal using the
Teager-Kaiser operator, calculating the heart rate and cardiac
synchrony of the PPG signal, or determining the zero crossings of
the PPG signal. These or other characteristics may be determined
from a PPG waveform or a first or second derivative of the PPG
waveform, e.g., such as the early systolic negative wave to the
early systolic positive wave (Ratio b/a) and be included as PPG
parameters.
[0391] Other health parameters may also be obtained at 4810, such
as a user's vitals (skin temperature, blood pressure, etc.) and
user data (such as age, pre-existing conditions, etc.).
[0392] A plurality of the parameters are processed at 4812 to
obtain a concentration level of glucose at 4814. The parameters may
be processed using an artificial intelligence (AI) or machine
learning technique. The AI processing device executes a machine
learning algorithm with the parameters as inputs and determines the
concentration level of glucose.
[0393] The AI processing device may be pre-configured with weights,
parameters or other learning vectors derived from a training set.
The training set preferably includes the same input parameters and
known values of the glucose levels. For example, glucose levels may
be obtained in a clinical setting using a known standard blood
test. The PPG signals and parameters are also obtained. This
training set is provided to a neural network training algorithm to
generate the learning vectors. The training set may include further
patient data such as age, weight, BMI, pre-existing conditions
(diabetes), medical history (family members with diabetes),
etc.
[0394] During a learning stage, the neural network adjusts
parameters, weights and thresholds iteratively to yield a known
output from the input parameters (PPG parameters, patient vitals
and/or patient data). The training is performed using defined set
of rules also known as the learning algorithm. For example, a
gradient descent training algorithm is used in case of supervised
training model. In case the actual output is different from target
output, the difference or error is determined. The gradient descent
algorithm changes the weights of the network in such a manner to
minimize this error. Other learning algorithms that may be
implemented include back propagation, least mean square (LMS)
algorithm, a "random forest", deep belief network trained using
restricted Boltzmann machines, or support vector machine. The
analysis may use any known regression analysis technique, such as,
for example and without limitation, random forests, support vector
machines, or a deep belief network trained using restricted
Boltzmann machines. In another embodiment, the machine learning
process may include a classifier type algorithm. Other types of AI
processing models may also be implemented to analyze the plurality
of parameters (PPG parameters, patient vitals and/or patient data)
to obtain a concentration level of glucose in blood flow.
[0395] Alternatively to AI processing, the plurality of parameters
may also be processed using a custom algorithm or processing model
to obtain the concentration level of glucose in 4814.
[0396] Personal calibration of the biosensor 100 may be performed
as well. For example, a person may input a glucose measurement
obtained using a blood test with a strip meter. The biosensor 100
may then determine PPG signals and perform calibration if needed.
This self-calibration may be requested as well if a glucose
measurement is abnormal, e.g. outside expected ranges for a
user.
[0397] FIG. 49 illustrates graphical representations 4900 of
additional test results obtained from an embodiment of the
biosensor 100. The graphical representations 4900 illustrate R
values obtained using a plurality of wavelengths. A first set of R
values are obtained from patients with Type 1 Diabetes, a second
set of R values are obtained from patients with Type 2 Diabetes and
a third set of R values are obtained from patients that are
non-Diabetic. A first graph 4902 illustrates R values from the
different set of patients obtained using PPG signals at 660 nm and
940 nm. A second graph 4904 illustrates R values obtained using PPG
signals at 530 nm and 940 nm. A third graph 4906 illustrates R
values obtained using PPG signals at 495 nm and 940 nm. A fourth
graph 4908 illustrates R values obtained using PPG signals at 395
nm and 940 nm. A fifth graph 4910 illustrates R values obtained
using PPG signals at 530 nm and 660 nm. A sixth graph 4912
illustrates R values obtained using PPG signals at 465 nm and 660
nm. A seventh graph 4914 illustrates R values obtained using PPG
signals at 395 nm and 660 nm. An eighth graph 4916 illustrates R
values obtained using PPG signals at 465 nm and 530 nm. A ninth
graph 4918 illustrates R values obtained using PPG signals at 395
nm and 530 nm. One or more of these R values may be used to
determine a concentration level of glucose as described herein,
e.g., with respect to FIG. 48.
[0398] FIG. 50 illustrates graphical representations 5000 of
additional test results obtained from an embodiment of the
biosensor 100. The graphical representations 5000 illustrate
various PPG parameters obtained using a plurality of wavelengths. A
first set of measurements are obtained from patients with Type 1
Diabetes, a second set of measurements are obtained from patients
with Type 2 Diabetes and a third set of measurements are obtained
from patients that are non-Diabetic. A first graph 5002 illustrates
R values obtained using PPG signals at 395 nm and 465 nm. This R
value may be used with one or more of the R values in FIG. 49 to
determine a concentration level of glucose as described herein,
e.g., with respect to FIG. 48. A second graph 5004 illustrates L
values obtained using PPG signals at 940 nm, a third graph 5006
illustrates L values obtained using PPG signals at 660 nm, a fourth
graph 5008 illustrates L values obtained using PPG signals at 530
nm, and a fifth graph 5010 illustrated L values obtained using PPG
signals at 465 nm. The sixth graph 5012 illustrates L values
obtained using PPG signals at 395 nm. The L values obtained using
PPG signals at 940 nm, 660 nm, 530 nm, 465 nm may be used with L
values obtained using PPG signals at 395 nm to determine a
concentration level of glucose as described herein, e.g., with
respect to FIG. 48.
[0399] FIG. 50 also illustrates other PPG parameters obtained using
the biosensor 100. For example, graph 5014 illustrates a phase
delay or time difference between systolic points of the pressure
pulse wave in the PPG signals obtained at 940 nm and 395 nm for a
plurality of patients. Graph 5016 illustrates a phase delay or time
difference between systolic points of the pressure pulse wave of
PPG signals obtained at 660 nm and 395 nm for a plurality of
patients. Graph 5018 illustrates a phase delay or time difference
between systolic points of the pressure pulse wave of PPG signals
obtained at 530 nm and 395 nm for a plurality of patients. These
phase delay measurements between the different PPG signals may be
used as PPG parameters to determine a concentration level of
glucose as described herein, e.g., with respect to FIG. 48.
[0400] FIG. 51 illustrates graphical representations 5100 of
additional test results obtained from an embodiment of the
biosensor 100. The graphical representations 5100 illustrate
various other PPG parameters obtained using a plurality of
wavelengths. A first set of measurements are obtained from patients
with Type 1 Diabetes, a second set of measurements are obtained
from patients with Type 2 Diabetes and a third set of measurements
are obtained from patients that are non-Diabetic.
[0401] Graph 5102 illustrates a phase delay or time difference
between systolic points of the pressure pulse wave of PPG signals
obtained at 465 nm and 395 nm for a plurality of patients. Graph
5104 illustrates a phase delay or time difference between diastolic
points of the pressure pulse wave of PPG signals obtained at 940 nm
and 395 nm for a plurality of patients. Graph 5106 illustrates a
phase delay or time difference between diastolic points of the
pressure pulse wave of PPG signals obtained at 660 nm and 395 nm
for a plurality of patients. Graph 5108 illustrates a phase delay
or time difference between diastolic points of the pressure pulse
wave of PPG signals obtained at 530 nm and 395 nm for a plurality
of patients. Graph 5110 illustrates a phase delay or time
difference between diastolic points of the pressure pulse wave of
PPG signals obtained at 465 nm and 395 nm for a plurality of
patients. These phase delay measurements between the different PPG
signals may be used as PPG parameters to determine a concentration
level of glucose as described herein, e.g., with respect to FIG.
48.
[0402] The graph 5112 illustrates beat time measurements obtained
from PPG signals at 940 nm. The beat time measurements is a
measurement of the time from the diastolic to systolic points of
the pressure pulse wave. The graph 5114 illustrates the beat time
measurements obtained using PPG signals at 660 nm, graph 5116
illustrates the beat time measurements obtained using PPG signals
at 530 nm and graph 5118 illustrates beat time measurements
obtained using PPG signals at 465 nm. As seen in these graphs, the
wavelength of the PPG signal may affect the signal to noise ratio
and measurement of the cardiac cycle and heart rate. Though the
systolic phase difference, diastolic phase difference and beat time
are described herein, other parameters of the pressure pulse wave
may be used as PPG parameters, such as dicrotic notch
[0403] FIG. 52 illustrates graphical representations 5200 of
additional test results obtained from an embodiment of the
biosensor 100. The graphical representations 5200 illustrate
various other PPG parameters obtained using PPG signals at a
plurality of wavelengths. A first set of measurements are obtained
from patients with Type 1 Diabetes, a second set of measurements
are obtained from patients with Type 2 Diabetes and a third set of
measurements are obtained from patients that are non-Diabetic.
Graph 5202 illustrates a beat time measured at 395 nm. As described
with respect to FIG. 51, the wavelength of the PPG signal may
affect the measurement of the cardiac cycle and heart rate. The
underlying tissue affects the absorption properties and quality of
the PPG signals. Thus, the wavelength of the PPG signal for
detection of heart rate may be selected depending on the underlying
tissue and quality of the signal.
[0404] Graph 5204 illustrates a phase delay measurement between 940
nm and 395 nm. Graph 5206 illustrates a phase delay measurement
between PPG signals at 660 nm and 395 nm for a plurality of
patients. Graph 5208 illustrates a phase delay measurement between
PPG signals at 530 nm and 395 nm for a plurality of patients. Graph
5210 illustrates a phase delay measurement between PPG signals at
465 nm and 395 nm. Graph 5212 illustrates autocorrelation
measurements of PPG signals at 940 nm. Graph 5214 illustrates a
correlation coefficient between PPG signals at 940 nm and 395 nm
for a plurality of patients. Graph 5216 illustrates a correlation
coefficient between PPG signals at 660 nm and 395 nm for a
plurality of patients. Graph 5218 illustrates a correlation
coefficient between PPG signals at 530 nm and 395 nm for a
plurality of patients. These heart rate, phase delay and
correlation measurements may be used as PPG parameters to determine
a concentration level of glucose as described herein, e.g., with
respect to FIG. 48.
[0405] FIG. 53 illustrates graphical representations 5300 of
additional test results obtained from an embodiment of the
biosensor 100. The graphical representations 5300 illustrate
various other PPG parameters obtained using a plurality of
wavelengths. A first set of measurements are obtained from patients
with Type 1 Diabetes, a second set of measurements are obtained
from patients with Type 2 Diabetes and a third set of measurements
are obtained from patients that are non-Diabetic. Graph 5302
illustrates a correlation coefficient between PPG signals at 465 nm
and 395 nm for the plurality of patients. Graph 5304 illustrates a
measurement of signal to noise ratio (SNR) of PPG signals at 940 nm
for the plurality of patients. Graph 5306 illustrates a measurement
of signal to noise ratio (SNR) of PPG signals at 660 nm for the
plurality of patients. Graph 5308 illustrates a measurement of
signal to noise ratio (SNR) of PPG signals at 530 nm for the
plurality of patients. Graph 5310 illustrates a measurement of
signal to noise ratio (SNR) of PPG signals at 465 nm for the
plurality of patients. Graph 5312 illustrates a measurement of
signal to noise ratio (SNR) of PPG signals at 395 nm for the
plurality of patients. Graph 5314 illustrates a temperature
measurement for the plurality of patients. These correlation
measurements, SNR measurements, and temperature measurements may be
used as parameters to determine a concentration level of glucose as
described herein, e.g., with respect to FIG. 48.
[0406] The SNR measurements may be used to select a wavelength for
use, e.g. a PPG signal with a higher SNR may be used over a PPG
signal with a lower SNR depending on the measurement (such as heart
rate). The SNR measurement may also be used to determine motion or
neural activity present in the PPG signal. When a SNR measurement
for a PPG signal is below a threshold, the PPG signal may not be
used or measurements based on the PPG signal may be flagged as
unreliable.
[0407] One or more of the parameters in FIGS. 49-53 may also be
used to detect a diabetic risk or a diabetic condition of a user.
When a diabetic risk or diabetic condition is determined, the
biosensor 100 may then determine a Type I diabetic risk or a Type
II diabetic risk.
[0408] FIG. 54 illustrates a schematic flow diagram of an
embodiment of a method 5400 for determining a concentration of a
substance in blood flow using a plurality of parameters. The
concentration level of substances other than glucose may also be
obtained using the plurality of parameters described herein (PPG
parameters, patient vitals and/or patient data).
[0409] At 5402, a first PPG signal is obtained at a wavelength with
a high absorption coefficient for the substance and a second PPG
signal is obtained at a wavelength with a lower absorption
coefficient for the substance. For example, absorption coefficients
for one or more frequencies that have an intensity level responsive
to concentration level of the substance in blood flow may be
determined. Other frequencies with a lower response to the
substance in the blood flow are then determined.
[0410] For example, the biosensor 100 may also determine alcohol
levels in the blood using wavelengths at approximately 390 nm
and/or 468 nm. For example, an R468,940 value (obtained from a
ratio of L468 nm and L940 nm) may be used as a liver enzyme
indicator, e.g. P450 enzyme indicator. The P450 liver enzyme is
generated in response to alcohol levels. Thus, the measurement of
the spectral response for the wavelength at approximately 468 nm
may be used to obtain blood alcohol levels from the concentration
levels of P450 and a calibration database.
[0411] In another aspect, the biosensor 100 may measure creatinine
levels using the PPG circuit by detecting a PPG signal with a
wavelength around 530 nm. Creatinine is produced by the kidneys and
various factors can affect the kidney production levels of
creatinine. The biosensor 100 may detect a high absorption
coefficient for creatinine, e.g. at 530 nm or in ranges +/-20
nm.
[0412] In another aspect, the biosensor 100 may detect various
electrolyte concentration levels or blood analyte levels, such as
bilirubin and sodium and potassium. In another aspect, the
biosensor 100 may detect sodium NACL concentration levels in the
arterial blood flow to determine dehydration.
[0413] In another aspect, the PPG sensor may detect white blood
cell counts to determine a risk of infection. For example, the
biosensor 100 may detect the various types of white blood cells
based on the spectral response of the wavelengths, e.g. using one
or more wavelengths shown in Table 1 below.
TABLE-US-00001 TABLE 1 White Blood Spectral Absorption Cell Type
Diameter Color Wavelengths Neutrophil 10-12 um Pink - Red, Red -
660 nm Blue, White Blue - 470 nm Green - 580 nm Eosinophil 10-12 um
Pink 660 nm, 470 nm, 580 nm Orange 600 nm Basophil 12-15 um Blue
470 nm Lymphocyte 7-15 um 633 nm Monocyte 15-30 um 580 nm
[0414] In yet another aspect, abnormal cells or proteins or
compounds that are present or have higher concentrations in the
blood with persons having cancer, may be detected using similar PPG
techniques described herein at one or more other wavelengths. Thus,
cancer risk may then be obtained through non-invasive testing by
the biosensor 100. Since the biosensor 100 may operate in multiple
frequencies, various health monitoring tests may be performed
concurrently.
[0415] In another aspect, the biosensor 100 may detect cholesterol
levels, such as LDL-Cholesterol, HDL-Cholesterol, and
Triglycerides. In a first embodiment, the biosensor 100 detects
cholesterol from PPG signals around a first wavelength with a high
absorption coefficient for cholesterol, such as 440 nm or 550 nm.
The wavelengths 440 nm and 550 nm may be used by the biosensor 100
to detect cholesterol as well as 468 nm. PPG signals around a
second wavelength with a lower absorption coefficient for
cholesterol are also obtained, such as 880 nm or 940 nm. Other
substances that may be obtained include bilirubin (using L460 nm)
and iron (using L510 nm, L651 nm, L300 nm) and potassium (using
L550 nm). In another embodiment, an R592,940 value (obtained from a
ratio of L592 nm and L940 nm) may be used as a digestive indicator
to measure digestive responses, such as phase 1 and phase 2
digestive stages.
[0416] Additional PPG signals are obtained at one or more
additional wavelengths having a different depth of penetration from
the first and second wavelengths, such as in a range of 510 nm-550
nm or below 660 nm at 5404.
[0417] At 5406, the PPG signal at a wavelength with a high
absorption coefficient for the substance is used to obtain a first
L value, and a PPG signal at a wavelength with a lower absorption
coefficient for the substance is used to obtain a second L value.
Additional L values may be obtained using the one or more other
wavelengths, such as using a PPG signal in a range of 510 nm-550
nm. The first and second L values are used to determine a first R
value. A second R value may be obtained using PPG signals at the
high absorption coefficient for the substance (or in a range of
+/-20 nm) and at 530 nm (or in a range of 510 nm-550 nm). A third R
value may be obtained using PPG signals obtained at the one or more
additional wavelengths, e.g. at 530 nm (or in a range of 510 nm-550
nm) and at 940 nm (or equal to or above 660 nm).
[0418] One or more other PPG parameters may be obtained using the
PPG signals at the plurality of wavelengths at 5408. For example,
other PPG parameters may include a measurement of a time or phase
delay between PPG signals with a high absorption coefficient for
the substance (or in a range of +/-20 nm) and at a low absorption
coefficient for the substance (or equal to or above 660 nm), a
measurement of correlation of phase shape between PPG signals with
a high absorption coefficient for the substance (or in a range of
+/-20 nm) and at a low absorption coefficient for the substance (or
equal to or above 660 nm), or a periodicity of a PPG signal with a
high absorption coefficient for the substance (or in a range of
+/-20 nm). Additional PPG parameters may include the diastolic and
systolic points, the pulse shape (measured by autoregression
coefficients and moving averages), characteristic features of the
shape of the PPG waveform, the average distance between pulses,
variance, instant energy information, energy variance, etc. Other
parameters may be extracted by representing the PPG signal as a
stochastic auto-regressive moving average (ARMA). Parameters also
may be extracted by modeling the energy of the PPG signal using the
Teager-Kaiser operator, calculating the heart rate and cardiac
synchrony of the PPG signal, or determining the zero crossings of
the PPG signal. These and other parameters may be obtained using
the PG signals.
[0419] Other health parameters may also be obtained at 5410, such
as a user's vitals (skin temperature, blood pressure, etc.) and
user data (such as age, pre-existing conditions, etc.).
[0420] A plurality of the parameters are processed at 5412 to
obtain a concentration level of the substance in blood flow at
5414. The plurality of parameters may be processed using an
artificial intelligence (AI) or machine learning technique, e.g.
using a regression model to determine the concentration levels.
Alternatively, the parameters may be processed using a customized
algorithm or processing models.
[0421] The plurality of the parameters may also be used to identify
an insulin release event, ET-1/NO efficacy balance, or
vasodilation/vasoconstriction level. In addition, the plurality of
parameters may also be analyzed to determine one or more health
indices described hereinbelow.
Embodiment--Health Indices
[0422] The biosensor 100 may determine one or more health indices
using the parameters described herein. Example indices are provided
below but additional or alternate indicators may be determined as
well. The health indices may include digital health parameters that
include discrete levels in numbers or letters. For example, the
health indices described herein may be within a numerical range
(e.g., 1-10) or a level (e.g., below, above, average, normal or
abnormal) or a letter grade (e.g., A, B, C).
[0423] Vascular Health Index--A vascular health index may be
determined by the biosensor 100 that indicates a vascular health of
a user. The vascular health index may be determined using one or
more of the parameters described herein including one or more of
the following: [0424] A relative vasoconstriction during an insulin
release event [0425] Arterial Stiffness [0426] Level of Insulin
Release
[0427] The measurement of relative vasoconstriction during an
insulin release event includes, e.g. a phase difference and/or
pulse shape differences between PPG signals, wherein the PPG
signals are measured at wavelengths with various depths of
penetration. Vasoconstriction is affected by the balance of ET-1
and NO as well as vascular disease such as atherosclerosis. By
measuring the relative vasoconstriction or change in vessel
diameter in response to insulin release, a vascular health index
may be assessed using the biosensor 100. The level of arterial
stiffness may be determined using the measurement of the diameter
change and level of insulin in the blood flow and comparing such
measurements to a general sampling of healthy persons without
vascular dysfunction. For example, a timing or period to change
from a state of vasodilation to normal width may be obtained using
phase differences between different wavelengths. The rate of change
may indicate vascular stiffness and a prediction of vascular
health. Circulation may also be considered when determining the
vascular health index. Alternative or additional measurements may
also be included in determining the vascular health index.
[0428] Pulse width and pulse area also correlate with the systemic
vascular resistance, which could reflect drug interaction of the
patient or blood viscosity. Another indicator of aortic stiffness
is the augmentation index, which is the ratio between the diastolic
peak and systolic peak. The time delay between the systolic and
diastolic peaks shortens with the subject's age and, given the
subject's height, provides an indicator for large artery stiffness.
Both of these indicators could inform a subject's vascular
health.
[0429] FIG. 55 illustrates a schematic flow diagram of an
embodiment of a method 5000 for determining a vascular health
index. At 5502, PPG signals are obtained at a plurality of
wavelengths, including a wavelength with a high absorption
coefficient for NO in a range of 380-410 nm, preferably 390-395 nm.
Additional PPG signals are obtained at one or more additional
wavelengths with a lower absorption coefficient for NO, such as in
a range of 510 nm-550 nm or equal to or above 660 nm (such as 940
nm).
[0430] Vascular health parameters are then determined using the PPG
signals at the plurality of wavelengths at 5504. For example, an
insulin release event is identified in the PPG signals, e.g. as
described with respect to FIG. 37. The insulin levels and the
relative change in diameter of vessels during the insulin release
event is obtained, as described with respect to FIG. 27 and FIG.
28. Other health parameters may also be obtained, such as a user's
vitals (skin temperature, blood pressure, etc.) and user data (such
as age, pre-existing conditions, etc.). The relative
vasoconstriction or change in vessel diameter in response to
insulin release event and level of insulin in the blood flow is
compared to such measurements in a sampling of healthy persons
without vascular dysfunction in a general population.
[0431] At 5506, the vascular health index is then determined. The
vascular health index may be a numerical range, percentage, letter
grade or other indicator that provides an indication of vascular
health of a user, e.g. compared to a in a sampling of healthy
persons without vascular dysfunction in a general population. The
vascular health index is then displayed or otherwise output at
5508.
[0432] Endothelial Dysfunction Index (or NO & ET-1 Peptide
Index)--The endothelial dysfunction index provides an indication of
the functioning or health of the endothelial layer of vessels. The
Endothelial Dysfunction Index (EDI) is a measurement of the level
of functioning of the endothelial layer. The EDI is an important
parameter in determining the overall health of a patient.
Endothelial Vascular dysfunction is a precursor or a symptom of
various conditions, including without limitation diabetes, renal
disease, sepsis, high blood pressure, sleep apnea, hearing loss,
heart failure, stroke, dementia, Alzheimer's disease, COPD and
sepsis. Since Endothelial Vascular dysfunction is either a
precursor or a symptom of various conditions, the EDI may provide
an early warning of such conditions.
[0433] FIG. 56 illustrates a schematic diagram of endothelial
dysfunction. The diagram is from the article entitled, "Shared
Features of Endothelial Dysfunction between Sepsis and Its
Preceding Risk Factors (Aging and Chronic Disease)", Bermejo-Martin
J F, Martin-Fernandez M, Lopez-Mestanza C, Duque P, Almansa R., J
Clin Med. 2018; 7(11):400, Published 2018 Oct. 30,
doi:10.3390/jcm7110400, incorporated by reference herein. The
diagram illustrates healthy endothelium at A. In A, the endothelial
cells are lining a blood vessel with normal blood circulation. The
diagram illustrates chronic endothelial dysfunction at B.
Endothelial cells are becoming disjointed with disassembly of cell
junctions. Endothelial NO is leaking from the vessels into the
tissue creating an increase in NO. The diagram further illustrates
acute or chronic endothelial dysfunction as occurs with sepsis at
C. In C, the disassembly of cell junctions is further aggravated
resulting in fluid leakage from the blood vessels. The level of NO
in acute dysfunction may now be double or triple the levels in
healthy endothelial of A.
[0434] FIG. 57 illustrates a schematic flow diagram of an
embodiment of a method 5200 for determining the endothelial
dysfunction index. At 5702, PPG signals are obtained at a plurality
of wavelengths, including a wavelength with a high absorption
coefficient for NO in a range of 380-410 nm, preferably 390-395 nm.
Additional PPG signals are obtained at one or more additional
wavelengths with a lower absorption coefficient for NO, such as in
a range of 510 nm-550 nm or equal to or above 660 nm (such as 940
nm).
[0435] Endothelial dysfunction parameters are then determined using
the PPG signals at the plurality of wavelengths at 5704. In an
embodiment, the phase delay, pulse shape correlation and R values
determined using PPG signals may be used to determine a balance of
ET-1 or NO in response to insulin in blood flow as described herein
with respect to FIG. 28. For example, the average or mean range of
one or more of these measurements in a healthy population is
measured. Then, an individual measurement is compared to the
average or mean range of one or more of phase delay, pulse shape
correlation and R values. The comparison may be used to determine a
level of balance between the effects of ET-1 and NO.
[0436] In another embodiment, a patient may be requested to fast
for 5-6 hours. A fasting NO level and glucose level is then
obtained. After ingestion or caloric intake, the NO level and
glucose level are again obtained and compared with the fasting
measurements.
[0437] Alternative or additional measurements may also be included
in determining the Endothelial Dysfunction Index. For example,
malondialdehyde (MDA) levels in a blood sample may be measured as
well as NO in plasma serum. The following blood measurements may
also provide information on vascular health: MRP: mid regional
proadrenomedullin, bioadrenomedullin, angiopoietin, syndecan-1.
Other blood measurements may include Endocan (ESM1), Intercellular
Adhesion Molecule 1 (ICAM-1), E-Selectin (SEL-E), P-Selectin
(SEL-P), Vascular Cell Adhesion Molecule 1 (VCAM-1) or
Thrombomodulin (THBD). In particular of the above, a blood
measurement of MRP provides a good basis for prediction of
Endothelium health. These measurements may provide a baseline for
testing of the Endothelial Dysfunction Index determined using PPG
signals. In another embodiment, one or more of these blood
measurements may also be included as additional parameters in
determining the Endothelial Dysfunction Index along with
measurements obtained using PPG signals.
[0438] At 5706, the endothelial dysfunction index is then
determined. The endothelial dysfunction index may be a numerical
range, percentage, letter grade or other indicator that provides an
indication of endothelial dysfunction of a user, e.g. compared to a
in a sampling of healthy persons without vascular dysfunction in a
general population. The endothelial dysfunction index is then
displayed or otherwise output at 5708.
[0439] Arterial stiffness (or Atherosclerosis) Index--The arterial
stiffness index provides an indication of a level or degree of
atherosclerosis. The arterial stiffness index may be determined,
e.g., by measuring the relative vasoconstriction or change in
arterial diameter during an insulin release event. The phase
difference between PPG signals during an insulin release event is
measured, wherein the PPG signals are obtained at wavelengths with
various depths of tissue penetration. A greater phase difference
between the PPG signals indicates that the circulation is varying
at the different depths of tissue. The vessels are not responding,
e.g. due to arterial stiffness. A normal range for phase
differences in the PPG signals may be measured using a general
population of healthy patients.
[0440] Alternative or additional measurements may also be included
in determining the arterial stiffness index. The arterial stiffness
index may be a numerical range, percentage, letter grade or other
indicator that provides an indication of arterial stiffness of a
user, e.g. compared to a sampling of healthy persons without
vascular dysfunction in a general population.
[0441] Insulin Release & Glucose Index--The biosensor may
identify a number and duration of insulin release events during a
time period and measure a glucose level over the time period as
described herein. The insulin release & Glucose index may be a
numerical range, percentage, letter grade or other indicator that
provides an indication of insulin release and glucose level
compared to a normal or average range of insulin release events and
glucose levels in a healthy population.
[0442] Heart Atrial Fibrillation Risk--Atrial Fibrillation is a
quivering or irregular heartbeat (arrhythmia) that can lead to
blood clots, stroke, heart failure and other heart-related
complications. The biosensor 100 may measure the pressure pulse
wave pattern (and so the cardiac cycle) using the PPG circuit. The
biosensor 100 may determine a heart rate variability and/or pulse
wave shape variability. Using these comparisons, the biosensor 100
may determine a risk of atrial fibrillation.
[0443] Sleep Quality Index--The biosensor 100 may measure heart
rate and pressure pulse wave pattern (and so the cardiac cycle)
during sleep or rest using the PPG circuit. In addition, the
biosensor 100 may measure respiration rate and oxygen saturation
during sleep or rest. Other factors such as sleep duration, sleep
disturbances, waking episodes, etc. may also be obtained. Using
these factors, the biosensor 100 may determine a sleep quality
index. Alternative or additional measurements may also be included
in determining the Sleep Quality Index.
[0444] Neural Activity--As described in U.S. patent application
Ser. No. 16/103,876, hereby incorporated by reference herein, the
biosensor 100 may detect movement of different body parts using
fluctuation patterns in PPG signals. Neural activation or stimulus
generates noise superimposed on the PPG signal. Neural activity may
thus be determined using autocorrelation patterns and S/N
ratio.
[0445] Hydration Index--The biosensor 100 may determine a hydration
index, e.g. by measuring a level of sodium in the blood stream. For
example, the PPG sensor may detect a sodium chloride (NACL)
concentration levels in the blood flow using a PPG signal at a
wavelength of 450 nm with a high absorption coefficient for NACL in
blood flow. Hydration decreases with increasing levels of NACL in
blood flow. The biosensor 100 may thus generate a hydration index
to indicate a level of hydration based on the measured NACL
levels.
[0446] Stress Index--The biosensor 100 may determine a stress index
based on one or more parameters, such as NO levels, heart rate,
variability of the pressure pulse wave, and/or S/N ratio of PPG
signals. Alternative or additional measurements may also be
included in determining the Stress Index.
[0447] Digestive Information and Reactive Calorie Intake
Estimation--The biosensor may determine a number and duration of
insulin release events during a period (such as 12 hours). The
number and duration of the insulin pulses may be correlated to
digestion and caloric intake. For example, integrals of the PPG
signals during insulin release events may be used to calibrate a
calorie count. The frequency or time between insulin release events
may be measured using the PPG signals to determine a stage of
digestion or a level of hunger. A time since ingestion of caloric
intake may also be estimated.
[0448] FIG. 58 illustrates a logical flow diagram of an embodiment
of a method for determining one or more health indices. At 5802,
PPG signals are obtained at one or more wavelengths. Health
parameters are determined at 5804. The health parameters may
include R values, L values, NO levels, glucose levels, insulin
level, insulin release events, ET-1/NO efficacy balance,
vasodilation/vasoconstriction level, levels of other analytes (such
as NACL), skin temperature, periodicity of PPG signals or other
parameters described herein. The health parameters are then used to
determine one or more health indices at 5806. In an embodiment, the
health parameters may be compared to average values or ranges of
similar parameters measured in a general healthy population. In
another embodiment, the health parameters may be compared to a
personal baseline of the user to obtain the health index.
[0449] An indication of the one or more health indices is then
provided at 5808. The health indices may include digital health
parameters, e.g. the health indexes expressed in a numerical or
alphabetical range or a level (such as above, below, average,
normal, abnormal, etc.). The biosensor 100 may thus determine one
or more health indices using the measurements described herein. The
indices described herein are exemplary and additional or alternate
indicators may be determined as well. The health indices may
provide an indication when further testing is needed of a user.
Embodiment--Diagnosis of a Health Condition
[0450] This multi-parameter approach may also be used to diagnose
other health conditions, such as kidney function, heart failure,
atrial fibrillation, other heart conditions, pneumonia, staph
infections, sepsis, other types of infections, respiratory
function, COPD, diabetes, Type I diabetes, or Type II diabetes. A
plurality of PPG parameters are input into a neural network or AI
classifier model that has been trained with data of patients
clinically diagnosed with the target health condition. The
plurality of parameters preferably includes a multiplicity of R
values each obtained using different wavelength ratios and a
multiplicity of L values obtained at different wavelengths.
[0451] FIG. 59 illustrates a logical flow diagram of an embodiment
of a method 5900 for determining a health condition using a
plurality of PPG parameters. At 5902, a first PPG signal is
obtained at a wavelength with a high absorption coefficient for a
substance related to the health condition and a second PPG signal
is obtained at a wavelength with a lower absorption coefficient for
the substance. For example, the substance related to the health
condition may be a substance that may help diagnose the health
condition. For example, creatinine is produced by the kidneys and
various factors can affect levels of creatinine in blood flow,
including kidney failure. The biosensor 100 may thus obtain a PPG
signal with a high absorption coefficient for creatinine, e.g. a
wavelength around 530 nm or in ranges+/-20 nm in step S902.
[0452] In another aspect, the biosensor 100 may determine PPG
signals having a high absorption coefficient for NO (e.g., a
wavelength of 395 nm or in ranges+/-20 nm of 395 nm) to help
diagnose sepsis or other infections, as well as diabetes, Type I
diabetes, or Type II diabetes or even PTSD. In another aspect, the
biosensor 100 may diagnose infections by detecting PPG signals at
wavelengths with high absorption coefficient for various types of
white blood cells shown in Table 1 above. In yet another aspect,
PPG signals at wavelengths with high absorption coefficient for
various types of abnormal cells or proteins or compounds that are
present or have higher concentrations in the blood with persons
having cancer, may be detected to aid in a cancer diagnosis.
[0453] In another aspect, PPG signals are detected at wavelengths
with high absorption coefficient for various types of cholesterol,
such as LDL-Cholesterol, HDL-Cholesterol, and Triglycerides to
determine normal or abnormal cholesterol levels. In another
example, PPG signals at wavelengths with high absorption
coefficient for iron (510 nm, 651 nm, 300 nm) are determined to
detect anemia in a patient. In another aspect, to detect heart
conditions or respiratory conditions (such as COPD), PPG signals
may be detected at wavelengths with high absorption coefficient for
oxygen, such as 660 nm or in ranges+/-20 nm thereof.
[0454] Additional PPG signals are obtained at one or more
additional wavelengths having a different depth of penetration from
the first and second wavelengths, such as in a range of 510 nm-550
nm or below 660 nm at 5904.
[0455] At 5906, the PPG signal at a first wavelength with a high
absorption coefficient for the related substance is used to obtain
a first L value, and a PPG signal at a second wavelength with a
lower absorption coefficient for the related substance is used to
obtain a second L value. Additional L values may be obtained using
the one or more other wavelengths, such as using a PPG signal in a
range between the first and second wavelength or other wavelengths
with a different penetration depth. The first and second L values
are used to determine a first R value. A second R value may be
obtained using PPG signals at the high absorption coefficient for
the substance (or in a range of +/-20 nm) and at a different
wavelength with a different penetration depth. A third R value may
be obtained using PPG signals obtained at the one or more
additional wavelengths.
[0456] One or more other PPG parameters may be obtained using the
PPG signals at the plurality of wavelengths at 5908. For example,
other PPG parameters may include a measurement of a time or phase
delay between PPG signals with a high absorption coefficient for
the substance (or in a range of +/-20 nm) and at a low absorption
coefficient for the substance (or equal to or above 660 nm), a
measurement of correlation of phase shape between PPG signals with
a high absorption coefficient for the substance (or in a range of
+/-20 nm) and at a low absorption coefficient for the substance (or
equal to or above 660 nm), or a periodicity of a PPG signal with a
high absorption coefficient for the substance (or in a range of
+/-20 nm). Additional PPG parameters may include the diastolic and
systolic points, the pulse shape (measured by autoregression
coefficients and moving averages), characteristic features of the
shape of the PPG waveform, the average distance between pulses,
variance, instant energy information, energy variance, etc. Other
parameters may be extracted by representing the PPG signal as a
stochastic auto-regressive moving average (ARMA). Parameters also
may be extracted by modeling the energy of the PPG signal using the
Teager-Kaiser operator, calculating the heart rate and cardiac
synchrony of the PPG signal, or determining the zero crossings of
the PPG signal. These and other parameters may be obtained using
the PG signals.
[0457] Other health parameters may also be obtained at 5910, such
as a user's vitals (skin temperature, blood pressure, etc.) and
user data (such as age, pre-existing conditions, gender, etc.).
[0458] A plurality of the parameters are processed at 5912 to
obtain a determination or diagnosis of the health condition or a
risk of the health condition. The plurality of parameters may be
processed using an artificial intelligence (AI) or machine learning
technique, e.g. using a classifier model to determine the
diagnosis. Alternatively, the parameters may be processed using a
customized algorithm or processing models.
Embodiment--Biosensor Configurations
[0459] The largest blood vessels are arteries and veins, which have
a thick, tough wall of connective tissue and many layers of smooth
muscle cells. The wall is lined by an exceedingly thin single sheet
of endothelial cells, the endothelium, separated from the
surrounding outer layers by a basal lamina. The inner layer (tunica
intima) is the thinnest layer, formed from a single continuous
layer of endothelial cells and supported by a subendothelial layer
of connective tissue and supportive cells.
[0460] Farther from the heart, where the surge of blood has
dampened, the percentage of elastic fibers in an artery's tunica
intima decreases and the amount of smooth muscle in its tunica
media increases. The artery at this point is described as a
muscular artery. The diameter of muscular arteries typically ranges
from 0.1 mm to 10 mm. Their thick tunica media allows muscular
arteries to play a leading role in vasoconstriction. In contrast,
their decreased quantity of elastic fibers limits their ability to
expand.
[0461] The radial artery and the proper digital artery to the index
finger are muscular arteries with greater smoother muscle cells.
Their thick tunica media allows these muscular arteries to play a
leading role in vasoconstriction. In contrast, their decreased
quantity of elastic fibers limits their ability to expand. The
radial artery extends to arterioles and capillaries in the
fingertip of the index finger. An arteriole is a small-diameter
blood vessel in the microcirculation that extends and branches out
from an artery and leads to capillaries. An arteriole is a very
small artery that leads to a capillary. Arterioles have the same
three tunics as the larger vessels, but the thickness of each is
greatly diminished. The critical endothelial lining of the tunica
intima is intact. The tunica media is restricted to one or two
smooth muscle cell layers in thickness. The tunica externa remains
but is very thin. The precise diameter of the lumen of an arteriole
at any given moment is determined by neural and chemical controls,
and vasoconstriction and vasodilation in the arterioles are the
primary mechanisms for distribution of blood flow.
[0462] Capillaries consist only of the thin endothelial layer of
cells with an associated thin layer of connective tissue. The
amounts of connective tissue and smooth muscle in the vessel wall
vary according to the vessel's diameter and function, but the
endothelial lining is always present. In the finest branches of the
vascular tree--the capillaries and sinusoids--the walls consist of
nothing but endothelial cells and a basal lamina, together with a
few scattered--but functionally important--pericytes. These are
cells of the connective-tissue family, related to vascular smooth
muscle cells, that wrap themselves round the small vessels.
Capillaries consist of a single layer of endothelium and associated
connective tissue without smooth muscle cells.
[0463] Due to the different vascular structure at different depths,
the use of an R value of 395 nm/530 nm wavelengths may be preferred
in obtaining results from tissues in a finger or other tissues
wherein vessels are closer to the surface. For example, in some
instances it may be preferred that wavelengths penetrate the tissue
at similar depths due to variations in the vascular profile at
different depths. The R value described herein may also be computed
using wavelengths with a low absorption coefficient for NO at 440
nm, 530 nm, 940 nm or another wavelength in the visible range or in
the IR range) and a wavelength with a high absorption coefficient
for NO (e.g., at 395 nm or in a range of +/-20 nm of 395 nm).
[0464] In addition, due to the different vascular structure at
different tissue sites, the biosensor 100 is preferably calibrated
for the type of tissue at a detection site. The same detection site
is preferably maintained throughout a measurement period because
vascular structure and dynamics varies between different tissue
sites. The variation may affect the calibration and relative
amplitude of the PPG signals.
[0465] The biosensor may be included in one or more different form
factors over various types of tissue, such as a watch, ring, patch,
earpiece, earbud, etc. In an embodiment, a small form factor such
as a ring or patch, may communicate via a wireless or wired
connection with a remote device, such as a watch, smart phone,
wrist band, computer, glasses, or other user device. The remote
device may include a PPG circuit and/or may include a processing
device for processing of PPG signals obtained by the remote
sensor.
[0466] In one or more aspects herein, a processing module or
circuit includes at least one processing device, such as a
microprocessor, micro-controller, digital signal processor,
microcomputer, neural network or AI processor, Quantum processor,
central processing unit, field programmable gate array,
programmable logic device, state machine, logic circuitry, analog
circuitry, digital circuitry, and/or any device that manipulates
signals (analog and/or digital) based on hard coding of the
circuitry and/or operational instructions. The processing circuit
further includes a memory device. The memory device is a
non-transitory memory and may be an internal memory or an external
memory, and the memory may be a single memory device or a plurality
of memory devices. The memory may be a read-only memory, random
access memory, volatile memory, non-volatile memory, static memory,
dynamic memory, flash memory, cache memory, and/or any
non-transitory memory device that stores digital information. The
processing device performs one or more of the functions described
herein in response to instructions stored in a memory device.
[0467] As may be used herein, the term "operable to" or
"configurable to" indicates that an element includes one or more of
circuits, instructions, modules, data, input(s), output(s), etc.,
to perform one or more of the described or necessary corresponding
functions and may further include inferred coupling to one or more
other items to perform the described or necessary corresponding
functions. As may also be used herein, the term(s) "coupled",
"coupled to", "connected to" and/or "connecting" or
"interconnecting" includes direct connection or link between
nodes/devices and/or indirect connection between nodes/devices via
an intervening item (e.g., an item includes, but is not limited to,
a component, an element, a circuit, a module, a node, device,
network element, etc.). As may further be used herein, inferred
connections (i.e., where one element is connected to another
element by inference) includes direct and indirect connection
between two items in the same manner as "connected to".
[0468] As may be used herein, the terms "substantially" and
"approximately" provides an industry-accepted tolerance for its
corresponding term and/or relativity between items. Such an
industry-accepted tolerance ranges from less than one percent to
fifty percent and corresponds to, but is not limited to,
frequencies, wavelengths, component values, integrated circuit
process variations, temperature variations, rise and fall times,
and/or thermal noise. Such relativity between items ranges from a
difference of a few percent to magnitude differences.
[0469] Note that the aspects of the present disclosure may be
described herein as a process that is depicted as a schematic, a
flowchart, a flow diagram, a structure diagram, or a block diagram.
Although a flowchart may describe the operations as a sequential
process, many of the operations can be performed in parallel or
concurrently. In addition, the order of the operations may be
re-arranged. A process is terminated when its operations are
completed. A process may correspond to a method, a function, a
procedure, a subroutine, a subprogram, etc. When a process
corresponds to a function, its termination corresponds to a return
of the function to the calling function or the main function.
[0470] The various features of the disclosure described herein can
be implemented in different systems and devices without departing
from the disclosure. It should be noted that the foregoing aspects
of the disclosure are merely examples and are not to be construed
as limiting the disclosure. The description of the aspects of the
present disclosure is intended to be illustrative, and not to limit
the scope of the claims. As such, the present teachings can be
readily applied to other types of apparatuses and many
alternatives, modifications, and variations will be apparent to
those skilled in the art.
[0471] In the foregoing specification, certain representative
aspects of the invention have been described with reference to
specific examples. Various modifications and changes may be made,
however, without departing from the scope of the present invention
as set forth in the claims. The specification and figures are
illustrative, rather than restrictive, and modifications are
intended to be included within the scope of the present invention.
Accordingly, the scope of the invention should be determined by the
claims and their legal equivalents rather than by merely the
examples described. For example, the components and/or elements
recited in any apparatus claims may be assembled or otherwise
operationally configured in a variety of permutations and are
accordingly not limited to the specific configuration recited in
the claims.
[0472] Furthermore, certain benefits, other advantages and
solutions to problems have been described above with regard to
particular embodiments; however, any benefit, advantage, solution
to a problem, or any element that may cause any particular benefit,
advantage, or solution to occur or to become more pronounced are
not to be construed as critical, required, or essential features or
components of any or all the claims.
[0473] As used herein, the terms "comprise," "comprises,"
"comprising," "having," "including," "includes" or any variation
thereof, are intended to reference a nonexclusive inclusion, such
that a process, method, article, composition or apparatus that
comprises a list of elements does not include only those elements
recited, but may also include other elements not expressly listed
or inherent to such process, method, article, composition, or
apparatus. Other combinations and/or modifications of the
above-described structures, arrangements, applications,
proportions, elements, materials, or components used in the
practice of the present invention, in addition to those not
specifically recited, may be varied or otherwise particularly
adapted to specific environments, manufacturing specifications,
design parameters, or other operating requirements without
departing from the general principles of the same.
[0474] Moreover, reference to an element in the singular is not
intended to mean "one and only one" unless specifically so stated,
but rather "one or more." Unless specifically stated otherwise, the
term "some" refers to one or more. All structural and functional
equivalents to the elements of the various aspects described
throughout this disclosure that are known or later come to be known
to those of ordinary skill in the art are expressly incorporated
herein by reference and are intended to be encompassed by the
claims. Moreover, nothing disclosed herein is intended to be
dedicated to the public regardless of whether such disclosure is
explicitly recited in the claims. No claim element is intended to
be construed under the provisions of 35 U.S.C. .sctn. 112(f) as a
"means-plus-function" type element, unless the element is expressly
recited using the phrase "means for" or, in the case of a method
claim, the element is recited using the phrase "step for."
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