U.S. patent application number 17/192743 was filed with the patent office on 2021-06-24 for system and method for screening and prediction of severity of infection.
The applicant listed for this patent is SANMINA CORPORATION. Invention is credited to Robert Steven Newberry, Matthew Rodencal.
Application Number | 20210186435 17/192743 |
Document ID | / |
Family ID | 1000005434545 |
Filed Date | 2021-06-24 |
United States Patent
Application |
20210186435 |
Kind Code |
A1 |
Newberry; Robert Steven ; et
al. |
June 24, 2021 |
SYSTEM AND METHOD FOR SCREENING AND PREDICTION OF SEVERITY OF
INFECTION
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
screen the user for an infection, such as sepsis, influenza and/or
COVID-19. The processing device may also determine a severity level
of the infection and a confidence level in the determination. The
parameters may include a measurement of nitric oxide (NO) level,
respiration rate, heart rate and/or oxygen saturation.
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: |
1000005434545 |
Appl. No.: |
17/192743 |
Filed: |
March 4, 2021 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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16848646 |
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10973470 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
A61B 2560/0223 20130101;
A61B 5/0002 20130101; A61B 5/6893 20130101; A61B 5/0022 20130101;
A61B 5/7275 20130101; A61B 5/1455 20130101; A61B 5/6817 20130101;
A61B 5/681 20130101; A61B 5/4845 20130101; A61B 5/14551 20130101;
A61B 5/6826 20130101; A61B 5/02416 20130101; A61B 5/14532 20130101;
A61B 5/743 20130101; A61B 5/01 20130101; A61B 5/7225 20130101; G16H
40/63 20180101 |
International
Class: |
A61B 5/00 20060101
A61B005/00; A61B 5/1455 20060101 A61B005/1455; A61B 5/145 20060101
A61B005/145; A61B 5/01 20060101 A61B005/01; G16H 40/63 20060101
G16H040/63; A61B 5/024 20060101 A61B005/024 |
Claims
1. A device, comprising: at least one memory device that stores at
least a first photoplethysmography (PPG) signal at a first
wavelength and a second PPG signal at a second wavelength, wherein
the first PPG signal and the second PPG signal are obtained from
light reflected from or transmitted through tissue of a user; at
least one processing circuit configured to: determine a first ratio
R value using the first PPG signal and the second PPG signal,
wherein the first ratio R value is obtained using an alternating
current (AC) signal component in the first spectral response and an
AC signal component in the second spectral response; and determine
a risk of sepsis in the user using the first ratio R value.
2. The device of claim 1, wherein the at least one processing
circuit is configured to determine a risk of sepsis in the user by:
determining a classification of either sepsis or no sepsis.
3. The device of claim 2, wherein the classification of either
sepsis or no sepsis includes a confidence level.
4. The device of claim 1, wherein the at least one processing
circuit is configured to determine a risk of sepsis in the user by:
determining a confidence level of no septic condition in the user
and a confidence level of a septic condition in the user.
5. The device of claim 1, wherein the at least one processing
circuit is further configured to: determine a heart rate and oxygen
saturation of the user using the first PPG signal or the second PPG
signal or a third PPG signal at a third wavelength obtained from
the user; and determine the risk for sepsis in the user using the
first ratio R value, the heart rate, and the oxygen saturation.
6. The device of claim 1, wherein the at least one processing
circuit is further configured to: obtain a temperature of the user;
and determine the risk for sepsis in the user using the first ratio
R value and the temperature of the user.
7. The device of claim 1, wherein the at least one processing
circuit is configured to determine a first ratio R value using the
first PPG signal and the second PPG signal by: determining a first
L value using the first PPG signal by isolating an alternating
current (AC) component of the first PPG signal; determining a
second L value using the second PPG signal by isolating an AC
component of the second PPG signal; and determining the first ratio
R value using the first L value and the second L value.
8. The device of claim 1, wherein the first wavelength is in a
range of 380 nm to 410 nm and wherein the second wavelength equals
or is above 660 nm.
9. The device of claim 1, wherein the at least one processing
circuit includes a processing circuit that includes artificial
intelligence (AI) or neural network processing models.
10. The device of claim 1, wherein the at least one processing
circuit is configured to: obtain one or more PPG parameters from
the first PPG signal and the second PPG signal, wherein the one or
more PPG parameters include 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,
a periodicity of first PPG signal or a periodicity of the second
PPG signal; and determine the risk for sepsis in the user using the
first ratio R value and the one or more PPG parameters.
11. A method, comprising: obtaining from a biosensor at least a
first PPG signal from light reflected from skin tissue of a
patient, wherein the light includes a first wavelength in an
ultraviolet (UV) range and at least a second PPG signal from light
reflected from skin tissue of the patient, wherein the light
includes a second wavelength in an infrared (IR) range; determining
by at least one processing device a first ratio R value using the
first PPG signal and the second PPG signal, wherein the first ratio
R value is obtained using alternating current (AC) signal
components in the first spectral response and the second spectral
response; and determining by the at least one processing device a
risk of sepsis in the user using the first ratio R value.
12. The method of claim 11, wherein determining a risk of sepsis in
the user comprises: determining by the at least one processing
device a classification of either sepsis or no sepsis.
13. The method of claim 12, wherein determining a classification of
either sepsis or no sepsis includes: determining by the at least
one processing device a confidence level of no septic condition in
the user and a confidence level of a septic condition in the
user.
14. The method of claim 11, further comprising: obtaining by the at
least one processing device a heart rate and an oxygen saturation
of the user; and determining by the at least one processing device
the risk for sepsis in the user using the first ratio R value, the
heart rate, and the oxygen saturation.
15. The method of claim 11, further comprising: obtaining by the at
least one processing device one or more PPG parameters from the
first PPG signal and the second PPG signal, wherein the one or more
PPG parameters include 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, a
periodicity of first PPG signal or a periodicity of the second PPG
signal; and determining by the at least one processing device the
risk for sepsis in the user using the first ratio R value and the
one or more PPG parameters.
16. The method of claim 11, wherein the first wavelength is in a
range of 380 nm to 410 nm and wherein the second wavelength equals
or is above 660 nm.
17. A device, comprising: at least one memory device that stores at
least a first photoplethysmography (PPG) signal at a first
wavelength and a second PPG signal at a second wavelength, wherein
the first PPG signal and the second PPG signal are obtained from
light reflected from or transmitted through tissue of a user; at
least one processing circuit configured to: determine a ratio R
value using the first PPG signal and the second PPG signal, wherein
the ratio R value is obtained using an alternating current (AC)
signal component in the first spectral response and an AC signal
component in the second spectral response; and determine a hybrid
quick Sequential Organ Failure Assessment (qSOFA) score using at
least the ratio R value.
18. The device of claim 17, wherein the hybrid quick Sequential
Organ Failure Assessment (qSOFA) score indicates a risk of sepsis
in the user.
19. The device of claim 18, wherein the at least one processing
circuit is further configured to: determine a heart rate and an
oxygen saturation from the first PPG signal or the second PPG
signal; and determine the hybrid quick Sequential Organ Failure
Assessment (qSOFA) score using at least the oxygen saturation, the
heart rate, and the ratio R value.
20. The device of claim 19, wherein the at least one processing
circuit is further configured to: determine an estimated blood
pressure from the first PPG signal or the second PPG signal; and
determine the hybrid quick Sequential Organ Failure Assessment
(qSOFA) score using at least the estimated blood pressure, the
oxygen saturation, the heart rate, and the ratio R value.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation of U.S. patent
application Ser. No. 16/848,646 entitled "SYSTEM AND METHOD FOR
SCREENING AND PREDICTION OF SEVERITY OF INFECTION" filed Apr. 14,
2020, and expressly incorporated by reference herein.
[0002] U.S. patent application Ser. No. 16/848,646 claims priority
under 35 U.S.C. .sctn. 120 as a continuation-in-part of U.S. patent
application Ser. No. 16/779,453 entitled "SYSTEM AND METHOD OF A
BIOSENSOR FOR DETECTION OF HEALTH PARAMETERS," filed Jan. 31, 2020,
to issue as U.S. Pat. No. 10,952,682 on Mar. 23, 2021, and
expressly incorporated by reference herein, which claims priority
under 35 U.S.C. .sctn. 119 to: [0003] 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 expressly
incorporated by reference herein.
[0004] U.S. patent application Ser. No. 16/848,646 claims priority
under 35 U.S.C. .sctn. 120 as a continuation-in-part of 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
expressly incorporated by reference herein,
[0005] U.S. patent application Ser. No. 16/848,646 claims priority
under 35 U.S.C. .sctn. 120 as a continuation-in-part of 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: [0006] 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; [0007] 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 [0008] 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.
[0009] U.S. patent application Ser. No. 16/848,646 claims priority
under 35 U.S.C. .sctn. 120 as a continuation-in-part of 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.
[0010] U.S. patent application Ser. No. 16/848,646 claims priority
under 35 U.S.C. .sctn. 120 as a continuation-in-part of 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:
[0011] 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.
[0012] U.S. patent application Ser. No. 16/848,646 claims priority
under 35 U.S.C. .sctn. 120 as a continuation-in-part of U.S. patent
application Ser. No. 16/208,358 entitled "VEHICLULAR HEALTH
MONITORING SYSTEM AND METHOD," filed Dec. 3, 2018 which claims
priority as a continuation to: [0013] U.S. patent application Ser.
No. 15/859,147 entitled "VEHICLULAR 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.
[0014] U.S. patent application Ser. No. 16/848,646 claims priority
under 35 U.S.C. .sctn. 120 as a continuation-in-part application to
U.S. patent 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: [0015] U.S. patent 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.
[0016] U.S. patent application Ser. No. 16/848,646 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: [0017] 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: [0018] U.S. patent 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: [0019] 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.
[0020] U.S. patent application Ser. No. 16/848,646 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.
[0021] U.S. patent application Ser. No. 16/848,646 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: [0022]
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: [0023]
U.S. patent 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: [0024] 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.
[0025] U.S. patent application Ser. No. 16/848,646 claims priority
under 35 U.S.C. .sctn. 120 as a continuation-in-part of 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: [0026]
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: [0027] 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: [0028] 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 claimed priority under 35 U.S.C. .sctn. 119 to: [0029]
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 [0030] 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.
[0031] U.S. patent application Ser. No. 16/848,646 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.
[0032] U.S. patent application Ser. No. 16/848,646 claims priority
under 35 U.S.C. .sctn. 120 as a continuation-in-part of 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: [0033]
U.S. patent application Ser. No. 14/866,500 entitled "SYSTEM AND
METHOD FOR GLUCOSE MONITORING," filed Sep. 25, 2015, now U.S. Pat.
No. 10,321,860 issued Jun. 18, 2019, and hereby expressly
incorporated by reference herein, which claims priority under 35
U.S.C. .sctn. 119(e) to: [0034] U.S. Provisional Application No.
62/194,264 entitled "SYSTEM AND METHOD FOR GLUCOSE MONITORING,"
filed Jul. 19, 2015, and both of which are hereby expressly
incorporated by reference herein.
FIELD
[0035] This application relates to a system and methods of
non-invasive, autonomous health monitoring, and in particular, a
system and method for health monitoring to detect a sepsis
condition in a patient.
BACKGROUND
[0036] Various invasive methods have been developed for measurement
of nitric oxide (NO) levels using one or more types of techniques
to remove cells from various types of bodily fluids. The methods
usually require drawing blood from a blood vessel using a needle
and syringe. The blood sample is then transported to a lab for
analysis to determine NO levels using physical or chemical
measurements. For example, in one current method, a blood sample is
inserted into a semi-permeable vessel including an NO reacting
substance that traps NO diffusing thereinto. A physical or chemical
detection method is then used to measure the levels of NO in the
blood sample.
[0037] These known in vitro measurements of NO levels have
disadvantages. The process of obtaining blood samples is time
consuming, inconvenient and painful to a patient. It may also
disrupt sleep of the patient. The measurements of the NO levels are
not continuous and may only be updated by taking another blood
sample.
[0038] One current non-invasive method is known for measuring
oxygen saturation in blood vessels 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 based on Beer-Lambert
law principles. 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, i.e. that due to arterial blood (AC components), from the
constant absorbance by nonpulsatile venous or capillary blood and
other tissue pigments (DC components), to eliminate the effect of
tissue absorbance to measure the oxygen saturation of arterial
blood.
[0039] A practical application of this technique is pulse oximetry,
which utilizes a noninvasive sensor to measure oxygen saturation
(SpO.sub.2) and pulse rate and can output representative
photoplethysmographic waveforms. Such PPG techniques have
heretofore been limited to determining oxygen saturation using
wavelengths in the infrared spectrum.
[0040] As such, there is a need for a patient monitoring system and
method that includes a non-invasive biosensor configured to monitor
concentration levels of nitric oxide (NO) in blood flow in vivo for
screening and predicting severity of an infection, such as sepsis
or viral infections.
SUMMARY
[0041] In one aspect, a device includes at least one memory device
that stores at least a first photoplethysmography (PPG) signal at a
first wavelength and a second PPG signal at a second wavelength,
wherein the first PPG signal and the second PPG signal are obtained
from light reflected from or transmitted through tissue of a user.
The device also includes at least one processing circuit configured
to determine a first ratio R value using the first PPG signal and
the second PPG signal, wherein the first ratio R value is obtained
using an alternating current (AC) signal component in the first
spectral response and an AC signal component in the second spectral
response and determine a risk of sepsis in the user using the first
ratio R value.
[0042] In another aspect, a method includes obtaining from a
biosensor at least a first PPG signal from light reflected from
skin tissue of a patient, wherein the light includes a first
wavelength in an ultraviolet (UV) range and at least a second PPG
signal from light reflected from skin tissue of the patient,
wherein the light includes a second wavelength in an infrared (IR)
range. The method further includes determining by at least one
processing device a first ratio R value using the first PPG signal
and the second PPG signal, wherein the first ratio R value is
obtained using alternating current (AC) signal components in the
first spectral response and the second spectral response; and
determining by the at least one processing device a risk of sepsis
in the user using the first ratio R value.
[0043] In another aspect, a device includes at least one memory
device that stores at least a first photoplethysmography (PPG)
signal at a first wavelength and a second PPG signal at a second
wavelength, wherein the first PPG signal and the second PPG signal
are obtained from light reflected from or transmitted through
tissue of a user. The device also includes at least one processing
circuit configured to determine a ratio R value using the first PPG
signal and the second PPG signal, wherein the ratio R value is
obtained using an alternating current (AC) signal component in the
first spectral response and an AC signal component in the second
spectral response and determine a hybrid quick Sequential Organ
Failure Assessment (qSOFA) score using at least the ratio R
value.
[0044] In one or more of the above aspects, the at least one
processing circuit is configured to determine a risk of sepsis in
the user by determining a classification of either sepsis or no
sepsis. The classification of either sepsis or no sepsis may also
include a confidence level. For example, a confidence level of no
septic condition in the user and a confidence level of a septic
condition in the user may be determined.
[0045] In one or more of the above aspects, the at least one
processing circuit is further configured to determine a heart rate
and oxygen saturation of the user using the first PPG signal or the
second PPG signal or a third PPG signal at a third wavelength
obtained from the user and determine the risk for sepsis in the
user using the first ratio R value, the heart rate, and the oxygen
saturation.
[0046] In one or more of the above aspects, the at least one
processing circuit is further configured to obtain a temperature of
the user and determine the risk for sepsis in the user using the
first ratio R value and the temperature of the user.
[0047] In one or more of the above aspects, the at least one
processing circuit is configured to determine a first ratio R value
using the first PPG signal and the second PPG signal by determining
a first L value using the first PPG signal by isolating an
alternating current (AC) component of the first PPG signal;
determining a second L value using the second PPG signal by
isolating an AC component of the second PPG signal; and determining
the first ratio R value using the first L value and the second L
value.
[0048] In one or more of the above aspects, the first wavelength is
in a range of 380 nm to 410 nm and wherein the second wavelength
equals or is above 660 nm.
[0049] In one or more of the above aspects, the at least one
processing circuit includes a processing circuit that includes
artificial intelligence (AI) or neural network processing
models.
[0050] In one or more of the above aspects, the at least one
processing circuit is configured to obtain one or more PPG
parameters from the first PPG signal and the second PPG signal,
wherein the one or more PPG parameters include 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, a periodicity of first PPG signal or a
periodicity of the second PPG signal; and determine the risk for
sepsis in the user using the first ratio R value and the one or
more PPG parameters.
[0051] In one or more of the above aspects, the plurality of L
values includes: a first L value determined using the first PPG
signal obtained at the first wavelength in a range of 380 nm-410
nm; and a second L value determined using a second PPG signal of
the plurality of additional PPG signals, wherein the second PPG
signal is obtained at a second wavelength equal to or above 660
nm.
[0052] In one or more of the above aspects, the hybrid quick
Sequential Organ Failure Assessment (qSOFA) score indicates a risk
of sepsis in the user. The at least one processing circuit is
further configured to determine a heart rate and an oxygen
saturation from the first PPG signal or the second PPG signal and
determine a hybrid quick Sequential Organ Failure Assessment
(qSOFA) score using at least the oxygen saturation, the heart rate,
and the ratio R value.
[0053] In one or more of the above aspects, the at least one
processing circuit is further configured to determine an estimated
blood pressure from the first PPG signal or the second PPG signal
and determine a hybrid quick Sequential Organ Failure Assessment
(qSOFA) score using at least the estimated blood pressure, the
oxygen saturation, the heart rate, and the ratio R value.
BRIEF DESCRIPTION OF THE DRAWINGS
[0054] FIG. 1 illustrates a schematic block diagram of exemplary
components in an embodiment of the biosensor.
[0055] FIG. 2 illustrates a logical flow diagram of an exemplary
embodiment of a method for detecting a risk of sepsis by the
biosensor.
[0056] FIG. 3 illustrates a graph of a measurement of NO levels for
a normal healthy patient without an infection.
[0057] FIG. 4 illustrates a schematic diagram of a graph of actual
clinical data obtained using an embodiment of the biosensor from a
patient with a diagnosis of sepsis.
[0058] FIG. 5 illustrates a logical flow diagram of an embodiment
of a method for use of the biosensor.
[0059] FIG. 6 illustrates a schematic block diagram illustrating an
embodiment of the PPG circuit in more detail.
[0060] FIG. 7 illustrates a logical flow diagram of an embodiment
of a method for determining a level of NO using Beer-Lambert
principles.
[0061] FIG. 8A illustrates a schematic block diagram of an
embodiment of a method for PPG techniques in more detail.
[0062] FIG. 8B illustrates a schematic block diagram of an
embodiment of a method for PPG techniques in more detail.
[0063] FIG. 9 illustrates a schematic diagram of a graph of actual
clinical data obtained using an embodiment of the biosensor 100 and
PPG techniques at a plurality of wavelengths.
[0064] FIG. 10 illustrates a logical flow diagram of an embodiment
of a method of the biosensor.
[0065] FIG. 11 illustrates a logical flow diagram of an exemplary
method to determine levels of nitric oxide (NO) using the spectral
response at a plurality of wavelengths.
[0066] FIG. 12 illustrates a logical flow diagram of an exemplary
method to determine levels of NO using the spectral response at a
plurality of wavelengths in more detail.
[0067] FIG. 13 illustrates a schematic block diagram of an
exemplary embodiment of a graph illustrating the extinction
coefficients over a range of frequencies for a plurality of
hemoglobin species.
[0068] FIG. 14 illustrates a schematic block diagram of an
exemplary embodiment of a graph illustrating a shift in absorbance
peaks of hemoglobin in the presence of NO.
[0069] FIG. 15 illustrates a schematic block diagram of an
exemplary embodiment of a graph illustrating a shift in absorbance
peaks of oxygenated and deoxygenated hemoglobin (HB) in the
presence of nitric oxide NO.
[0070] FIG. 16 illustrates a logical flow diagram of an exemplary
embodiment of a method for measuring NO concentration levels in
vivo using shifts in absorbance spectra.
[0071] FIG. 17 illustrates a logical flow diagram of an exemplary
embodiment of a method for measuring NO concentration levels using
one or more measurement techniques.
[0072] FIG. 18 illustrates a logical flow diagram of an embodiment
of a method for providing a health alert for sepsis by monitoring
NO measurements.
[0073] FIG. 19 illustrates a logical flow diagram of an embodiment
of a method for adjusting operation of the biosensor in response to
a position of the biosensor.
[0074] FIG. 20 illustrates a schematic drawing of an exemplary
embodiment of results of a filtered spectral response obtained
using an embodiment of the biosensor from a patient.
[0075] FIG. 21 illustrates a schematic drawing of an exemplary
embodiment of results of L values obtained over a time period.
[0076] FIG. 22 illustrates a schematic drawing of an exemplary
embodiment of results of averaged R values.
[0077] FIG. 23A illustrates a schematic drawing of an exemplary
embodiment of a calibration curve for correlating oxygen saturation
levels (SpO2) with R values.
[0078] FIG. 23B illustrates a schematic drawing of an exemplary
embodiment of a calibration curve for correlating for correlating
NO levels (mg/dl) with R values.
[0079] FIG. 24 illustrates a schematic block diagram of an
embodiment of a calibration database.
[0080] FIG. 25 illustrates a schematic block diagram of an
embodiment of predetermined thresholds of NO measurements for
detecting a risk of sepsis.
[0081] FIG. 26 illustrates a logical flow diagram of an embodiment
of a method for determining predetermined thresholds for health
alert indicators for sepsis.
[0082] FIG. 27 illustrates a graphical representation of an
embodiment of severity levels of an infection.
[0083] FIG. 28A illustrates a graphical representation of clinical
data of a sample patient over a four day time period.
[0084] FIG. 28B illustrates a graphical representation of clinical
data of the sample patient showing an expansion of a first period
in FIG. 28A.
[0085] FIG. 28C illustrates a graphical representation of clinical
data of the sample patient showing an expansion of a second period
in FIG. 28A.
[0086] FIG. 29A illustrates a graphical representations of clinical
data obtained from one of a plurality of patients in a second
clinical trial.
[0087] FIG. 29B illustrates a graphical representations of clinical
data obtained from one of a plurality of patients in a second
clinical trial.
[0088] FIG. 29C illustrates a graphical representations of clinical
data obtained from one of a plurality of patients in a second
clinical trial.
[0089] FIG. 29D illustrates a graphical representations of clinical
data obtained from one of a plurality of patients in a second
clinical trial.
[0090] FIG. 29E illustrates a graphical representations of clinical
data obtained from one of a plurality of patients in a second
clinical trial.
[0091] FIG. 29F illustrates a graphical representations of clinical
data obtained from one of a plurality of patients in a second
clinical trial.
[0092] FIG. 30 illustrates a graphical representation of clinical
data obtained from blood samples of the patients diagnosed with
sepsis during the second clinical trial.
[0093] FIG. 31 illustrates a graphical representation of
conclusions from data obtained during the second clinical
trial.
[0094] FIG. 32 illustrates a graphical representation of
conclusions from the second clinical trial.
[0095] FIG. 33 illustrates a schematic block diagram of an
embodiment of a method for screening for an infection by the
biosensor.
[0096] FIG. 34 illustrates a schematic block diagram of an
embodiment of an example graphical user interface (GUI) for
displaying data obtained from the biosensor.
[0097] FIG. 35 illustrates a schematic diagram of endothelial
dysfunction in a patient with sepsis.
[0098] FIG. 36 illustrates a graphical representation of NO levels
in patients with a flu-like illness and in COVID-19 patients at a
first time period.
[0099] FIG. 37 illustrates a graphical representation of NO levels
in patients with a flu-like illness and in COVID-19 patients at a
second subsequent time period.
[0100] FIG. 38 illustrates a graphical representation of
embodiments of methods of the biosensor 100 for screening and
monitoring COVID-19 patients.
[0101] FIG. 39 illustrates a graphical representation of a
plurality of parameters that may be analyzed to diagnose a patient
with an infection and/or determine a severity level of the
infection.
[0102] FIG. 40 illustrates a schematic block diagram of an
embodiment of a processing device for processing the one or more of
the plurality of input parameters.
[0103] FIG. 41 illustrates a logical flow diagram of an embodiment
of a method for using a machine learning or neural network
technique for detection of health data.
[0104] FIG. 42A illustrates a schematic block diagram of an
embodiment of a method for generating a hybrid qSOFA score by the
biosensor.
[0105] FIG. 42B illustrates a schematic block diagram of an
embodiment of a method for generating a hybrid SOFA score by the
biosensor.
[0106] FIG. 43A illustrates a perspective view of a disposable form
factor of the biosensor.
[0107] FIG. 43B illustrates a perspective view of internal
components of the biosensor.
[0108] FIG. 44 illustrates a perspective view of the biosensor
positioned on a finger of a patient.
[0109] FIG. 45A illustrates a first perspective view of a
non-disposable form factor of the biosensor.
[0110] FIG. 45B illustrates a second perspective views of a
non-disposable form factor of the biosensor.
[0111] FIG. 46A illustrates a perspective view of a top of a
biosensor implemented in a patch form factor.
[0112] FIG. 46B illustrates a perspective view of a back 4 of the
biosensor implemented in a patch form factor.
[0113] FIG. 47 illustrates a schematic block diagram of an
embodiment of the biosensor with another biomarker sensor
device.
DETAILED DESCRIPTION
[0114] 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.
[0115] 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.
[0116] Nitric oxide (NO) is produced by a group of enzymes called
nitric oxide synthases. These enzymes convert arginine into
citrulline, producing NO in the process. Oxygen and NADPH are
necessary co-factors. There are three isoforms of nitric oxide
synthase (NOS) named according to their activity or the tissue type
in which they were first described. The isoforms of NOS are neural
NOS (or nNOS, type 1), inducible NOS (or iNOS, type 2), and
endothelial NOS (or eNOS, type 3). These enzymes are also sometimes
referred to by number, so that nNOS is known as NOS1, iNOS is known
as NOS2, and eNOS is NOS3. Despite the names of the enzymes, all
three isoforms can be found in variety of tissues and cell types.
Two of the enzymes (nNOS and eNOS) are constitutively expressed in
mammalian cells and synthesize NO in response to increases in
intracellular calcium levels. In some cases, however, they are able
to increase NO production independently of calcium levels in
response to stimuli such as shear stress.
[0117] In most cases NO production increases in proportion to the
number of calories or food consumed. Normally this is derived from
the eNOS type NO production, and the body uses the NO first as a
vasodilator and also as a protective oxidation layer to prevent
undesired oxides from passing thru the cells in the blood vessels
walls. The amount of NO released in this case is measured in small
pulses and builds up as part of the normal digestion process. In
the case of type 1 or type 2 diabetics, the normal levels of eNOS
are abnormally low as found in recent clinical studies.
[0118] However, iNOS activity is independent of the level of
calcium in the cell, and all forms of the NOS isoforms are
dependent on the binding of calmodulin. Increases in cellular
calcium lead to increase in levels of calmodulin and the increased
binding of calmodulin to eNOS and nNOS leads to a transient
increase in NO production by these enzymes. By contrast iNOS is
able to bind tightly to calmodulin even at extremely low
concentrations of calcium. Therefore, iNOS activity does not
respond to changes in calcium levels in the cell. As a result of
the production of NO by iNOS, it lasts much longer than other forms
of isoforms of NOS and tends to produce much higher concentrations
of NO in the body. This is likely the reason that iNOS levels are
known to be elevated in dementia & Alzheimer's patents and have
increased calcium deposits in their brain tissue.
[0119] Inducible iNOS levels are highly connected with infections,
such as sepsis, which typically leads to large levels of NO in the
blood stream, which in turns leads to organ failure. Lastly
abnormal amounts of nNOS levels are typically associated with
issues with blood pressure regulation, neurotransmission issues and
penal erection. Thus, the overproduction or underproduction of NO
levels may be associated with many different health conditions.
These health conditions may be detected by measuring NO levels in
tissue and/or in the blood stream of a patient. The NO levels
include levels of one or more of: gaseous NO, nNOS levels and/or
other NO compounds, either measured as a relative level,
concentration in mmol/liter, percentage, etc.
Overview of Detection of Sepsis
[0120] The signs and symptoms of sepsis may be subtle. The
unacceptably low survival rate of severe sepsis indicates that
current patient diagnosis strategies are lacking in timeliness and
accuracy. SIRS (systemic inflammatory response syndrome) refers to
the systemic activation of the body's immune response. SIRS is
manifested by, for example, the presence of more than one of a
temperature greater than 38.degree. C. or less than 36.degree. C.;
a heart rate greater than 90 beats/min.; a respiration rate greater
than 20 breaths/min. or white blood count over 12,000 or less than
4,000. Prior definitions of sepsis included that two or more of the
SIRS symptoms are present with a confirmed or suspected infection.
Severe sepsis was defined as signs of end organ damage, hypotension
or blood tests confirming an elevated lactate level. For example,
factors in diagnosis of severe sepsis include elevated lactate,
creatinine greater than 2 mg/dL, Bilirubin greater than 2 mg/dL,
platelet count less than 100,000 and urine output less than 0.5
mL/kg/hr or more than 2 hours despite fluid resuscitation. Septic
shock ensues from severe sepsis and persistent low blood pressure
despite fluid resuscitation.
[0121] Other definitions of sepsis include a Sequential
[Sepsis-Related] Organ Failure Assessment (SOFA) score and a quick
SOFA (qSOFA) score. Under the qSOFA score, sepsis is diagnosed with
systolic blood pressure of less than 100 mmHg, an altered mental
status, and respiration rate greater than 22 breaths/min. The SOFA
score provides a score of 0-4 using, e.g., the following factors:
respiration/oxygen levels, coagulation platelets, liver bilirubin,
cardiovascular, CNS GCS score, renal creatinine level and urine
output. There is no definition for severe sepsis, and septic shock
is a subset of sepsis wherein needing vasopressors for a mean
arterial blood pressure (MAP) greater or equal to 65 mmHg or an
increase in lactate greater than 2 mmol/L despite adequate fluid
resuscitation. A table summarizing the SOFA score is shown
below.
TABLE-US-00001 TABLE 1 SOFA SCORE SCORE 0 1 2 3 4 Respiratory
.gtoreq.400 <400 <300 <200 with <100 with System
PaO.sub.2/FiO.sub.2 respiratory support respiratory support (mmHg)
Hepatic System <1.2 1.2-1.9 2.0-5.9 6.0-11.9 >12.0 Bilirubin
(mg/dL) Cardiovascular MAP .gtoreq.70 MAP <70 Dopamine <5 or
Dopamine 5.1 to 15 or Dopamine >15 or System mmHg mmHg
dobutamine epinephrine .ltoreq.0.1 or epinephrine >0.1 or (any
dose)* norepinephrine .ltoreq.0.1* norepinephrine >0.1*
Coagulation .gtoreq.150 <150 <100 <50 <20 Platelets
.times.10.sup.3/.mu.L Central Nervous 15 13-14 10-12 6-9 <6
System Glasgow coma scale Renal System <1.2 1.2-1.9 2.0-3.4
3.5-4.9 >5.0 Creatinine (mg/dL) Urine Output (mL/d) <500
<200 Notes: *All catecholamine doses represent .mu.g/kg/min.
Organ dysfunction is identified as an increase in the SOFA score of
.gtoreq.2 points. In patients with not known preexisting organ
dysfunction, the baseline SOFA score is assumed to be zero.
Intensive Care Med. The SOFA (Sepsis-related Organ Failure
Assessment) score to describe organ dysfunction/failure. On behalf
of the Working Group on Sepsis-Related Problems of the European
Society of Intensive Care Medicine. 22(7), 1996, 707-710, Vincent J
L, Moreno R, Takala J, et al. Abbreviations: PaO2--partial pressure
of oxygen; FiO2--fraction of inspired oxygen; MAP--mean arterial
pressure.
[0122] However, many of the parameters in the SOFA score and prior
definitions require blood samples and laboratory tests that may
take hours. Thus, the diagnosis and treatment of sepsis may be
delayed. Since sepsis has an 8% mortality rate compounded per hour
left untreated, the delay in diagnosis and treatment of sepsis may
affect a patient's outcome.
[0123] Moreover, conventional tests for sepsis give insufficient
advance warning of deteriorating patient health, e.g. from SIRS to
sepsis to septic shock. Many of the various parameters in Table 1
require blood tests, such as bilirubin levels and platelet levels.
For example, blood tests may include a complete blood count (CBC),
C-reactive protein (CRP), endotoxin, procalcitonin (PCT), blood
culture (to identify type of bacterial virus, or fungal infection)
and serum lactate levels. Urinalysis and urine cultures may also be
performed. A physician may also want to test for specific
infections, such as a chest x-ray for pneumonia, sputum test for an
infection in the throat or lungs, CT or MRI for meningitis, RT-PRC
for COVID-19, influenza tests, strep throat, etc. These types of
tests are invasive, non-continuous, costly, and time consuming.
Since sepsis is very dangerous and may escalate to life threatening
conditions quickly, this diagnosis process is not sufficient for
early warning of sepsis.
[0124] It has been shown that sepsis causes an increased amount of
nitric oxide (NO) to be released into the blood stream. The role of
nitric oxide in sepsis is described in the article entitled "Nitric
oxide in septic shock," by Michael A. Tiitheradge, Biochimica et
Biophysica Acta 1411 (1999) 437-455, which is hereby incorporated
by reference herein. As described in the article, a patient in
septic shock has hepatic glucose production that causes extreme
levels of lactate and amino acids. This in turn accelerates
production of Nitric Oxide or related Nitrate compounds to critical
levels within the body. The overproduction of NO during sepsis
induces excessive vascular relaxation and a profound hypotension
that is also a characteristic feature of sepsis.
[0125] In one or more embodiments herein, an early warning system
and method is described for early detection or prediction of
sepsis. A biosensor detects NO levels in vivo in the blood stream
of a patient. The biosensor includes an optical sensor circuit
configured to determine NO levels in blood vessels and/or
surrounding tissue of a patient. The biosensor may also detect
temperature as well as other vital signs indicative of sepsis, such
as pulse rate and respiration rate. The biosensor includes a
visible or audible indicator that signals detection of sepsis or a
risk of sepsis. The biosensor thus provides a noninvasive and
continuous monitoring tool for early warning of a patient's
condition and allows for more immediate medical intervention. The
patient may include any type of user, either animal or human. The
patient may or may not be under medical care or in a medical
facility. For example, the patient may be a user at home or at
work.
Embodiment of the Biosensor
[0126] In an embodiment, the biosensor includes an optical sensor
photoplethysmography (PPG) circuit configured to transmit light at
a plurality of wavelengths directed at skin tissue of a patient.
The patient may include any living organism, human or non-human.
The PPG circuit detects the light reflected from the skin tissue
and generates spectral responses at the plurality of wavelengths.
The processing circuit is configured to obtain a measurement of NO
levels from the spectral responses at the plurality of wavelengths
using one or more measurement techniques described herein.
[0127] FIG. 1 illustrates a schematic block diagram of exemplary
components in an embodiment of the biosensor 100. The biosensor 100
includes an optical or PPG circuit 110 as described in more detail
herein. The PPG circuit 110 may be configured to detect oxygen
saturation (SaO2 or SpO2) levels in blood flow, as well as heart
rate and respiration rate. In addition, the PPG circuit 110 is
configured to detect levels of NO in blood vessels and/or
surrounding tissue of a user as described in more detail
herein.
[0128] The biosensor 100 also includes one or more processing
circuits 202 communicatively coupled to a memory device 204. In one
aspect, the memory device 204 may include one or more
non-transitory processor readable memories that store instructions
which when executed by the one or more processing circuits 202 or
other components of the biosensor 100, causes the one or more
processing circuits 202 or other components to perform one or more
functions described herein. The processing circuit 202 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 or encasement. The biosensor 100 may be
battery operated and include a battery 210, such as a lithium ion
battery. In an embodiment, the battery 210 is disposable and
designed to include a short lifespan of 24-48 hours.
[0129] The biosensor 100 may also include a temperature sensor 214
configured to detect a temperature of a patient. For example, the
temperature sensor 214 may include an array of sensors (e.g.,
16.times.16 pixels) positioned on the biosensor 100 such that the
array of sensors are adjacent to the skin of the patient. The array
of sensors is configured to detect a temperature of the patient
from the skin. The temperature sensor 214 may also be used to
calibrate the PPG circuit 110, e.g. such as the LEDs in the PPG
circuit 110.
[0130] The biosensor 100 may also include a health alert indicator
220. The health alert indicator 220 may include one or more LEDs or
a display. When symptoms of sepsis are detected, the health alert
indicator may illuminate to provide a warning. For example, a first
LED may illuminate a first color (e.g. green) to indicate no or
little risk of sepsis has been detected while a second LED may
illuminate a second color (e.g. red) to indicate a risk of
sepsis.
[0131] FIG. 2 illustrates a logical flow diagram of an exemplary
embodiment of a method 300 for detecting sepsis by the biosensor
100. The biosensor 100 non-invasively obtains an NO measurement
related to the level of NO in blood vessels and/or surrounding
tissue at 202. An indication of the NO measurement may be displayed
at 204. For example, the patch 102 may include a row of LEDs that
are illuminated to indicate the level of the NO. Alternatively, the
patch 102 may include an LED configured to illuminate one or more
colors or hues to indicate the level of NO. In another aspect, a
display may display a concentration (mmol/liter) or relative level
of measured NO.
[0132] The NO measurement of the patient is compared to
predetermined levels at 306. For example, the predetermined
threshold may be based on a range of average or mean NO
measurements of a sample healthy population without a sepsis
condition. The NO measurement of an individual patient may then be
compared to the normal range derived from the sample healthy
population. Depending on the comparison, the NO measurement may be
determined within normal ranges. Alternatively, the NO measurement
may be determined to be higher than the predetermined normal
ranges. An indication of a health alert may then be displayed when
the NO measurement is indicative of a risk of sepsis at 308.
[0133] FIGS. 3 and 4 illustrate schematic diagrams of graphs of
actual clinical data obtained using an embodiment of the biosensor
100. FIG. 3 illustrates a graph 300 of a measurement of NO levels
for a normal healthy patient without an infection. The NO
measurement is obtained from a ratio R or R value 302. The R value
302 is obtained from a spectral response in the ultraviolet (UV)
range at 395 nm and a spectral response in the infrared (IR) range
at 940 nm.
[0134] In unexpected results, the UV range from 380 nm to 410 nm,
and in particular at 390 nm, has been determined to have a high
absorption coefficient for NO or NO compounds. The NO levels in
vivo in blood vessels may thus be measured without a need for a
blood sample or lab analytics. In this graph 400, the average R
value 402 for the healthy patient ranges from 2.6 to 2.4.
[0135] FIG. 4 illustrates a schematic diagram of a graph 404 of
actual clinical data obtained using an embodiment of the biosensor
100 from a patient with a diagnosis of sepsis. The graph 404
illustrates a measurement of NO levels for the patient with sepsis.
The NO measurement is obtained from a ratio R or R value 406. The R
value 406 is obtained from a spectral response in the UV range and
a spectral response in the IR range. In one aspect, the first
wavelength in the UV range is from 380-410 nm and in this example,
is from an LED with a wavelength of 395 nm. As seen in the graph, R
value 406 is around 30 for the patient with sepsis.
[0136] Nitric oxide (NO) is found in the blood stream in a gaseous
form and also bonded to a plurality of types of hemoglobin species.
The measured NO levels obtained using the UV range from 380-410
include measurements of NO in gaseous form as well as the NO bonded
to the plurality of types of hemoglobin species in the blood
vessels. The measured NO concentration levels may thus include NO
in various isoforms, in gaseous form or bonded to a plurality of
types of hemoglobin species. The NO measurement levels obtained as
described herein are thus more sensitive and have a greater dynamic
range than other methods for measuring NO levels based on a single
species of hemoglobin, such as methemoglobin (HbMet). The NO
measurements herein may also provide an earlier detection of
increases in NO in blood vessels than measurements based on HbMet
alone. In addition, the NO measurements may also extend to ranges
beyond hemoglobin saturation levels.
[0137] In one clinical trial, it was determined that the average R
value may range from 0.1 to 8 for a patient without a sepsis
condition. In addition, it was determined that an average R value
of 30 or higher is indicative of a patient with a sepsis condition
and that an average R value of 8-30 was indicative of a risk of
sepsis in the patient. In general, an R value of 2-3 times a
baseline R value was indicative of a risk of sepsis in the
patient.
[0138] FIG. 5 illustrates a logical flow diagram of an embodiment
of a method 500 for use of the biosensor 100. In this embodiment,
the biosensor 100 may include a disposable finger attachment or be
located in a disposable patch form factor. A new, unused patch 102
is attached to skin tissue of a patient at 502 or a finger of the
patient positioned in the disposable finger attachment. The
disposable patch may include an adhesive backing such that it may
adhere to a patient's skin. The patch may additionally or
alternatively be secured through other means, such as tape, band,
etc.
[0139] The biosensor 100 is activated at 504. The biosensor 100
non-invasively monitors an NO measurement related to the
concentration of NO in blood vessels at 506. The NO measurement of
the patient is compared to one or more predetermined thresholds.
For example, the predetermined thresholds may be derived based on
measurements of a sample healthy general population. A mean or
range of average values for the NO measurement from the sample
healthy population may then be used to set the predetermined
thresholds. The NO measurement of the patient may then be compared
to the predetermined thresholds derived from the sample healthy
population. In an embodiment, the mean or range of values of NO
levels in patients with sepsis may be obtained. For example,
patients diagnosed with sepsis using traditional methods may be
tested over days and weeks to determine a range of NO levels
indicating sepsis.
[0140] Within minutes of activation, the patch 102 may determine
the NO measurement and provide a health indicator at 508. Depending
on the comparison of the NO measurement to the one or more
predetermined thresholds, the health indicator may signal that the
NO measurement is within predetermined normal ranges.
Alternatively, the health indicator may signal that the NO
measurement is not within than the predetermined thresholds, e.g.
outside normal ranges or in a range indicative of sepsis. The
health indicator then provides a warning or alert of a risk of
sepsis.
[0141] To lower costs, the health indicator may include one or more
LEDs on the patch 102. For example, the patch 102 may include a row
of LEDs that are illuminated to indicate the level of the NO
concentration. Alternatively, the patch 102 may include an LED
configured to illuminate in one or more colors or hues to indicate
the level of NO concentration, a first color to indicate normal
ranges and a second color to indicate not within normal ranges. In
another embodiment, the patch 102 may include a display that
provides a visual indication of the NO measurement.
[0142] When monitoring of the single patient is complete, the
disposable finger attachment is removed and disposed. A new
disposable finger attachment is then obtained and used with a next
patient. The disposable finger attachment is thus designed for use
with a single patient.
[0143] In an embodiment with a disposable patch, the disposable
patch including the biosensor 100 is disposed of. The disposable
patch is thus designed and manufactured for a single use on a
single patient for a short duration of time, e.g. 24-48 hours. The
disposable patch form factor 102 has several advantages including a
low cost (such as under $10). The patch 102 is easy to use with a
simple visible indicator. The patch may be sold for hospital or
home use to provide a health indicator within minutes. For example,
the patch 102 may be used in triage at hospitals or clinics, or the
patch 102 may be used at home to monitor an at risk patient to
determine a possible infection or risk of sepsis.
Embodiment--PPG Circuit
[0144] FIG. 6 illustrates a schematic block diagram illustrating an
embodiment of the PPG circuit 110 in more detail. The PPG circuit
110 includes a light source 620 configured to emit a plurality of
wavelengths of light across various spectrums. For example, the
light source 620 mat include a plurality of LEDs 622a-n. The PPG
circuit 110 is configured to direct the emitted light at an outer
or epidermal layer of skin tissue of a patient through at least one
aperture 628a. The plurality of LEDs 622a-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 618. For example, the biosensor 100 may include a
first LED 622a that emits visible light and a second LED 622b that
emits infrared light and a third LED 622c that emits UV light, etc.
In another embodiment, one or more of the light sources 622a-n 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 618.
[0145] In an embodiment, the driver circuit 618 is configured to
control the one or more LEDs 622a-n to generate light at one or
more frequencies for predetermined periods of time. The driver
circuit 618 may control the LEDs 622a-n to operate concurrently or
consecutively. The driver circuit 618 is configured to control a
power level, emission period and frequency of emission of the LEDs
622a-n. 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 patient.
[0146] The PPG circuit 110 further includes one or more
photodetector circuits 630a-n. For example, a first photodetector
circuit 630 may be configured to detect visible light and the
second photodetector circuit 630 may be configured to detect IR
light. Alternatively, both photodetectors 630a-n may be configured
to detect light across multiple spectrums and the signals obtained
from the photodetectors are added or averaged. The first
photodetector circuit 630 and the second photodetector circuit 630
may also include a first filter 660 and a second filter 662
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
patient is desired to pass through the filters. The first
photodetector circuit 630 and the second photodetector circuit 632
are coupled to a first A/D circuit 638 and a second A/D circuit
640. Alternatively, a single A/D circuit may be coupled to each of
the photodetector circuits 630a-n.
[0147] In another embodiment, a single photodetector circuit 630
may be implemented operable to detect light over multiple spectrums
or frequency ranges. The one or more photodetector circuits 630
include one or more types of spectrometers or photodiodes or other
type of circuit configured to detect an intensity of light as a
function of wavelength to obtain a spectral response. In use, the
one or more photodetector circuits 630 detect the intensity of
light reflected from skin tissue of a patient that enters one or
more apertures 628b-n of the biosensor 100. In another example, the
one or more photodetector circuits 630 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 630a-n then obtain a spectral response of
the reflected or transmissive light by measuring an intensity of
the light at one or more wavelengths.
[0148] In another embodiment, the light source 620 may include a
broad spectrum light source, such as a white light to infrared (IR)
or near IR LED 622, that emits light with wavelengths from e.g. 350
nm to 2500 nm. Broad spectrum light sources 620 with different
ranges may be implemented. In an aspect, a broad spectrum light
source 620 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 620 for
spectroscopy may be used. The spectral response of the reflected
light 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 630 to measure the spectral response of the
detected light over the broad spectrum.
Embodiment--Measurement of NO Levels
[0149] One or more of the embodiments of the biosensor 100
described herein is configured to detect a level of NO within blood
flow and/or surrounding tissue using photoplethysmography (PPG)
techniques. The biosensor 100 may detect NO levels as well as
peripheral oxygen (SpO.sub.2 or SaO.sub.2) saturation,
concentration of one or more other substances as well as patient
vitals, such as pulse rate and respiration rate.
[0150] In use, the biosensor 100 performs PPG techniques using the
PPG circuit 110 to detect the levels of one or more substances in
blood flow and/or surrounding tissue. In one aspect, the biosensor
100 receives reflected light from skin tissue to obtain a spectral
response. The spectral response 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. The ratio of
the resonance absorption peaks from two different frequencies is
calculated and based on the Beer-Lambert law used to obtain the
levels of substances in the blood flow.
[0151] First, the spectral response of a substance or substances in
the 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 l (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 using the following equations:
At the first wavelength .lamda..sub.1,
I.sub.1=I.sub.in1*10.sup.-(a.sup.g1.sup.c.sup.gw.sup.+.alpha..sup.w1.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.w-
2.sup.c.sup.w.sup.)*l
wherein: [0152] I.sub.in1 is the intensity of the initial light at
.lamda..sub.1 [0153] I.sub.in2 is the intensity of the initial
light at .lamda..sub.2 [0154] .alpha..sub.g1 is the absorption
coefficient of the substance in arterial blood at .lamda..sub.1
[0155] .alpha..sub.g2 is the absorption coefficient of the
substance in arterial blood at .lamda..sub.2 [0156] .alpha..sub.w1
is the absorption coefficient of arterial blood at .lamda..sub.1
[0157] .alpha..sub.w2 is the absorption coefficient of arterial
blood at .lamda..sub.2 [0158] C.sub.gw is the concentration of the
substance and arterial blood [0159] C.sub.w is the concentration of
arterial blood
[0160] Then letting R equal:
R = log 10 ( I 1 Iin 1 ) log 10 ( I 2 Iin 2 ) ##EQU00001##
[0161] The concentration of the substance Cg may then be equal
to:
C g = C g w C g w + C w = .alpha. w 2 R - .alpha. w 1 ( .alpha. w 2
- .alpha. g w 2 ) * R - ( .alpha. w 1 - .alpha. gw 1 )
##EQU00002##
[0162] The biosensor 100 may thus determine the concentration of
various substances in arterial blood flow from the Beer-Lambert
principles using the spectral responses of at least two different
wavelengths. These calculations may be modified to determine
concentration in venous blood flow as well as arterial blood flow
and/or surrounding tissue.
[0163] FIG. 7 illustrates a logical flow diagram of an embodiment
of a method 700 for determining level of NO using Beer-Lambert
principles. The biosensor 100 transmits light at least at a first
predetermined wavelength and at a second predetermined wavelength.
The biosensor 100 detects the light (reflected from the skin tissue
or transmitted through the skin tissue) and determines the spectral
response at the first wavelength at 702 and at the second
wavelength at 704. The biosensor 100 then determines an indicator
or level of NO using the spectral responses of the first and second
wavelength at 706. In general, the first predetermined wavelength
is selected that has a high absorption coefficient for NO and/or NO
compounds in blood flow while the second predetermined wavelength
is selected that has a lower absorption coefficient for NO and/or
NO compounds 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 NO and/or NO compounds in
blood flow than the spectral response for the second predetermined
wavelength. In an embodiment, the first predetermined wavelength is
in a range of 380-410 nm and in particular at 390 nm or 395 nm.
[0164] In another aspect, the biosensor 100 may transmit light in a
range of approximately 1 nm to 50 nm around the first predetermined
wavelength. Similarly, the biosensor 100 may transmit light 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.
[0165] The first spectral response of the light over the first
range of wavelengths including the first predetermined wavelength
and the second spectral response of the light over the second range
of wavelengths including the second predetermined wavelengths is
then generated at 702 and 704. The biosensor 100 analyzes the first
and second spectral responses to detect an indicator or level of NO
in the blood flow and/or surrounding tissue at 706.
[0166] FIG. 8A and FIG. 8B illustrate schematic block diagrams of
an embodiment of a method for photoplethysmography (PPG) techniques
in more detail. 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 arterial blood flow
and the concentration or absorption levels of substances being
measured in the arterial blood flow. As shown in FIG. 8A, over a
cardiac cycle 802, pulsating arterial blood 804 changes the volume
of blood flow in an artery.
[0167] Incident light I.sub.O 812 is directed at a tissue site and
a certain amount of light is reflected or transmitted 818 and a
certain amount of light is absorbed 820. At a peak of arterial
blood flow or arterial volume, the reflected/transmitted light
I.sub.L 814 is at a minimum due to absorption by the venous blood
808, nonpulsating arterial blood 806, pulsating arterial blood 804,
other tissue 810, etc. At a minimum of arterial blood flow or
arterial volume during the cardiac cycle, the transmitted/reflected
light I.sub.H 816 is at a maximum due to lack of absorption from
the pulsating arterial blood 804.
[0168] The biosensor 100 is configured to filter the
reflected/transmitted light I.sub.L 814 of the pulsating arterial
blood 804 from the transmitted/reflected light I.sub.H 816. This
filtering isolates the light due to reflection/transmission of
substances in the pulsating arterial blood 804 from the light due
to reflection/transmission from venous (or capillary) blood 808,
other tissues 810, etc. The biosensor 100 may then measure the
levels of one or more substances from the reflected/transmitted
light I.sub.L 814 in the pulsating arterial blood flow 804.
[0169] For example, as shown in FIG. 8B, incident light I.sub.O 812
is directed at a tissue site by an LED 122 at one or more
wavelengths. The reflected/transmitted light I 818 is detected by
photodetector 130. At a peak of arterial blood flow or arterial
volume, the reflected light I.sub.L 814 is at a minimum due to
absorption by venous blood 808, non-pulsating arterial blood 806,
pulsating arterial blood 804, other tissue 810, etc. At a minimum
of arterial blood flow or arterial volume during the cardiac cycle,
the Incident or reflected light I.sub.H 816 is at a maximum due to
lack of absorption from the pulsating arterial blood 804. Since the
light I 818 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 patient's arteriolar bed at different times. Though the
above has been described with respect to arterial blood flow, the
same principles described herein may be applied to venous blood
flow.
[0170] In general, the relative magnitudes of the AC and DC
contributions to the reflected/transmitted light signal I 818 may
be used to substantially determine the differences between the
diastolic points and the systolic points. In this case, the
difference between the reflected light I.sub.L 814 and reflected
light I.sub.H 816 corresponds to the AC contribution of the
reflected light 818 (e.g. due to the pulsating arterial 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 818 to determine the magnitude of the reflected light
I.sub.L 814 due to the pulsating arterial blood 804. 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 814
due to pulsating arterial blood flow.
[0171] FIG. 9 illustrates a schematic diagram of a graph of actual
clinical data obtained using an embodiment of the biosensor 100 and
PPG techniques at a plurality of wavelengths. In one aspect, the
biosensor 100 is configured to emit light having a plurality of
wavelengths during a measurement period. The light at each
wavelength (or range of wavelengths) may be transmitted
concurrently or sequentially. The intensity of the reflected light
at each of the wavelengths (or range of wavelengths) is detected
and the spectral response is measured over the measurement period.
The spectral response 908 for the plurality of wavelengths obtained
using an embodiment of the biosensor in clinical trials is shown in
FIG. 9. In this clinical trial, two biosensors 100 attached to two
separate fingertips of a patient were used to obtain the spectral
responses 908. The first biosensor 100 obtained the spectral
response for a wavelength at 940 nm 610, a wavelength at 660 nm 612
and a wavelength at 390 nm 614. The second biosensor 100 obtained
the spectral response for a wavelength at 940 nm 616, a wavelength
at 592 nm 618 and a wavelength at 468 nm 620.
[0172] In one aspect, the spectral response of each wavelength may
be aligned based on the systolic 602 and diastolic 604 points in
their 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
may mimic the cardiac cycle 906 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 906 associated with the local
pressure wave within the patient'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
902 and diastolic points 904 in the spectral response are
determined. These systolic points 902 and diastolic points 904 for
the one or more wavelengths may then be aligned as a method to
discern concurrent responses across the one or more
wavelengths.
[0173] In another embodiment, the systolic points 902 and diastolic
points 904 in the absorbance measurements are temporally correlated
to the pulse-driven pressure wave within the arterial 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. 9
illustrates the spectral response of the plurality of wavelengths
with the systolic points 902 and diastolic points 904 aligned.
[0174] FIG. 10 illustrates a logical flow diagram of an embodiment
of a method 1000 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. 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 patient 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 patient 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).
[0175] The spectral responses are obtained around the plurality of
wavelengths, including at least a first wavelength and a second
wavelength at 1002. The spectral responses may be measured over a
predetermined period (such as 300 usec.). 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 absorption levels are measured
over one or more cardiac cycles and systolic and diastolic points
of the spectral response are 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.
[0176] A low pass filter (such as a 5 Hz low pass filter) is
applied to the spectral response signal at 1004. The relative
contributions of the AC and DC components are obtained I.sub.AC+DC
and I.sub.ACA peak detection algorithm is applied to determine the
systolic and diastolic points at 1006. The systolic and diastolic
points of the spectral response for each of the wavelengths may be
aligned and may also be aligned with systolic and diastolic points
of an arterial pulse waveform or cardiac cycle.
[0177] Beer Lambert equations are then applied as described herein
at 1008. For example, the L.sub..lamda. values are then calculated
for the wavelengths .lamda., wherein the L.sub..lamda. values for a
wavelength equals:
L .lamda. = Log 10 ( IAC + D C IDC ) ##EQU00003##
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 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 or L .lamda. = IAC + D C IDC ##EQU00004##
[0178] A ratio R of the L.sub..lamda. values at two wavelengths may
then be determined. For example, the ratio R may be obtained from
the following:
Ratio R = L .lamda. 1 L .lamda. 2 ##EQU00005##
[0179] 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
level of a substance may then be obtained from the R value. The
biosensor 100 may substantially continuously monitor a user over
2-3 hours or over days or weeks.
[0180] The R.sub.390,940 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 in a physician's office or other
clinical setting or at home. In particular, in unexpected results,
it is believed that nitric oxide NO levels in the arterial 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 levels in
arterial 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 NO Levels at a Plurality of
Wavelengths
[0181] FIG. 11 illustrates a logical flow diagram of an exemplary
method 1100 to determine levels of NO using the spectral response
at a plurality of wavelengths. The absorption coefficient may be
higher at other wavelengths due to NO or NO isoforms or NO
compounds. For example, the increased intensity of light at a
plurality of wavelengths may be due to reflectance by NO or NO
isoforms or other NO compounds in the arterial blood flow. Another
method for determining NO levels may then be used by measuring the
spectral response and determining L and R values at a plurality of
different wavelengths of light. In this example then, NO level is
determined over multiple wavelengths. An example for calculating
the concentration of one or more substances 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 [0182] wherein, [0183]
I.sub.1-n=intensity of light at wavelengths .lamda..sub.1-n [0184]
.mu..sub.n=absorption coefficient of substance 1, 2, . . . n at
wavelengths .lamda..sub.1-n [0185] C.sub.n=Concentration level of
substance 1, 2, . . . n When the absorption coefficients
.mu..sub.1-n of NO or NOS isoforms or other NO compounds are known
at the wavelengths .lamda..sub.1-n, then the concentration level C
of the substances may be determined from the spectral responses at
the wavelengths .lamda..sub.1-n (and e.g., including a range of 1
nm to 50 nm around each of the wavelengths). The concentration
level of NO may be isolated from the NOS isoforms or other NO
compounds by compensating for the concentration of the hemoglobin
compounds. Thus, using the spectral responses at multiple
frequencies provides a more robust determination of the
concentration level of NO.
[0186] In use, the biosensor 100 transmits light directed at skin
tissue at a plurality of wavelengths or over a broad spectrum at
1102. The spectral response of light from the skin tissue is
detected at 1104, and the spectral response is analyzed for a
plurality of wavelengths (and in one aspect including a range of
+/-10 to 50 nm around each of the wavelengths) at 1106. Then, the
concentration level C of the substance may be determined using the
spectral response at the plurality of wavelengths at 1108.
[0187] FIG. 12 illustrates a logical flow diagram of an exemplary
method 1200 to determine levels of NO using the spectral response
at a plurality of wavelengths in more detail. The spectral
responses are obtained at 1202. 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 1204. The AC fluctuation
is due to the pulsatile expansion of the arteriolar bed due to the
volume increase in arterial 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 1206. Fast Fourier
transform (FFT) or differential absorption techniques may also be
used to isolate the DC component of each spectral response signal.
The various methods 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.
[0188] The I.sub.AC+DC and I.sub.DC components are then used to
compute the L values at 1210. 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).
[0189] In an embodiment, NO isoforms 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 NO 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 as described herein. Thus, the spectral responses
obtained around 390 nm may include a level of the hemoglobin
compounds as well as nitric oxide. The hemoglobin compound levels
must thus be compensated for to isolate the nitric oxide levels.
Multiple wavelengths and absorption coefficients for hemoglobin are
used to determine a concentration of the hemoglobin compounds at
1214. This process is discussed in more detail herein below. Other
methods may also be used to obtain a level of hemoglobin in the
arterial blood flow as explained herein. The concentration of the
hemoglobin compounds is then adjusted from the measurements to
determine the level of NO at 1216. The R values are then determined
at 1218.
[0190] To determine a level of NO, a calibration database is used
that associates R values to levels of NO at 1220. The calibration
database correlates the R value with an NO level. The calibration
database may be generated for a specific patient or may be
generated from clinical data of a large sample population. It is
determined that the R values should correlate to similar NO levels
across a large sample population. Thus, the calibration database
may be generated from testing of a large sample of a general
population.
[0191] 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 NO levels depending
on the underlying skin tissue characteristics.
[0192] The NO level is then obtained at 1224. The NO level may be
expressed as mmol/liter, as a saturation level percentage, as a
relative level on a scale, etc. In order to remove the hemoglobin
concentration(s) from the original PPG signals, a mapping function
may be created which is constructed through clinical data and
tissue modeling. For example, known SpO.sub.2 values in the
infrared region and the same signals at the UV side of the spectrum
are obtained. Then a linear inversion map can be constructed where
the R values are input into a function and the desired
concentration(s) can be determined. For example, a curve that
correlates R values to levels may be tabulated. A polynomial
equation with multiple factors can also be used to account for
different R values to represent the linear inversion map. This
correlation may be derived from validated clinical data.
[0193] For example, a regression curve that correlates R values and
NO levels may be generated based on clinical data from a large
general population. A polynomial may be derived from the curve and
used to solve for an NO level from the R value. The polynomial is
stored in the calibration database and may be used rather than
using a calibration look-up table or curve.
Embodiment--Determination of a Concentration of Hemoglobin
Compounds
[0194] The Beer-Lambert theory may be generalized for a
multi-wavelength system to determine a concentration of known
hemoglobin species using the following matrix notation:
[ dA .lamda. 1 LB dA .lamda. n LB ] = [ .DELTA. l .lamda. 1 0 0
.DELTA. l .lamda. n ] [ .lamda. 1 , HbX 1 .lamda. 1 , HbX m .lamda.
n , Hb X 1 .lamda. n , HbX m ] [ HbX 1 HbX m ] c ( Hb ) ,
##EQU00006##
wherein [0195] dA.sub..lamda..sup.LB is a differential absorption
within the Beer-Lambert model [0196] .epsilon..sub..lamda.n1,HbX1
is an extinction coefficient [0197] HbX are hemoglobin fractions
[0198] .DELTA.1.lamda. is the optical path-length for wavelength A
[0199] c(Hb) is the hemoglobin concentration This Beer-Lambert
matrix equation for determining hemoglobin levels may be solved
when m is equal or greater than n, e.g., which means that at least
four wavelengths are needed to solve for four hemoglobin species.
The spectral responses at these four wavelengths may be analyzed to
determine the concentration of the plurality of hemoglobin
species.
[0200] FIG. 13 illustrates a schematic block diagram of an
exemplary embodiment of a graph 1300 illustrating the extinction
coefficients over a range of frequencies for a plurality of
hemoglobin species. The hemoglobin species include, e.g.,
Oxyhemoglobin [HbO.sub.2 or OxyHb] 1302, Carboxyhemoglobin [HbCO or
CarboxyHb] 1304, Methemoglobin [HbMet or MetHb] 1306, and
deoxygenated hemoglobin (DeoxyHb or RHb) 1308. A method for
determining the relative concentration or composition of hemoglobin
species included in blood is described in more detail in U.S. Pat.
No. 6,104,938 issued on Aug. 15, 2000, which is hereby incorporated
by reference herein.
[0201] A direct calibration method for calculating hemoglobin
species may be implemented by the biosensor 100. Using four
wavelengths and applying a direct model for four hemoglobin species
in the blood, the following equation results:
wherein
H b X = a 1 * d A 1 + a 2 * d A 2 + a 3 * d A 3 + a 4 * d A 4 b 1 *
d A 1 + b 2 * d A 2 + b 3 * d A 3 + b 4 * d A 4 ##EQU00007## [0202]
dA.sub..lamda. is the differential absorption signal [0203] a.sub.n
and b.sub.n are calibration coefficients The calibration
coefficients a.sub.n and b.sub.n may be experimentally determined
over a large population average. The biosensor 100 may include a
calibration database to account for variances in the calibration
coefficients a.sub.1 and b.sub.1 (or extinction coefficients) for
the hemoglobin species for various underlying tissue
characteristics.
[0204] A two-stage statistical calibration and measurement method
for performing PPG measurement of blood analyte concentrations may
also be implemented by the biosensor 100. Concentrations of MetHb,
HbO.sub.2, RHb and HbCO are estimated by first estimating a
concentration of MetHb (in a first stage) and subsequently, if the
concentration of MetHb is within a predetermined range, then the
estimated concentration of MetHb is assumed to be accurate and this
estimated concentration of MetHb is utilized as a "known value" in
determining the concentrations of the remaining analytes HbO.sub.2,
RHb and HbCO (in a second stage). This method for determining a
concentration of hemoglobin species using a two stage calibration
and analyte measurement method is described in more detail in U.S.
Pat. No. 5,891,024 issued on Apr. 6, 1999, which is hereby
incorporated by reference herein.
[0205] The concentration of the hemoglobin compounds may thus be
determined. The biosensor 100 compensates for the hemoglobin
concentration in determinations to obtain the level of NO by the
biosensor 100. Though several methods are described herein for
obtaining a concentration of hemoglobin analytes, other methods or
processes may be used by the biosensor 100 to determine the
concentration of hemoglobin analytes or otherwise adjusting or
compensating the obtained measurements to account for a hemoglobin
concentration when determining the levels of NO in a blood
stream.
Embodiment--Determination of NO Levels Using Shifts in Absorbance
Peaks
[0206] In another embodiment, a level of NO 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
continuously, removing the uncertainty as to when to sample for
NO.
[0207] FIG. 14 illustrates a schematic block diagram of an
exemplary embodiment of a graph 1400 illustrating a shift in
absorbance peaks of hemoglobin in the presence of NO. In graph A,
the curve 1402 illustrates the absorbance spectra of reduced
hemoglobin. The addition of nitric oxide (NO) shifts the absorbance
spectra curve 1402 to a lower wavelength curve 1404 due to the
production of methemoglobin. In graph B, the absorbance spectra
curve of reduced hemoglobin 1402 is again illustrated. Endothelial
cells are then added and the absorbance spectra measured again. The
curve 1406 illustrates that little change occurs in the absorbance
spectra curve 1402 of reduced hemoglobin in the presence of
unstimulated endothelial cells. The curve 1408 illustrates the
production of methemoglobin when the same dose of endothelial cells
was given after stimulation of EDRF synthesis by the ionophore.
[0208] Though the absorbance spectrums shown in the graph 1400 were
measured using in vitro assays, the biosensor 100 may detect nitric
oxide in vivo using PPG techniques by measuring the shift in the
absorbance spectra curve of reduced hemoglobin 1402 in tissue
and/or arterial blood flow. The absorbance spectra curve 1402
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 1402, the higher the
production of methemoglobin and NO level. Correlations may be
determined between the degree of the measured shift in the
absorbance spectra curve 1402 of reduced hemoglobin to an NO level.
The correlations may be determined from a large sample population
or for a particular patient and stored in a calibration database.
The biosensor 100 may thus obtain an NO level by measuring the
shift of the absorbance spectra curve 1402 of reduced
hemoglobin.
[0209] FIG. 15 illustrates a schematic block diagram of an
exemplary embodiment of a graph 1500 illustrating a shift in
absorbance peaks of oxygenated and deoxygenated hemoglobin (HB) in
the presence of nitric oxide NO. The absorbance spectra curve 1502
of deoxygenated HB has a peak of around 430 nm. After a one minute
time period of exposure to a nitric oxide mixture, the absorbance
spectra curve 1504 of deoxygenated HB shifted to a peak of around
405 nm. In addition, the absorbance spectra curve 1506 of
oxygenated HB has a peak around 421 nm. After a twenty minute time
period of exposure to a nitric oxide mixture, the absorbance
spectra curve 1508 of oxygenated HB shifted to a peak of around 393
nm. The Deoxygenated Hb has an absorption peak at 430 nm (curve
1502) and in the presence of NO has a peak shift to 405 nm (curve
1504). The Oxygenated Hb has absorption peak at 421 nm (curve 1506)
in presence of NO has peak shift to 393 nm (curve 1508).
[0210] Though the absorbance spectrums shown in the graph 1500 were
measured using in vitro assays, the biosensor 100 may obtain an NO
level by measuring the shift of the absorbance spectra curve 1502
of deoxygenated hemoglobin and/or by measuring the shift of the
absorbance spectra curve 1506 of oxygenated hemoglobin in vivo. The
biosensor 100 may then access a calibration database that
correlates the measured shift in the absorbance spectra curve 1502
of deoxygenated hemoglobin to an NO level. Similarly, the biosensor
may access a calibration database that correlates the measured
shift in the absorbance spectra curve 1506 of oxygenated hemoglobin
to an NO level.
[0211] FIG. 16 illustrates a logical flow diagram of an exemplary
embodiment of a method 1600 for measuring NO levels in vivo using
shifts in absorbance spectra. The biosensor 100 may obtain a
concentration of NO by measuring shifts in absorbance spectra of
one or more substances that interact with NO. For example, the one
or more substances may include oxygenated and deoxygenated
hemoglobin (HB). The PPG circuit 110 detects a spectral response at
a plurality of wavelengths of the one or more substances that
interact with NO at 1602. The biosensor 100 determines the relative
shift in the absorbance spectra for the substance at 1604. For
example, the biosensor 100 may measure the absorbance spectra curve
1502 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.
[0212] The biosensor 100 accesses a calibration database that
correlates the relative shift in the absorbance spectra of the
substance with a level of NO at 1606. The biosensor 100 may thus
obtain an NO level using calibration database and the measured
relative shift in absorbance spectra of the spectrum at 1608.
[0213] FIG. 17 illustrates a logical flow diagram of an exemplary
embodiment of a method 1700 for measuring NO levels using one or
more measurement techniques. In an embodiment, the biosensor 100 is
configured to determine a level of NO in vivo using PPG technology
and one or more measurement techniques described herein. For
example, the biosensor 100 may determine an R value using at least
one L value obtained from a spectral response in the UV range at
1702. For example, the R value may be obtained using, e.g. an L
Value in the range from 380-410 such as 390 nm or 395 nm. at
L.sub.39/L.sub.940, at 1702 and accessing a calibration database
that maps the R value to an NO level. In another example, the
biosensor may determine NO level using absorption spectrum over a
plurality of wavelengths and adjusting or compensating for
hemoglobin concentrations at 1704. In another example, the
biosensor 100 may determine the relative shift in the absorbance
spectra for a substance (such as hemoglobin) and access a
calibration database that correlates the relative shift in the
absorbance spectra of the substance with a level of NO at 1706.
[0214] The biosensor 100 may use a plurality of these methods to
determine a plurality of values for the level of NO at 1708. The
biosensor 100 may determine a final concentration value using the
plurality of values. For example, the biosensor 100 may average the
values, obtain a mean of the values, etc.
[0215] FIG. 18 illustrates a logical flow diagram of an embodiment
of a method 1800 for providing a health alert for sepsis by
monitoring NO measurements. In 1802, a baseline of an NO level in
blood vessels is obtained. For example, the NO level may be
obtained from an R value using L.sub..lamda.1=390 nm and
L.sub..lamda.2=940 nm or an R value at L.sub..lamda.2=395 nm and
L.sub..lamda.2=660 nm. In another embodiment, the NO measurement
may be obtained using a value of L.sub..lamda.1=380 nm-400 nm and
L.sub..lamda.2.gtoreq.660 nm. The spectral response used to
determine the value of L.sub..lamda.1=380 nm-400 nm may also be
measuring other NO compounds, such as NO bonded to a plurality of
hemoglobin species. The concentration of the plurality of
hemoglobin species may be adjusted from the NO measurements and a
calibration database used to obtain an NO level. In another
example, the biosensor 100 may determine the relative shift in the
absorbance spectra for a substance (such as hemoglobin) and access
a calibration database that correlates the relative shift in the
absorbance spectra of the substance with a level of NO.
[0216] In 1804, the biosensor 100 displays the baseline NO
measurement and then non-invasively and continuously monitors the
NO measurement in blood vessels at 1806. For example, the biosensor
100 may obtain the NO measurement at least once per minute or more
frequently, such as every 10 seconds or 30 seconds, and continues
to display the NO measurement. The biosensor 100 may also monitor
other patient vitals indicative of sepsis condition, such as
temperature, pulse, and respiration rate.
[0217] The NO measurement of the nitric oxide is compared to a
first predetermined threshold. For example, normal ranges of the NO
measurement from the baseline measurement are determined. Patient
vitals may also be compared to predetermined thresholds. Depending
on the comparison, one or more warnings are displayed. For example,
the first predetermined threshold may be when the NO measurement
has exceeded at least 10% of the baseline level of the NO
measurement. A warning is displayed to indicate a health alert at
1810. A caregiver may then perform other tests to determine the
cause of the elevated NO measurement, such as lactic acid blood
test for sepsis.
[0218] The biosensor continues to monitor the NO measurement in
blood vessels and compare the NO measurement to one or more
predetermined thresholds. In 1812, it is determined that the NO
measurement has exceeded a second predetermined threshold. For
example, the NO measurement equals or exceeds at least 30% of a
baseline level of the NO measurement. A warning to indicate a
medical emergency is displayed at 1814. Due to the immediate danger
of such high levels of NO measurement and dangers of septic shock,
a request for immediate emergency treatment may be indicated.
Though 10% and 30% are illustrated in this example, other
percentages over the baseline level may also trigger warnings or
alerts.
TABLE-US-00002 TABLE 2 SpNO % Interpretation (Nitric Oxide Levels)
0-1.5% Diabetic patients 1.5-2% Pre-Diabetic .sup. 2-8% Normal
Patient >10% Clinically significant, consult medical control for
direction >30% Assess for septic shock, provide high flow O2,
and transport Consider emergency treatment
Embodiment--Adjustments in Response to Positioning of the
Biosensor
[0219] FIG. 19 illustrates a logical flow diagram of an embodiment
of a method 1900 for adjusting operation of the biosensor 100 in
response to a position of the biosensor 100. When the biosensor 100
is implemented in the patch 102 form factor, the biosensor 100 may
be positioned over different areas of a patient. The skin tissue
exhibits different underlying characteristics depending on the area
of the body.
[0220] For example, the biosensor 100 may be positioned on or
attached to, e.g. a hand, a wrist, an arm, forehead, chest,
abdominal area, ear lobe, fingertip 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.
[0221] The biosensor 100 is configured to obtain position
information on a patient at 1902. The position information may be
input from a user interface. In another aspect, the biosensor 100
may determine its own positioning. For example, the PPG circuit 110
may be configured to detect characteristics of underlying tissue.
The biosensor 100 then correlates the detected characteristics of
the underlying tissue with known or predetermined characteristics
of underlying tissue (e.g. measured from an abdominal area, wrist,
forearm, leg, forehead, etc.) to determine its positioning.
Information of amount and types of movement from an activity
monitoring circuit implemented within the biosensor 100 may also be
used in the determination of position.
[0222] In response to the determined position and/or detected
characteristics of the underlying tissue, the operation of the
biosensor 100 is adjusted at 1904. For example, the biosensor 100
may adjust operation of the PPG circuit 110 at 1906. The article,
"Optical Properties of Biological Tissues: A Review," by Steven L.
Jacques, Phys. Med. Biol. 58 (2013), which is hereby incorporated
by reference herein, describes wavelength-dependent behavior of
scattering and absorption of different tissues. 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 at 1908. For example, an absorption
coefficient may be adjusted when determining a level of a substance
based on Beer-Lambert principles due to the characteristics of the
underlying tissue.
[0223] In addition, the calibrations utilized by the biosensor 100
may vary depending on the positioning of the biosensor at 1908. For
example, the calibration database may include different table or
other correlations between R values and NO level 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. The calibration database may thus include different
correlations of the R value and NO level depending on the
underlying tissue. Other adjustments may also be implemented by the
biosensor 100 depending on predetermined or measured
characteristics of the underlying tissue.
[0224] The biosensor 100 is thus configured to obtain position
information and perform adjustments to its operation in response to
the position information.
[0225] FIG. 20 illustrates a schematic drawing of an exemplary
embodiment of results of a filtered spectral response 2000 obtained
using an embodiment of the biosensor 100 from a patient. The
spectral response 2000 was obtained at a wavelength of around 395
nm and is filtered by the biosensor 100 using digital signal
processing techniques to eliminate noise and background
interference to obtain the filtered spectral response 2000. A first
respiration cycle 2002 and a second respiration cycle 2004 may be
seen in the low frequency intensity fluctuation of the filtered
spectral response 2100. Due to this fluctuation in intensity during
respiratory cycles, the obtained L values may be averaged over a
plurality of respiratory cycles or over a predetermined time period
including a plurality of respiratory cycles, such as 1-2 minutes.
In addition, the respiration rate of the patient may be obtained
from measuring the periodicity of the low frequency cycles.
[0226] A low pass filter (such as a 5 Hz low pass filter) is
applied to the filtered spectral response 2100 (I.sub.AC+DC) to
obtain the DC component of the spectral response I.sub.DC. Rather
than using a low pass filter, fast Fourier transform or other
functions may also be used to isolate the DC component of the
filtered spectral response 2000. The I.sub.AC signal is generated
from the filtered spectral response and the signal I.sub.DC. The AC
component is the fluctuation due to the pulsatile expansion and
contraction of the arteriolar bed as the volume of arterial blood
increases and decreases due to the pulse rate. 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 filtered spectral response. Rather
than using a low pass filter, fast Fourier transform or other
functions may also be used to isolate the DC component of the
filtered spectral response to obtain AC. A pulse rate may also be
obtained from the I.sub.AC signal.
[0227] FIG. 21 illustrates a schematic drawing of an exemplary
embodiment of results of averaged L values 2100. In this
embodiment, the L values are obtained using spectral response from
an LED at 395 nm in the UV range. Other wavelengths may be
implemented in a UV range, such as from 380-410 nm. This range of
wavelengths has a high absorption coefficient for NO compounds. The
filtered spectral response I.sub.AC and I.sub.DC signal components
are used to compute L values 2400. The L values are affected by the
respiratory cycle as previously described. Thus, the L values 2100
shown in FIG. 21 are averaged over two or more respiratory cycles.
Alternatively, the L values 2100 may be averaged over a
predetermined time period (such as a 1-2 minute time period) that
includes a plurality of respiratory cycles. As shown in FIG. 21,
the averaged L values 2100 fluctuate between 0.2 and 0.3 over a
three minute time period.
[0228] The averaged L values may be used as an NO measurement for
baseline measurements of NO or to provide alerts based on NO
measurements as well. For example, when the averaged L.sub.395
exceeds 10% of the baseline value, e.g. such as exceeds 0.3 by over
10%, then an alert may be provided by the biosensor 100. When the
averaged L.sub.395 exceed 30% of the baseline value, e.g. such as
exceeds 0.3 by 30% or more, then another alert of a medical
emergency may be provided by the biosensor 100. Alternatively, the
baseline value of the averaged L value for an individual may be
based on observations of a healthy general population over a period
of hours or days.
[0229] FIG. 22 illustrates a schematic drawing of an exemplary
embodiment of results of averaged R values 2200. In this
embodiment, the R value is a ratio of the averaged L.sub.395nm
values and L.sub.940nm values:
Ratio R = L 3 9 5 L 9 4 0 ##EQU00008##
[0230] The averaged R values 2200 may be obtained from averaging
the Ratio R over a predetermined time period or may be calculated
from the averaged L values. As shown in FIG. 22, the averaged R
values 2206 at 395/940 nm wavelengths fluctuate between 1.68 and
1.58 over a three minute time period. The averaged R values 2204 at
479/940 nm wavelengths fluctuate between 1.68 and 1.8 over a three
minute time period. The averaged R values 2202 at 660/940 nm
wavelengths fluctuate between 0.8 and 0.78 over a three minute time
period.
[0231] The averaged R values may be used as an NO measurement for
baseline measurements of NO or to provide alerts based on NO
measurements as well. For example, when the averaged R value
exceeds 10% of the baseline value, e.g. such as exceeds 1.68 by
over 10%, then an alert may be provided by the biosensor 100. When
the averaged R value exceed 30% of the baseline value, e.g. such as
exceeds 1.68 by 30% or more, then another alert of a medical
emergency may be provided by the biosensor 100. Alternatively, the
baseline value of the averaged R value for an individual may be
based on observations of a healthy general population over a period
of hours or days. A mean or average of the R values 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.
[0232] FIG. 23A illustrates a schematic drawing of an exemplary
embodiment of an empirical calibration curve 2300 for correlating
oxygen saturation levels (SpO.sub.2) with R values. The calibration
curve 2300 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 an embodiment, the biosensor 100 may
use the 660 nm wavelength to determine SpO.sub.2 levels, e.g.
rather than 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.
[0233] FIG. 23B illustrates a schematic drawing of an exemplary
embodiment of an empirical calibration curve 2302 for correlating
NO levels (mg/dl) with R values. The calibration curve 3002 may be
included as part of the calibration database for the biosensor 100.
For example, the R values may be obtained from measurements of
L.sub.395nm/L.sub.940nm for a general population and the NO levels
also measured using one or more other techniques for verification
to generate such a calibration curve 2302. This calibration curve
2302 is based on limited clinical data and is for example only.
Additional calibration curves 2302 may also be derived from
measurements of a general population of patients 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, etc.
[0234] From the clinical trials, the L values obtained at
wavelengths around 390 nm (e.g. 380-410) are measuring NO levels in
the blood flow and surrounding tissue. The R value for
L.sub.390/L.sub.940nm 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
L.sub.390nm/L.sub.940nm and wavelengths around 390 nm such as
L.sub.395nm/L.sub.940nm. The NO levels may thus be obtained from
the R values, e.g. using a calibration database that correlates the
R value with known level of NO for the patient or for a large
general population or using neural network techniques described
herein.
[0235] In other embodiments, rather than L.sub..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.sub..lamda.1=395 nm
is used to obtain a level of NO. In addition, L.sub..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 levels of
NO.
[0236] FIG. 24 illustrates a schematic block diagram of an
embodiment of a calibration database 2400. The calibration database
2400 includes one or more calibration tables 2402, calibration
curves 2404 or calibration functions 2406 for correlating obtained
values to levels of NO. The level of NO may be expressed in the
calibration tables 2402 as units of mmol/liter, as a saturation
level percentage (SpNO %), as a relative level on a scale (e.g.,
0-10), etc.
[0237] The calibration tables 2402 include one or more calibration
tables for one or more underlying skin tissue type 2408a-n. In one
aspect, the calibration tables 2408 correlate an R value to a level
of NO for a plurality of underlying skin tissue types. For example,
a first set of tables 2408a-n may correlate R values to NO levels
for a wrist area, a second table for an abdominal area, a third
table for a forehead area, etc.
[0238] In another aspect, a set of calibration tables 2410a-n
correlate an absorption spectra shift to a level of NO for a
plurality of underlying skin tissue types. For example, a first
table 2410 may correlate a degree of absorption spectra shift of
oxygenated hemoglobin to NO levels for a wrist area, a second table
2410 for an abdominal area, a third table 2410 for a forehead area,
etc. 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 tables 2410 may correlate a degree of
absorption spectra shift of deoxygenated hemoglobin to NO levels
for a wrist area, a second table for an abdominal area, a third
table for a forehead area, etc. The degree of shift may be for the
peak of the absorbance spectra curve of deoxygenated hemoglobin
from around 430 nm.
[0239] The calibration database 2402 may alternatively or
additionally include a set of calibration curves 2404 for a
plurality of underlying skin tissue types. The calibration curves
may correlate L values or R values or degree of shifts to levels of
NO.
[0240] The calibration database 2402 may also include calibration
functions 2406. The calibration functions 2406 may be derived
(e.g., using regressive functions) from the correlation data from
the calibration curves 2404 or the calibration tables 2402. The
calibration functions 2406 may correlate L values or R values or
degree of shifts to levels of NO for a plurality of underlying skin
tissue types.
Embodiment--Screening and Prediction of Sepsis
[0241] FIG. 25 illustrates a schematic block diagram of an
embodiment of predetermined thresholds of NO measurements for
detecting a risk of sepsis. In one initial clinical trial, R values
were obtained from patients without sepsis and from patients
diagnosed with sepsis using a lactic acid blood test. In this
clinical trial, the R.sub.395/940 value for a person without a
septic condition was in a range of 0.1-8. In addition, it was
determined that an R value of 30 or higher was indicative of a
septic condition and that an R value of 8-30 was indicative of a
risk of sepsis in a patient. In general, an R value of 2-3 times a
baseline R value was indicative of a risk of sepsis in a
patient.
[0242] For example, in the example shown in FIG. 25, a range of the
R value 2500 is from 0.1 to 8 for a person without a septic
condition. The range 2502 of the R value for a person with a sepsis
condition is from 30 to 200 or above. These ranges are based on
preliminary clinical data and may vary as described hereinbelow
with additional clinical data. In addition, a position of the
biosensor, pre-existing conditions of a patient or other factors
may alter the numerical values of the ranges of the R values
described herein.
[0243] The R values are determined by measuring an NO level
directly using a wavelength in the UV range with high absorption
coefficient for NO or NO compounds, 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.
[0244] 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.
[0245] A healthcare provider may determine to continue monitoring
or perform additional tests or begin a treatment for infection. For
R.sub.395/940 values at 30 or above, the biosensor 100 may be
configured to indicate an alert indicating a high health risk or
onset of sepsis. A healthcare provider may determine to immediately
begin an aggressive treatment for infection or perform additional
treatments and intervention.
[0246] FIG. 26 illustrates a logical flow diagram of an embodiment
of a method 2600 for determining predetermined thresholds for
health alert indicators for sepsis. A baseline NO measurement in
blood vessels of a healthy general population is obtained in 2602.
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 L.sub.395 value, R value or determine a relative
level, umol/liter concentration, saturation level, etc. for a
general population over a period of time, such as hours or days.
These NO measurements are then used (such as determining an
average, mean, normalized range) to determine a baseline NO
measurement or a baseline range of NO measurements. The measurement
of NO levels include levels of one or more of: gaseous NO, nNOS
levels and/or other NO compounds, either measured as a relative
level, concentration in mmol/liter, percentage, etc.
[0247] The NO measurement in blood vessels is then obtained for a
patients with a diagnosis of sepsis at 2604. For example, the
biosensor 100 may obtain R values or other NO measurements (such as
an L.sub.395 value or a relative level, umol/liter concentration,
saturation level, etc.) 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 then used (such as
determining an average, mean, normalized range) to determine a
range of values that indicate a septic condition in a patient.
[0248] Predetermined thresholds may then be obtained from the NO
measurements at 2606. 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
at 2608.
[0249] 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.
[0250] 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.
[0251] FIG. 27 illustrates a graphical representation of an
embodiment of severity levels 2700 of an infection. In an
embodiment, the biosensor 100 may provide screening for infections,
such as sepsis, COVID-19, influenza, pneumonia, or other types of
infection. The biosensor 100 may detect an activated immune
response and determine a severity level of the infection based a
measurement of NO levels and other factors. For example, in a first
stage 2702, a patient may have a mild or moderate infection but is
not septic (e.g., not considered septic per standard blood tests of
serum lactate). The patient may have a confirmed or suspected
infection but not presenting with SIRS. An immune response is
present but may be mild or moderate. In clinical trials, the
biosensor 100 was able to detect increased levels of NO and/or
spikes or pulses of high levels of NO indicating this first stage
of an immune response to an infection.
[0252] In a second stage 2704, a patient is diagnosed with sepsis
(e.g. diagnosed with standard laboratory tests of blood samples).
Sepsis is diagnosed with two or more of the SIRS symptoms and a
confirmed or suspected infection. Prior definitions of severe
sepsis included signs of organ dysfunction, hypotension or blood
tests confirming an elevated lactate level. For example, factors in
diagnosis of severe sepsis include elevated lactate, creatinine
greater than 2 mg/dL, Bilirubin greater than 2 mg/dL, platelet
count less than 100,000 and urine output less than 0.5 mL/kg/hr or
more than 2 hours despite fluid resuscitation. The newer
definitions of sepsis include a SOFA score based on several similar
parameters shown in TABLE 1 above. Nearly all patients with severe
sepsis require treatment in an intensive care unit (ICU).
[0253] Septic shock ensues from severe sepsis and persistent low
blood pressure despite fluid resuscitation. In some studies, it
appears that on average, approximately 30% of patients diagnosed
with severe sepsis do not survive. Up to 50% of survivors suffer
from post-sepsis syndrome. Until a cure for sepsis is found, early
detection and treatment is essential for survival and limiting
disability for survivors.
[0254] In clinical trials, the biosensor 100 was able to detect
peak NO levels. In addition, the biosensor 100 was able to
determine an onset of sepsis or severe sepsis using measurements of
NO indicating increased levels of NO. For example, using a
measurement of NO levels, the biosensor 100 was able to determine
that sepsis would present in the patient up to 2-8 hours before the
clinical diagnosis of sepsis in the patient from laboratory tests.
These measurements of NO levels included pulses or spikes
indicating high levels of NO.
[0255] In a third stage 2706, a patient is in recovery from sepsis
or severe sepsis or septic shock. The levels of NO measured by the
biosensor 100 are returning to normal levels, and the peaks are not
as frequent or have lower levels. The biosensor 100 detects the
decreased immune response, recovery from the infection, and a
return to health.
[0256] In addition to the measurement of NO levels, the biosensor
100 was able to detect other parameters in diagnosing SIRS, sepsis,
severe sepsis and septic shock. For example, the biosensor 100 is
able to detect heart rate and respiration rate from one or more PPG
signals at one or more wavelengths. The biosensor 100 is thus able
to detect when the heart rate is greater than 90 bpm and
respiratory rate is greater than 20 breaths/min., both of which are
indications of SIRS and sepsis. The biosensor 100 may also include
a temperature sensor. Another factor in SIRS and sepsis is a
temperature of greater than 38 degrees C. or less than 36 degrees
C. The biosensor 100 may also detect an estimate of mean arterial
pressure changes indicate of hypotension in severe sepsis. The
biosensor 100 may also detect oxygen saturation levels, and
measurement of creatinine levels, liver enzyme levels, and
bilirubin levels. Using one or more of these factors, the biosensor
100 may screen a patient to detect an infection in a patient, such
as sepsis, COVID-19, flu, pneumonia, etc. and determine a severity
level (SIRS, sepsis, severe sepsis, acute sepsis, recovery). The
biosensor 100 may also determine a hybrid qSOFA score or hybrid
SOFA score using one or more of these factors.
[0257] FIG. 28A illustrates a graphical representation of clinical
data 2800 of a sample patient over a four day time period. A second
clinical trial was conducted to test the biosensor 100 and included
122 patients with a portion of the patients being diagnosed as
septic using conventional blood tests, such as CBC complement,
serum lactate levels or other tests. FIGS. 28A-C illustrates
clinical data 2800 obtained from a sample patient A002 diagnosed
with sepsis.
[0258] An embodiment of the biosensor 100 obtained PPG signals at a
first wavelength of 395 nm and a second wavelength of 940 nm from
the patient periodically over the four day time period and
determined R values 395 nm/940 nm shown as line 2804. As shown at a
first period 2806 during DAY 1, the R.sub.395/940 values were in a
range greater than 20 indicating a high risk of sepsis. At a second
period 2808 during DAY 2, the biosensor 100 obtained R values 2804
with large pulses over 30. These large pulses of NO levels obtained
by the biosensor 100 indicate that the patient presents with a
septic condition. However, it took over 6 hours later after these
PPG signals were detected during this second period 2808 for the
hospital to obtain a sepsis diagnosis using convention blood
tests.
[0259] Conventional blood tests for sepsis may thus provide
insufficient advance warning of deteriorating patient health or the
onset of potentially serious physiological conditions resulting
from sepsis (such as SIRS). In conventional tests, blood samples
must be taken and laboratory tests performed to obtain a diagnosis
of sepsis. For example, blood tests for sepsis include CBC
complement, serum lactate levels or other types of tests. These
types of blood tests are usually only performed once a day and are
invasive, non-continuous, costly, and time consuming. Since sepsis
is very dangerous and may escalate to be life threatening
conditions quickly, this diagnosis process is not sufficient for
early warning of the onset of sepsis or severe sepsis.
[0260] FIG. 28B illustrates a graphical representation of clinical
data 2800 of the sample patient showing an expansion of the first
period 2806 in FIG. 28A. The period 2806 includes about 5 minutes
and shows the R value 2804 during this period 2806. The R value
ranges from approximately 12 to over 20 during this five minute
period 2806.
[0261] FIG. 28C illustrates a graphical representation of clinical
data 2800 of the sample patient showing an expansion of the second
period 2808 in FIG. 28A. The period 2808 includes about 5 minutes
and shows the R value 2804 during this period 2808. The R value
ranges from approximately 5 to over 40 during this five minute
period 2808.
[0262] The biosensor 100 is thus able to monitor a patient
continuously or periodically throughout the day and obtain a
measurement of NO levels in less than five minutes. The biosensor
100 may thus detect an increase in NO levels indicating an
escalation of the infection prior to detection by conventional
blood tests. Using the measurement of NO levels by the biosensor
100, a patient may be screened within 5 minutes to determine a
presence of an infection and a severity level of the infection.
[0263] In addition to levels of NO, the biosensor 100 may also
consider other factors in the screening and monitoring of patients
for infections. For example, FIG. 28A depicts the phase difference
2802 between the PPG signal at 395 nm and the PPG signal at 940 nm.
The two wavelengths have different penetrations of depth in tissue
of a patient such that changes in the phase difference between the
two wavelengths indicates changes in the circulation of vessels in
the skin tissue, such as the microvasculature circulation or
micro-circulation. For example, the patient received a
vasoconstriction medication during period 2806 and period 2808. The
phase difference increased after both periods indicating decreased
circulation due to the effects of the vasoconstrictor.
[0264] The cardiovascular system faces great challenge during
systemic inflammatory response syndrome (SIRS). The response of the
cardiovascular system (tachycardia and hypotension) has been used
as important components in the list of diagnostic criteria for SIRS
and sepsis. Thus, the phase difference between the two PPG signals
may provide additional information for the screening and monitoring
for SIRS and sepsis and other infections, such as COVID-19,
influenza, pneumonia, etc.
[0265] FIGS. 29A-F illustrate graphical representations of clinical
data obtained from a plurality of patients in the second clinical
trial. The second clinical trial included n=122 patients admitted
and hospitalized. The patients had any of two: an infection with a
total SOFA score equal to 0 or 1 and/or a patient without sepsis
prone to the development of sepsis (CCI>2). The mean age was
75.+-.13 years and the gender distribution was male=46%,
female=54%. A 33% portion of the patients were diabetic and 60%
presented with one or more infection(s). Of the 122 patients, 11%
of the cases were verified as septic during the clinical trial
using conventional blood tests.
[0266] The patients shown in FIG. 29 were diagnosed at some point
during the clinical trial with sepsis using conventional laboratory
tests such as, CBC complement, serum lactate levels or other tests.
The biosensor 100 obtained PPG signals from the patients during a
sample window of approximately five minutes at two hour intervals.
Each interval in the graphs 29A-F indicates the average or mean
R.sub.395/940 value obtained during the corresponding sample window
of testing by the biosensor 100.
[0267] In FIG. 29A, the patient D010 was tested at two hour
intervals over a three day period to obtain 34 sample windows. The
R.sub.395/940 values 2900 range from an approximate low of 2 to an
approximate high of 14. In FIG. 29B, the patient A002 was tested at
two hour intervals for a total of 31 sample windows. The
R.sub.395/940 values 2902 range from an approximate low of 2 to an
approximate high of 35. In FIG. 29C, the patient A009 was tested at
two hour intervals to obtain 32 sample windows. The R.sub.395/940
values 2904 range from an approximate low of 2.5 to an approximate
high of 15. In FIG. 29D, the patient A010 was tested at two hour
intervals to obtain 18 sample windows. The R.sub.395/940 values
2906 range from an approximate low of 6 to an approximate high of
17. In FIG. 29E, the patient D005 was tested at two hour intervals
to obtain 14 sample windows. The R.sub.395/940 values 2908 range
from an approximate low of 1 to an approximate high of 17. In FIG.
29F, the patient DO11 was tested at two hour intervals to obtain 36
sample windows. The R.sub.395/940 values 2910 range from an
approximate low of 4 to an approximate high of 22.
[0268] Various factors affect the NO levels among the patients. For
example, it seems from the R values that patient D010 released less
NO in the bloodstream than patient A002. Patient D010 also had
issues with kidney function and presented with vascular disease due
to diabetes. These underlying illnesses seemed to lessen the NO
released in the blood stream and the resulting R values. Thus, the
endothelial health of a patient may affect the R values and
diagnosis. In an embodiment, the biosensor 100 may adjust its
determination of thresholds or other parameters in response to an
underlying health condition of a patient, such as diabetes or
atherosclerosis.
[0269] FIG. 30 illustrates a graphical representation 3000 of
clinical data obtained from blood samples of the patients diagnosed
with sepsis during the second clinical trial. The patients shown in
FIG. 30 were diagnosed with sepsis using conventional laboratory
tests such as, CBC complement, serum lactate levels or other types
of tests. These patients were also identified under qSOFA by the
presence of 2 or more clinical criteria: altered mentation,
respiratory rate .gtoreq.22 breaths/min, and systolic blood
pressure .ltoreq.100 mm Hg. The NO level was tested in the patients
by obtaining a blood sample and analyzing NO levels in blood plasma
in vitro. The NO level is illustrated in the graphical
representation 300 in units of umol/L. This testing of NO levels
was performed once per day over one or more days, D1, D2, D3, for
each of the patients identified as A009, A010, D005, D10, D011,
D029, D032, D033, D051, D061 and D075.
[0270] In contrast, the biosensor 100 was able to obtain a
measurement of NO levels in just 5 minutes at two hour intervals.
Thus comparing patient A0002, using conventional methods, an NO
level was obtained daily, e.g. three times using blood serum data
over the three day period. In contrast, the biosensor 100 was able
to obtain a measurement of NO levels 28 times over the same three
day period at two hour intervals.
[0271] The testing shows abnormally high levels of NO in blood
plasma due to the infection. The biosensor 100 was able to detect
these high levels of NO in the patients at least 2-8 hours before
Sepsis-3 identification under qSOFA methods. Thus, it seems that
increased NO levels are a precursor to sepsis and may be used as a
factor to screen a patient for infection and sepsis to determine
hospitalization, ICU placement, respiratory treatment, antibiotic
course of treatment, etc.
[0272] FIG. 31 illustrates a graphical representation of
conclusions from data obtained during the second clinical trial.
The second clinical trial included n-122 patients with a portion of
the patients being diagnosed as septic using conventional
laboratory tests, such as CBC complement, serum lactate levels or
other tests. Through an analysis of the data, it was determined
that healthy patients without sepsis have an average R.sub.395/940
value in a range from 1-10. In vitro blood serum tests of these
patients indicate an approximate NO level of 20 umol/L. Patients
with an infection or pre-septic condition have an average
R.sub.395/940 value in a range from 12 to less than 20. In vitro
blood serum tests of these patients indicate an approximate NO
level of 20 umol/L to less than 90 umol/L. Patients with sepsis or
an acute infection requiring hospitalization or treatment in an
intensive care unit (ICU) have an average R.sub.395/940 value
greater than 20. In vitro blood serum tests of these patients
indicate an approximate NO level of 30 umol/L to less than 180
umol/L. The average R.sub.395/940 values are thus dependent on the
NO levels in blood and provide an indication of a presence of an
infection and severity level of the infection (SIRS, sepsis, severe
sepsis, septic shock, recovery).
[0273] The ranges of R.sub.395/940 values for healthy, sick and
acute infection in FIG. 31 are exemplary and based on limited
clinical data of 122 patients. As seen from FIG. 25, in the first
clinical trial, a range of the R value was from 0.1 to 8 for a
person without a septic condition. The range of the R value for a
person with a sepsis condition was from 30 to 200 or above. The
ranges of the R.sub.395/940 values for healthy, sick and acute
infection in the second clinical trial shown in FIG. 31 are
somewhat more refined than the R.sub.395/940 values from the first
clinical trial shown in FIG. 27. Both of these ranges are based on
preliminary clinical data and may vary with additional clinical
data. In addition, a position of the biosensor, pre-existing
conditions of a patient or other factors may alter the numerical
values of the ranges of the R values described herein.
[0274] FIG. 32 illustrates a graphical representation of
conclusions from the second clinical trial. The second clinical
trial included 122 patients with a portion of the patients being
diagnosed as septic using conventional laboratory tests, such as
CBC complement, serum lactate levels or other tests. The biosensor
100 was able to correctly identify sepsis in 80.4% of the patients
with confirmed cases of sepsis. The biosensor 100 was able to
identify non-septic patients in 95.2% of the cases.
[0275] The biosensor 100 may thus be used as a screening tool to
determine a presence of an infection in a presenting patient. The
results of the biosensor 100 may be confirmed with conventional
laboratory tests or other additional clinical verification. The
biosensor 100 may provide a front line screening to determine an
activated immune responses and an initial assessment of a severity
of illness. The biosensor 100 is a more cost-effective and quick
screening tool versus traditional blood sampling and laboratory
tests.
[0276] In another embodiment, the average R value and/or NO levels
detected by the biosensor 100 may be used with traditional factors
in determining a qSOFA score. Traditional factors in determining a
qSOFA score include mentation of a patient, a fever of more than
100.4.degree. F. (38.degree. C.) or less than 96.8.degree. F.
(36.degree. C.), heart rate of more than 90 beats per minute,
respiratory rate of more than 20 breaths per minute, arterial
carbon dioxide tension (PaCO2) of less than 32 mm Hg., and/or
abnormal white blood cell count. In addition to these traditional
factors, the measurement of NO levels from the biosensor 100 may
also be considered with the qSOFA score for screening patients.
[0277] In another embodiment, the average R value and/or NO levels
detected by the biosensor 100 may be used with traditional factors
in determining a qSOFA score. Traditional factors in determining a
qSOFA score include mentation of a patient, a fever of more than
100.4.degree. F. (38.degree. C.) or less than 96.8.degree. F.
(36.degree. C.), heart rate of more than 90 beats per minute,
respiratory rate of more than 20 breaths per minute, arterial
carbon dioxide tension (PaCO2) of less than 32 mm Hg., and/or
abnormal white blood cell count. In addition to these traditional
factors, the measurement of NO levels from the biosensor 100 may
also be considered with the qSOFA score for screening patients.
[0278] In another embodiment, the biosensor 100 may monitor a
patient with a known or suspected infection for early signs of
sepsis or other increased severity in the illness. The biosensor
100 may monitor continuously or at periodic intervals (e.g. for 5
minutes or less every 1-2 hours).
[0279] FIG. 33 illustrates a schematic block diagram of an
embodiment of a method 3300 for screening for an infection by the
biosensor 100. At 3302, the biosensor 100 obtains a measurement of
NO levels using PPG signals, such as R.sub.395/940. At 3304, the
biosensor 100 may obtain one or more additional parameters, such as
heart rate, respiration rate, measurement of microcirculation,
measurement of bilirubin and creatinine levels, or a blood pressure
estimation. The biosensor 100 then determines a presence of an
infection using at least the measurement of NO levels. The
biosensor may also use one or more of the additional parameters in
its determination of a presence of an infection. The infection may
be a virus, bacterial infection, fungal infection or parasite. The
infection may include sepsis, or other types of underlying
infections such as influenza, pneumonia, strep throat, UTI,
COVID-10, etc. The biosensor 100 may generate a visual or auditory
indication of an infection or no infection. At 3306, the biosensor
100 may indicate a confidence level in its determination. For
example, the biosensor 100 may generate a percentage from 0-100%. A
95% confidence interval is a range of values that has 95% certainty
it contains the true mean of the population. A confidence level for
a data set may be measured in one embodiment by taking half of the
size of the confidence interval, multiplying it by the square root
of the sample size and then dividing by the sample standard
deviation. Other methods may be employed to determine the
confidence level for the determination of infection or no
infection.
[0280] For example, when a patient exhibits a measurement of an NO
level greater than 50, then a 95% confidence level may be
determined depending on the data set. However, a measurement of an
NO level of 11 may generate a 50% confidence level depending on the
data set. The confidence level thus provides guidance to a
physician on next steps, e.g. further testing or immediate
admittance to the hospital/ICU.
[0281] The biosensor 100 may also determine a severity level of the
infection at 3310 using at least the measurement of the level of
NO. The severity level may include one or more classifications,
such as mild/moderate, acute, recovery. For example, as shown in
FIG. 31, in an embodiment, an R395/940 value of 12 to less than 20
indicates a mild/moderate infection while an R395/940 value of
greater than 20 indicates an acute infection, such as sepsis,
severe sepsis or septic shock. In another embodiment, the biosensor
100 may generate a range of values to designate the severity level.
The SOFA score has a range of 0-24. The biosensor 100 may also
determine a range 0-24 to indicate the relative severity level of
the infection. The severity level may be based on the measurement
of the NO level as well as one or more other parameters, such as
respiration rate, temperature, heart rate, estimation of blood
pressure, etc.
[0282] FIG. 34 illustrates a schematic block diagram of an
embodiment of an example graphical user interface (GUI) 3400 for
displaying data obtained from the biosensor 100. The GUI 3400 may
display a measurement of NO levels determined using R.sub.UV/IR
values 3402 (such as R.sub.395nm/940nm). The display may also
illustrate a chart of the heart rate 3404 determined from the PPG
signals and a current or moving average heart rate 3414. Oxygen
saturation 3406 determined from the PPG signals may also be
displayed.
[0283] The biosensor 100 may determine and display an indicator of
an infection 3408. In an embodiment, the indicator of the infection
3408 is binary, either yes or no. The biosensor 100 may also
display a confidence level 3410 of its determination of the
infection. The biosensor 100 further may display a severity level
3412, such as a classification (mild/moderate, acute, recovery) or
a range (0-10). The confidence level 3410 and severity level 3412
provide additional guidance to a caregiver on next steps for
treatment of the patient, e.g. further testing, admitting to the
hospital/ICU, immediate antibiotic treatment, etc.
[0284] The biosensor 100 may also determine an offset 3416. The
offset provides a calibration factor for an individual based on any
underlying endothelial dysfunction or other illness, such as
diabetes. In operation, the biosensor 100 obtains PPG signals
reflected from or transmitted through the tissue of the patient. In
less than five minutes, the biosensor 100 is able to determine and
provide the indicator of the infection, the confidence level and
the severity level.
[0285] FIG. 35 illustrates a schematic diagram of endothelial
dysfunction in a patient with sepsis. 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 (in the form of eNOS)
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 due to leaking of
endothelial NO in acute dysfunction may now be double or triple the
levels in healthy endothelial of A. The levels of NO in the blood
and surrounding tissue may thus provide guidance on the presence of
sepsis and the severity of sepsis.
Embodiment--Screening for COVID-19
[0286] The severe acute respiratory syndrome coronavirus 2
(SARS-CoV-2) virus causes the disease COVID-19. The SARS-CoV-2
virus is not a living organism, but a protein module (DNA) covered
by a protective layer of lipid (fat), which, when absorbed by the
cells of the ocular, nasal, or buccal mucosa, changes their genetic
code. The SARS-CoV-2 virus mutates cells and coverts them into
aggressor and multiplier cells. Current COVID-19 testing includes
the COVID-19 RT-PCR test. It is a real-time reverse transcription
polymerase chain reaction (RT-PCR) test for the qualitative
detection of nucleic acid from SARS-CoV-2 in upper and lower
respiratory specimens (such as nasopharyngeal or oropharyngeal
swabs, sputum, lower respiratory tract aspirates, bronchoalveolar
lavage, and nasopharyngeal wash/aspirate or nasal aspirate)
collected from individuals suspected of SARS-COV-2 viral infection
by their healthcare provider. These COVID-19 RT-PCR tests are
critically low and even if administered, it takes 1-5 days to
obtain a result. Furthermore, screening of patients is problematic
because it currently is based on limited information, such as
temperature and individuals' self-assessment of their state of
health or contact with other persons diagnosed with COVID-19.
[0287] In an embodiment, the biosensor 100 and methods thereof may
assist in screening patients for SARS-COV-2 to determine a presence
of an infection. In initial testing, blood plasma data from thirty
patients with COVID-19 was obtained. The blood plasma data includes
NO levels over two periods seven days apart with increasing
severity of illness. Based on initial testing using the blood
plasma data, the blood plasma did contain elevated levels of NO.
The biosensor 100 may thus screen patients for COVID-19 using a
measurement of NO levels.
[0288] The biosensor 100 may detect whether a patient has an
infection and provide a confidence factor and even a severity
level. As described with respect to FIG. 31, in a second clinical
trial, healthy patients without infection had an average
R.sub.395/940 value in a range from 1-10. Patients with an
infection or pre-septic condition had an average R.sub.395/940
value in a range from 12 to less than 20. Patients with sepsis or
an acute infection requiring hospitalization or treatment in an
intensive care unit (ICU) had an average R.sub.395/940 value
greater than 20. Additional clinical data and verification may be
obtained to derive the R values, e.g. measurements of NO levels,
present in COVID-19 patients at various stages of the illness.
[0289] A physician may use the screening information from the
biosensor 100 to determine treatment and further testing for a
patient. For example, when the patient has no elevated levels of
NO, e.g. an R value of 1-10, the patient may be advised that no
further testing is required. In another example, when the patient
has a measurement value of NO around 15, the physician may request
further testing including COVID-19 RT-PCR testing since the immune
expression may be correlated with the presence of the SARS-COV-2
virus. In another example, when the patient has a measurement value
of NO around 30, the physician may advise immediate hospitalization
and testing.
[0290] A person may have COVID-19 and be asymptomatic (no cough or
fever), but once a person is exposed the coronavirus, the body
starts producing an immune response to fight the infection. The
biosensor 100 may thus provide a more accurate screening of persons
needing to be tested for COVID-19. Additionally, when a person is
not asymptomatic, the measurement of the NO levels by the biosensor
100 may be used along with one or more of the symptoms, such as
cough, fever, contact with other COVID-19 patients, in determining
screening and testing.
[0291] The biosensor 100 may thus screen for infections, such
SARS-COV-2. The biosensor 100 may differentiate between patients
that need further testing, such as a conventional COVID-19 RT-PCR
test and/or a flu test, and healthy patients with no infection that
need no further screening. Moreover, sepsis is strongly linked to
poor outcomes and mortality in patients with COVID-19. The
biosensor 100 may thus provide monitoring of COVID-19 patients and
provide early indications of sepsis in COVID-19 patients.
[0292] FIG. 36 illustrates a graphical representation 3600 of NO
levels in patients with a flu-like illness and in COVID-19 patients
at a first time period. Blood data of at least 30 patients with a
flu-like illness and blood data of at least 30 patients with the
SARS-COV-2 virus and pneumonia were analyzed for NO levels. The
patients diagnosed with COVID-19 had elevated Nitric Oxide (NO)
levels 3604 in blood plasma >=40 umol/L while patients with
Flu-Like illness had NO levels 3602 in blood plasma of
approximately 30 umol/L. Healthy patients in general have NO levels
of approximately 20 umol/L. These findings were provided by
European PI on Mar. 17, 2020 via blood serum sampling of 30
COVID-19 patients vs 30 Flu-Like patients at a first time period
and then at a second time period seven days later.
[0293] As seen in FIG. 36, the range of NO levels is distinct
between healthy patients, patients with a flu-like illness and
patients with COVID-19. Thus, with further data and verification,
the biosensor 100 may thus use a measurement of the NO level of a
patient to screen a patient as healthy or with COVID-19. In
addition, to the NO levels, the biosensor 100 may use other
parameters for this screening.
[0294] FIG. 37 illustrates a graphical representation 3700 of NO
levels in patients with a flu-like illness and in COVID-19 patients
at a second subsequent time period. At the second time period seven
days later, the patients diagnosed with COVID-19 had greatly
elevated Nitric Oxide (NO) levels 3704 in blood plasma >=70
umol/L while patients with Flu-Like illness had NO levels 3702 in
blood plasma of approximately 50 umol/L. Healthy patients in
general have NO levels of approximately 20 umol/L. These findings
were provided by European PI on Mar. 17, 2020 via blood serum
sampling of 30 COVID-19 patients vs 30 Flu-Like patients at a first
time period and then at a second time period seven days later.
[0295] The patients with COVID-19 exhibited increasing NO levels
with increased severity of illness after 7 days. The biosensor 100
may monitor the NO levels of patients with COVID-19 to track and
predict the severity of the illness over time. The biosensor 100
may thus indicate a severity of COVID-19 in patients with a short,
non-invasive test of 5 minutes or less that may easily be
administered periodically (e.g., every 1-2 hours) or continuously.
These measurements of NO levels may be used to determine a need for
hospitalization, ICU, mechanical or non-mechanical ventilation of a
patient or early warning of an onset of sepsis.
[0296] FIG. 38 illustrates a graphical representation of
embodiments of methods of the biosensor 100 for screening and
monitoring COVID-19 patients. In a first use case 3802, the
biosensor 100 may provide screening for COVID-19 in patients,
including public and health care workers. Current state 3804 of
screening methods includes determining whether a person has
symptoms consistent with COVID-19. To verify, the COVID-19 RT-PCR
test is administered including a nasopharyngeal swab and
oropharyngeal swab, nasopharyngeal aspirate, endotracheal aspirate,
BAL or sputum tests. The time to test results may be 1-5 days
depending on the lab service capacity, work-load and
prioritization. In a method 3806 with the biosensor 100, the
biosensor 100 screens patients for COVID-19. In five minutes or
less, the biosensor 100 may detect a measurement of NO levels to
screen for COVID-19. The biosensor 100 may detect other parameters
and use these parameters as well to screen for COVID-19, such as
heart rate, respiration rate, temperature, etc.
[0297] In another use case 3802, the biosensor 100 may monitor
patients either at home or in a hospital or other care facility.
Conventional methods of monitoring patients with COVID-19 include a
clinical assessment of severity of expression of the immune system
response to the SARS-CoV-2 virus to determine medication,
hospitalization, ICU, oxygen therapy or ventilation. The clinical
assessment is dependent on clinical progression of symptoms of
COVID-19 in patients over time after first presentation. In an
embodiment, the biosensor 100 monitors a patient and assists a
clinician in determining a severity level, such as healthy, severe,
critical. For example, the biosensor 100 determines a measurement
of NO level which is used to determine the severity of the immune
response and need for medication, hospitalization, ICU, oxygen
therapy or ventilation.
[0298] In another use case 3802, the biosensor 100 may monitor
patients at high risk such as in nursing homes or homes for the
disabled. Current methods include monitoring a patient for severe
infection or sepsis according to qSOFA guidelines. However, the
qSOFA guidelines may not be met until 2-8 hours after onset of
sepsis. In an embodiment, the biosensor 100 may monitor patients
for severe infection/sepsis using current hospital protocols and
the measurements of NO levels from the biosensor 100. The biosensor
100 may determine elevated NO levels indicating early onset of
sepsis up to 2-8 hours before standard clinical methods (such as
qSOFA guidelines).
[0299] The biosensor 100 may track other indicators of COVID-19,
such as heart rate, respiration rate, oxygen saturation and
temperature. By tracking NO levels, respiratory rate, heart rate,
oxygen saturation and temperature, the biosensor 100 may build a
model for early COVID-19 detection.
[0300] In another use case, the biosensor 100 may determine when a
patient has recovered from COVID-19 and may return to work or exit
quarantine. Currently, a person must test negative twice at least
24 hours apart to be considered "recovered" and allowed to return
to work. However, due to the shortage of tests and length of time
to obtain results, this method may prevent healthy persons from
returning to essential jobs. The biosensor 100 may detect the
measurement of NO levels and other parameters to determine whether
a person still has an immune response to the SARS-CoV-2 virus. For
example, if the patient has no temperature and no elevated NO
levels (e.g., R.sub.395/940 value is 10 or less) over a 24 hour
period, the person may be deemed "recovered" and allowed to return
to work.
Embodiment--Neural Network Processing of a Plurality of Parameters
for Infection Screening and Monitoring
[0301] One or more types of neural networks (a.k.a., machine
learning algorithms) may be implemented herein to diagnose an
infection (such as sepsis, influenza, COVID-19, pneumonia, etc.) in
a patient and/or determine a severity of the infection in the
patient.
[0302] For example, neural networks may be used to analyze 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..
[0303] 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.
[0304] FIG. 39 illustrates a graphical representation of a
plurality of parameters 3900 that may be analyzed to diagnose a
patient with an infection (such as sepsis, influenza, COVID-19,
pneumonia, etc.) and/or determine a severity level of the
infection. As previously discussed, an R value obtained using
L.lamda.1=380 nm-410 nm and L.lamda.2.gtoreq.660 nm may be used as
a measurement of a level of NO in blood flow. In an embodiment, the
measurement of the level of NO may be used to diagnose a patient
with an infection (such as sepsis, influenza, COVID-19, pneumonia,
etc.) and determine a severity level of the illness. In addition to
the R value obtained using L.lamda.1=380 nm-410 nm and
L.lamda.2.gtoreq.660 nm, other parameters may be considered in
addition to and/or alternatively to this R value in diagnosing an
infection (such as sepsis, influenza, COVID-19, pneumonia, etc.)
and determining a severity level of the illness. For example, one
or more of the following parameters may be used in these
determinations:
[0305] R value obtained using PPG signals at 395 nm (or in a range
of 380 nm-410 nm) and at 940 nm (or equal to or above 660 nm)
[0306] 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)
[0307] 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)
[0308] R value obtained using PPG signals at 460 nm (or in a range
of 440 nm-480 nm) and at 940 nm (or equal at or above 660 nm)
[0309] 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)
[0310] R value obtained using PPG signals at 468 nm (or in a range
of 448 nm-488 nm) and at 940 nm (or equal at or above 660 nm)
[0311] L value determined using PPG signals around 395 nm (or in a
range of 380 nm-400 nm)
[0312] L value determined using PPG signals around 940 nm (or equal
at or above 660 nm)
[0313] Measurement of a Time or Phase Difference 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)
[0314] 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)
[0315] 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)
[0316] Skin Temperature
The above parameters are exemplary and additional or alternate
parameters may also be considered to diagnose a patient with sepsis
or COVID-19 and/or determine a severity level of the illness.
[0317] The biosensor 100 may measure creatinine levels using the
PPG circuit by detecting PPG signals around 530 nm or in
ranges+/-20 nm thereof. Creatinine is produced by the kidneys and
various factors can affect the kidney production levels of
creatinine. The level of creatinine is also used to determine a
SOFA score as shown in Table 1 hereinabove. The biosensor 100 may
detect spectral responses, e.g. at 530 nm and 940 nm or in
ranges+/-20 nm thereof and obtain an R.sub.530/940 value. The
biosensor 100 may then then provide the measurement of the level of
creatinine in blood flow to the neural network.
[0318] In another aspect, the biosensor 100 may detect various
electrolyte concentration levels or blood analyte levels, such as
bilirubin (using L460 nm and L>660 nm or in ranges+/-20 nm
thereof to determine an R value) and iron (using L510 nm, L651 nm,
L300 nm and L>660 nm or in ranges+/-20 nm thereof to determine
an R value) and potassium (using L550 nm or in ranges+/-20 nm
thereof and L>660 nm to determine an R value). In particular,
the level of bilirubin is a parameter to determine a SOFA score as
shown in Table 1 hereinabove. In another aspect, the biosensor 100
may detect sodium chloride NACL (using L450 nm or in ranges+/-20 nm
thereof and L>660 nm or in ranges+/-20 nm thereof to determine
an R value) concentration levels in the arterial blood flow and
determine dehydration level. In another aspect, the biosensor 100
may detect various the levels of the liver enzyme P450 (using L468
nm and L>660 nm or in ranges+/-20 nm thereof to determine an R
value).
[0319] 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, 660 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 the immune system response.
[0320] 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 at 395 nm and 940 nm 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.
[0321] 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
circulation issues are occurring.
[0322] 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. A signal to noise ratio of a PPG signal may
be input. During moments of stress, the PPG signal exhibits
decreased periodicity or similarity. Blood volume may change with
heart rate as well.
[0323] The biosensor 100 may also use the PPG signals to monitor
respiration rate and respiration cycles to determine shortness of
breath or respiratory effort. Temperature of the patient, such as
skin temperature, may be monitored by the biosensor 100 or input.
The biosensor 100 may sample temperature periodically, e.g., once a
minute, so it may detect slight increases that could signal
infection days before symptoms show. The biosensor 100 may also
monitor heart rate and oxygen saturation. Blood pressure, oxygen
saturation, or temperature may be input as parameters.
[0324] The biosensor 100 may also measure the amplitude of the
pressure pulse wave as an estimation of blood pressure. In another
embodiment, the neural network processing device 4000 may estimate
a systolic blood pressure from PPG signals, as described in the
article Khalid S G, Zhang J, Chen F, Zheng D. Blood Pressure
Estimation Using Photoplethysmography Only: Comparison between
Different Machine Learning Approaches. J Healthc Eng. 2018;
2018:1548647. Published 2018 Oct. 23. doi:10.1155/2018/1548647,
incorporated by reference herein. The article describes using a
single PPG based cuffless blood pressure estimation using three
machine learning algorithms (regression tree, multiple linear
regression (MLR). The training dataset consisted of three PPG
waveform features (pulse area, pulse rising time, and Width_25%)
from each of 8133 PPG segments and their corresponding reference
systolic and diastolic blood pressure (SBP and DBP). The biosensor
100 may perform similar modeling and training of the neural network
using the same or additional or alternative PPG waveform
features.
[0325] Asepsis SOFA or qSOFA score if available may also bean input
parameter to the neural network.
[0326] One or more of these parameters may be used to diagnose a
patient with an infection, such as sepsis, influenza, pneumonia
and/or COVID-19 and/or determine a severity level of the illness.
An absolute value, minimum value, maximum value, median value and
standard deviation of the values of one or more of these parameters
may be input into the neural network.
[0327] FIG. 40 illustrates a schematic block diagram of an
embodiment of a processing device for processing the one or more of
the plurality of input parameters 4004. The processing device 4000
performs one or more of the functions described herein in response
to instructions stored in a memory device 4002 and/or other storage
devices, either local or remote.
[0328] In an embodiment, one or more types of artificial
intelligence or neural network processing models may be implemented
by the processing device 4000 to determine an output 4006 including
health data 4008, 4010, 4012 from one or more of the input
parameters 4004. For example, the processing device 4000 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 input parameters 4004 to classify a
patient as having an infection or no infection.
[0329] In another embodiment, a custom algorithm or correlation may
be applied to one or more of the input parameters 4004 to determine
to determine a severity level of an illness or classify a patient
as healthy or having an infection, such as sepsis, COVID-19,
influenza, etc. In addition, other types of AI or neural network or
machine learning processing, custom algorithms or quantum
processing may be applied to determine health data from one or more
of these parameters.
[0330] FIG. 41 illustrates a logical flow diagram of an embodiment
of a method 4100 for using a machine learning or neural network
technique for detection of health data. At 4102, a first PPG signal
at a first wavelength (e.g., 380-410 nm) having a high absorption
coefficient for NO is obtained and a second PPG at a second
wavelength (e.g. greater than 660 nm) having a lower absorption
coefficient for NO is obtained. PPG signals at one or more other
wavelengths having different depths of penetration into skin tissue
are obtained at 4104. The L values and/or R values are obtained at
4106 using the wavelengths having different depths of penetration
into the tissue, e.g. 395 nm, 460, 468, 530 nm, 660 nm, 940 nm.
[0331] Various parameters of the PPG signals may be determined or
measured at 4108. These parameters include the plurality of
parameters described hereinabove with respect to FIG. 39, such as
heart rate, respiration rate, oxygen saturation, 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. The biosensor 100 may also
use the PPG signals to determine respiration rate and respiration
cycles to measure shortness of breath or respiratory effort. The
biosensor 100 may also monitor heart rate, oxygen saturation and
estimate blood pressure. These and other parameters may be obtained
using one or more PPG signals. The PPG input data may include the
PPG signals, and/or one or more parameters derived from the PPG
signals.
[0332] In an embodiment, additional health parameters or patient
data is obtained at 4110. The patient data may include one or more
of: age, weight, body mass index, temperature, SOFA or qSOFA score,
mean arterial pressure (MAP), pre-existing medical conditions,
trauma events, mental conditions, injuries, demographic data,
physical examinations, laboratory tests, diagnosis, treatment
procedures, medications, radiology examinations, historic
pathology, medical history, surgeries, etc. Other factors, such as
contact with persons with COVID-19 or in a geographic area with a
high density of COVID-19 cases may also be considered.
[0333] The plurality of PPG and health parameters of the patient
are processed by a processing device executing a neural network
(aka machine learning algorithm) at 4112. The processing device
executes the machine learning algorithm or neural network
techniques to determine health data. The health data includes a
diagnosis of whether an infection is present in the patient. The
diagnosis may also include a type of infection, such as sepsis,
influenza, COVID-19, pneumonia, etc. The health data may also
include a confidence factor in the diagnosis. The health data may
further include a severity level of the illness. Alarms or warnings
may be issued based on the health data. Recommended further
screening or tests may be included as well.
[0334] The biosensor 100 may classify patients as not having an
infection or as having an infection or as needing further testing,
such as a conventional PCR test for SARS-COV-2 and/or an influenza
test. The biosensor 100 may diagnose a patient as having a certain
type of infection, such as sepsis, COVID-19 or other flu-like
illness. The biosensor 100 may also indicate a level of severity of
the condition, such as mild, acute, critical, recovery, etc. or in
a range from 0-10. The biosensor 100 may also determine a
confidence level in its diagnosis.
[0335] The biosensor 100 may thus indicate a diagnosis of an
infection, such as sepsis or COVID-19 and a severity of the
infection in patients with a short, non-invasive test of 5 minutes
or less that may easily be administered periodically (e.g., every
1-2 hours) or continuously. These measurements may be used to
determine a need for hospitalization, ICU, mechanical or
non-mechanical ventilation of a patient.
[0336] The neural network processing device 4000 needs to be
pre-configured with weights, parameters or other learning vectors
derived from a training set. The training set preferably includes
sets with the same type of information in the input parameters 4004
and known values of the health data 4008, 4010, 4012 in the output
4006. For example, during a learning stage, a neural network
adjusts parameters, weights and thresholds iteratively to yield a
known output vector from a known input vector. 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 the 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. Thus, the training set is used for learning or
modeling by the neural network.
[0337] In an embodiment, the training set is obtained in a clinical
setting. For example, patient data may be obtained during a
clinical trial or during use of the biosensor 100. The patient data
includes independently verifiable values, such as infection, type
of infection (sepsis, COVID-19, pneumonia, influenza, etc.) and
severity of illness (SOFA score). Other data may include age,
weight, temperature, heart rate, respiration rate, blood pressure,
pre-existing conditions, and/or medical history.
[0338] Input parameters 4004 and clinically verified output 4006 is
obtained for the training set. Preferably, the output 4006 is
obtained using a verifiable, independent method. For example, an NO
level and COVID-19 status of the patient is obtained using a known
method such as a blood test and COVID-19 PCR test respectively.
Then PPG signals at one or more wavelengths are obtained, such as
at 390 nm, 460 nm, 468 nm, 530 nm, 660 nm and 940 nm or in a range
of +/-20 nm from these wavelengths. PPG parameters may be
determined from the PPG signals, as described hereinabove.
Temperatures from a temperature sensor on the biosensor 100 may
also be used as part of the training set. The input parameters 4004
are then derived from the PPG parameters and patient data. The
training set is then generated using the input parameters 4004 and
verified output 4006.
[0339] The training set is preferably derived from thousands or
hundreds of thousands of patients having infections, such as
sepsis, COVID-19, influenza and pneumonia. The breadth of data
helps the model and training of the neural network processing
device 4000.
[0340] The training set is processed, e.g. using a learning
algorithm for a neural network. The neural network determines a
learning vector, e.g. using an estimator function or other learning
algorithms. The estimator function system may work blindly, in the
sense that no functional restriction is imposed on the relationship
between the input and output. In an embodiment, the machine
learning algorithm may include one or more of: a "random forest",
deep belief network trained using restricted Boltzmann machines, or
support vector machine. The analysis may use any known classifier
or 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.
[0341] The learning vector is thus generated and includes one or
more configuration parameters for the neural network processing
device 4000. The neural network processing device 4000 is
configured with the processing parameters in the learning vector
2106 to process input vectors to obtain output vectors.
[0342] In an embodiment, the training set is continually updated,
e.g. from clinical settings and user input. The learning vector may
be periodically updated (such as hourly, daily, etc.). The updated
learning vector may then be obtained and configured on the neural
network processing device 4000 periodically as well (such as
hourly, daily, etc.).
Embodiment--Hybrid SOFA and qSOFA Score
[0343] The qSOFA score (also known as quickSOFA) helps identify
patients with suspected infections who are at greater risk for a
poor outcome outside the intensive care unit (ICU). It uses three
criteria, assigning one point for each criteria: low blood pressure
(SBP.ltoreq.100 mmHg), high respiratory rate (.gtoreq.22 breaths
per min), or altered mentation (Glasgow coma scale<15). Organ
dysfunction can be identified as an acute change in total qSOFA
score .gtoreq.2 points consequent to infection. The baseline qSOFA
score can be assumed to be zero in patients not known to have
preexisting organ dysfunction. The qSOFA score .gtoreq.2 reflects
an overall mortality risk of approximately 10% in a general
hospital population with suspected infection. The qSOFA criteria is
used to prompt clinicians to further investigate for organ
dysfunction, to initiate or escalate therapy as appropriate, and to
consider referral to critical care or increase the frequency of
monitoring, if such actions have not already been undertaken.
[0344] FIG. 42A illustrates a schematic block diagram of an
embodiment of a method 4200 for generating a hybrid qSOFA score by
the biosensor 100. In an embodiment, the biosensor 100 may generate
a hybrid qSOFA score. The biosensor 100 obtains one or more PPG
signals at one or more wavelengths from a patient at 4202. For
example, the biosensor 100 may obtain PPG signals at 395 nm, 660
nm, 940 nm and determine a S/N ratio for the PPG signals to
determine one or more signals to use for the hybrid qSOFA score.
Using the one or more of the PPG signals, the biosensor 100 may
determine a respiratory rate and estimation of blood pressure at
4204. To obtain the estimation of blood pressure, the biosensor 100
may measure an amplitude of the pressure pulse wave and correlate
the pressure pulse wave measurement to a range that correlates to
hypotension.
[0345] In another embodiment, the neural network processing device
4000 may estimate a systolic blood pressure from the one or more
PPG signals, as described in the article Khalid S G, Zhang J, Chen
F, Zheng D. Blood Pressure Estimation Using Photoplethysmography
Only: Comparison between Different Machine Learning Approaches. J
Healthc Eng. 2018; 2018:1548647. Published 2018 Oct. 23.
doi:10.1155/2018/1548647, incorporated by reference herein. The
article describes using a single PPG based cuffless blood pressure
estimation using three machine learning algorithms (regression
tree, multiple linear regression (MLR). The training dataset
consisted of three PPG waveform features (pulse area, pulse rising
time, and Width_25%) from each of 8133 PPG segments and their
corresponding reference systolic and diastolic blood pressure (SBP
and DBP). The biosensor 100 may perform similar modeling and
training of the neural network processing device 4000 using the
same or additional or alternative PPG waveform features to estimate
SBP.
[0346] The third criteria of the qSOFA score is an altered
mentation (Glasgow coma scale<15). An altered mentation score
may need to be input into the biosensor 100 or considered in
addition to the hybrid qSOFA score generated by the biosensor 100.
For example, organ dysfunction can be identified as an acute change
in total SOFA score .gtoreq.2 points consequent to infection. The
biosensor 100 may determine a qSOFA score .gtoreq.2 points using
only the respiratory rate and the estimation of blood pressure. The
altered mentation criteria may not need to be considered then to
escalate treatment of the patient. When the biosensor 100 generates
a qSOFA score of 1 point from only the respiratory rate and the
estimation of blood pressure, the biosensor 100 may prompt the
caregiver to consider mentation independently. In another aspect,
the biosensor 100 may prompt an input of a measure of mentation,
such as a Glasgow coma scale, as a third factor to determine the
overall hybrid qSOFA score.
[0347] FIG. 42B illustrates a schematic block diagram of an
embodiment of a method 4210 for generating a hybrid SOFA score by
the biosensor 100. In an embodiment, the biosensor 100 may generate
a hybrid SOFA score. Table 1 hereinabove lists the criteria of a
SOFA score 0-4. According to Sepsis-3 definitions, a new increase
in SOFA score above baseline (score 0) in the presence of infection
makes the diagnosis of sepsis. Increasing SOFA scores are associate
with incremental increases in mortality. It is generally advised to
calculate the SOFA score using the worst value for each variable in
the preceding 24-hour period.
[0348] The biosensor 100 obtains a plurality of PPG signals at a
plurality of wavelengths from a patient at 4212. As described with
respect to FIG. 42A, the biosensor 100 may determine a respiratory
rate and estimation of systolic and diastolic blood pressure from
one or more of the plurality of PPG signals. The mean arterial
pressure (MAP) may be determined from the SBP and DBP. The
biosensor 100 may select a worst value or worst average over a
sliding window (such as 5-10 minute windows) for each criteria in
the preceding 24-hour period.
[0349] For the mentation criteria, the biosensor 100 may prompt an
input of a measure of mentation, such as a Glasgow coma scale at
4214. In another embodiment, the biosensor 100 does not consider
the measure of mentation to generate the hybrid SOFA score. The
altered mentation criteria may need to be considered with the
hybrid SOFA score by a caregiver to escalate treatment of the
patient.
[0350] Another criteria in the SOFA score is the ratio
PaO.sub.2/FiO.sub.2. The partial pressure of oxygen, also known as
PaO.sub.2, is a measurement of oxygen pressure in arterial blood
and is determined by blood tests. It reflects how well oxygen is
able to move from the lungs to the blood, and it is often altered
by severe illnesses. FiO.sub.2 is defined as the percentage or
concentration of oxygen that a person inhales (the fraction of
inspired oxygen). Natural air includes 21% oxygen, which is
equivalent to FiO.sub.2 of 0.21. Oxygen-enriched air has a higher
FiO.sub.2 than 0.21; up to 1.00 which means 100% oxygen. FiO.sub.2
is typically maintained below 0.5 even with mechanical ventilation,
to avoid oxygen toxicity. This ratio PaO.sub.2/FiO.sub.2 may be
input into the biosensor 100. Alternatively, the biosensor 100 may
use a measurement of oxygen saturation as an estimate of the ratio
PaO.sub.2/FiO.sub.2 at 4216.
[0351] The SOFA criteria also include bilirubin and creatinine
levels. A measurement of bilirubin levels in blood flow may be
measured by the biosensor 100 at 4218 using L460 nm and L>660 nm
or in ranges+/-20 nm to determine an R value. The biosensor 100 may
measure creatinine levels using PPG signals around 530 nm or in
ranges+/-20 nm thereof to determine an R value.
[0352] The SOFA criteria further includes a platelets count that is
normally obtained using a blood test. The biosensor 100 may
determine an estimate of platelets count using PPG signals at 4220
to detect platelets in blood flow. In another embodiment, the
platelet count may be input into the biosensor 100.
[0353] Using one or more of these criteria, the biosensor 100 may
determine a hybrid SOFA score at 4222. The biosensor 100 may also
use one or more other factors described herein to determine the
hybrid SOFA score and qSOFA score, such as R values, L values, PPG
parameters, etc. One or more of the criteria may be estimated by
the biosensor 100 or substituted for other measures of the
condition.
Embodiment--Infectious Disease Form Factor
[0354] FIG. 43A illustrates a perspective view of a disposable form
factor 4300 of the biosensor 100. In an embodiment, the biosensor
100 may be implemented in a disposable form factor 4300 with a
finger attachment. The biosensor 100 is designed for use with a
single patient and may be disposed after use by the single patient.
This disposal helps prevent spread of infectious diseases between
patients.
[0355] The biosensor includes a finger boot 4302 configured to
securely hold the biosensor 100 onto a finger. The finger boot 4202
may include rubber or other pliable material that may stretch
around and exert a pressure on the finger to hold it securely. A
handle 4304 may be used to stretch a top of the finger boot 4302
for insertion of the finger or removal of the finger from the
finger boot 4302. A power switch 4308 may be implemented to
initiate power and scanning by the biosensor 100. A wired USB port
4310 may be implemented that connects the biosensor 100 to another
processing device for viewing and/or analyzing the data collected
by the biosensor 100. The biosensor 100 may also include a wireless
interface, such as a Bluetooth interface, to transmit data to
another processing device. In addition, a removable memory card is
connected to a pull tab 4306. The memory card may be easily removed
by pulling the tab 4306. The memory card may be sanitized and the
data collected by the biosensor 100 retrieved from the memory
card.
[0356] FIG. 43B illustrates a perspective view of internal
components of the biosensor 100 implemented in a disposable form
factor with the finger attachment 4300. The biosensor 100 includes
one or two batteries 4314, such as coin cells, or other power
source. The biosensor 100 also includes a Bluetooth wireless
transceiver 4312 and/or other type of wireless transceiver. The
biosensor 100 may also include one or more LEDs that indicate a
presence of an infection (such as green LED lit for yes or RED LED
for no). In another embodiment, the biosensor 100 includes a
display, such as shown in FIG. 34.
[0357] FIG. 44 illustrates a perspective view of the biosensor 100
positioned on a finger of a patient. The biosensor 100 includes the
finger boot 4302 configured to securely hold the biosensor 100 onto
the finger. The finger boot 4302 may include rubber or other
pliable material that may stretch around and exert a pressure on
the finger to hold it securely. The handle 4304 may be used to
stretch a top of the finger boot 4302 for insertion of the finger
or removal of the finger from the finger boot 4302.
[0358] FIG. 45A and FIG. 45B illustrate first and second
perspective views of a non-disposable form factor of the biosensor
100. In this embodiment, the biosensor 100 includes a finger
attachment 4502. The finger attachment 4502 includes the PPG
circuit 110 and is configured to securely hold a finger that is
inserted into the finger attachment 4502.
[0359] In use, a patient places a finger inside the finger
attachment 4502. The biosensor 100 is configured to monitor PPG
signals of the patient. The biosensor 100 may also monitor
temperature using a temperature sensor array in the finger
attachment 4502. The biosensor 100 may continuously monitor the
patient, e.g. the NO measurements may be obtained a plurality of
times per minute and averaged over a predetermined time period, or
may be monitored during sample windows (such as five minutes or
less) at periodic intervals (such as 1-2 hour periods).
[0360] The biosensor 100 may display one or more measurements of
the NO levels. The displays may include, e.g., a nitric oxide
saturation level 4504 (such as SpNO %). The display may include a
bar meter 4506 illustrating a relative measured NO level. The
display may include a dial type display 4508 that indicates a
relative measured NO level. The biosensor 100 may display the
measured NO level in mmol/liter units 4512. These types of displays
are examples only and other types of display may be employed to
indicate the level of NO measured in a patient. The biosensor 100
may also obtain and display other patient vitals such as heart
rate, respiration rate, oxygen saturation and temperature. Though
in this embodiment, the display is located on the finger attachment
4502, the biosensor 100 may transmit data to a monitoring station
or user device for display of the information. The biosensor 100
may also include a separate device (such as a user device) that
processes the PPG signals to obtain the health data described
herein.
[0361] The biosensor 100 may be implemented in other compact form
factors, such as on a patch, wrist band, ring or earpiece. Due to
its compact form factor, the biosensor 100 may be configured for
measurements on various skin surfaces of a patient, including on a
forehead, arm, wrist, abdominal area, chest, leg, ear, ear lobe,
finger, toe, ear canal, etc.
[0362] FIG. 46A and FIG. 46B illustrate perspective views of an
embodiment of the biosensor 100 implemented in a patch 4602. FIG.
46A illustrates a perspective view of a top 4610 of the biosensor
100 while FIG. 46B illustrates a perspective view of the back 4612
of the biosensor 100. The biosensor 100 is configured for placement
of the back 4612 of the patch 4602 adjacent to skin tissue of the
patient. The patch 4602 may include an adhesive backing 104 such
that it may adhere to a patient's skin. The patch 4602 may
alternatively be secured through other means, such as tape,
etc.
[0363] The patch 4602 includes the optical sensor
photoplethysmography (PPG) circuit 110. The biosensor 100 further
includes a health alert indicator to provide an indicator of an
infection in the patient. The health alert indicator in this
embodiment includes a first LED 4606. When symptoms of an infection
are detected, the first LED 4606 may illuminate to provide a
warning. For example, the first LED 106 may illuminate a first
color (e.g. green) to indicate no or little risk of infection, such
as sepsis, COVID-19 or other infection while a second color (e.g.
red) may indicate that symptoms have been detected indicating a
risk of an infection. The biosensor 100 may also measure other
patient vitals such as heart rate, e.g. beats per minute (bpm),
respiration rate, oxygen saturation or temperature. These
measurements may also be considered when determining an infection,
such as sepsis or COVID-19 or other infection. Due to its compact
form factor, the patch 4602 may be attached on various skin
surfaces of a patient, including on a forehead, arm, wrist,
abdominal area, chest, leg, hand, etc.
[0364] In an embodiment, the patch 4602 is designed to be
disposable, e.g. designed to be used on a single patient. For
example, the biosensor 100 may include a battery with a relatively
short life span of 24-48 hours. In use, the biosensor 100 is
activated and the adhesive backing is peeled and attached to a
single patient for monitoring. A second LED 4608 may indicate
activation of the biosensor 100. For example, when the second LED
4608 is illuminated, it indicates that the biosensor 100 is
activated and monitoring the patient. When the second LED 4608 is
not lit, it indicates that monitoring has stopped. When monitoring
is complete for that single patient or the battery of the biosensor
100 has lost charge, the patch 4602 is removed and thrown away.
[0365] FIG. 47 illustrates a schematic block diagram of an
embodiment of the biosensor 100 with another biomarker sensor
device 4702. The biosensor 100 may include or be incorporated with
or communicate with one or more other types of sensor devices. In
one embodiment, the biosensor 100 is configured to include a
biomarker sensor device 4702 that analyzes fluid samples from a
patient. For example, the patient may test saliva, blood, urine or
other fluid onto a chip or test strip. The input reader 4704
receives the fluid samples, e.g. on a chip or test strip. The
biomarker sensor device 4702 may perform one or more tests to
detect conditions of the patient.
[0366] In one example, a test for infection such as COVID-19 is
incorporated into the biosensor 100. A patient swabs a throat and
rinses the swab in a test liquid. The test liquid is inserted into
a chip for insertion into the input reader 4704. The biosensor 100
may perform tests on the chip to determine a presence of an
infection.
[0367] The biosensor 100 may communicate over a wired or wireless
local area network 4708 to a user device 4706. In another
embodiment, the biosensor 100 may communicate directly with the
user device 4706 using Bluetooth, RFID or other short range
communication. The user device 4708 and/or biosensor 100 may
communicate over a wide area network (WAN) 4710 to a biosensor
application server 4712. The biosensor application server 4712 may
collect data of a plurality of users for modeling of the data and
determination of infections in geographical areas and determining
demographic data of infections. The biosensor 100 may also
communicate patient data to a caregiver device 4714. The patient
data may be stored in an electronic medical record (EMR) 4716.
[0368] 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, 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. A memory is a
non-transitory memory device 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.
[0369] 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".
[0370] 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.
[0371] 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.
[0372] 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.
[0373] 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.
[0374] 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.
[0375] 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.
[0376] 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."
* * * * *