U.S. patent application number 16/989740 was filed with the patent office on 2021-05-20 for systems and methods for biological metrics measurement.
The applicant listed for this patent is Spry Health, Inc.. Invention is credited to Andrew DeKelaita, Elad Ferber, Pierre-Jean Julien Ghislain Cobut, Ramkrishnan Narayanan.
Application Number | 20210145334 16/989740 |
Document ID | / |
Family ID | 1000005374063 |
Filed Date | 2021-05-20 |
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United States Patent
Application |
20210145334 |
Kind Code |
A1 |
Ferber; Elad ; et
al. |
May 20, 2021 |
SYSTEMS AND METHODS FOR BIOLOGICAL METRICS MEASUREMENT
Abstract
A wearable member may include a plurality of energy transmitters
that are arranged on a surface of the wearable member, each of the
energy transmitters being configured to project energy into tissue
of a user. A wearable member may include a plurality of energy
receivers each of which is configured to generate a signal based on
a received portion of the energy that is projected by one or more
of the energy transmitters and reflected by the tissue of the user,
wherein at least one of the energy transmitters and the energy
receivers are multi-dimensionally arranged on the wearable member
such that energy reflected by the tissue of the user at locations
that are multi-dimensionally different is incident on the plurality
of energy receivers. The processor may be configured to calculate a
biological metric based on signals generated by at least part of
the plurality of energy receivers.
Inventors: |
Ferber; Elad; (Woodside,
CA) ; DeKelaita; Andrew; (Foster City, CA) ;
Narayanan; Ramkrishnan; (San Jose, CA) ; Ghislain
Cobut; Pierre-Jean Julien; (Menlo Park, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Spry Health, Inc. |
Palo Alto |
CA |
US |
|
|
Family ID: |
1000005374063 |
Appl. No.: |
16/989740 |
Filed: |
August 10, 2020 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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15717737 |
Sep 27, 2017 |
10736552 |
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16989740 |
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62400456 |
Sep 27, 2016 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
A61B 5/0205 20130101;
A61B 5/02438 20130101; A61B 2562/043 20130101; A61B 2562/0238
20130101; A61B 5/021 20130101; A61B 5/02433 20130101; A61B 5/0075
20130101; A61B 5/14552 20130101; A61B 5/0816 20130101; A61B 5/14546
20130101; A61B 5/7278 20130101; A61B 5/14535 20130101; A61B 5/14532
20130101 |
International
Class: |
A61B 5/1455 20060101
A61B005/1455; A61B 5/00 20060101 A61B005/00; A61B 5/0205 20060101
A61B005/0205; A61B 5/024 20060101 A61B005/024; A61B 5/08 20060101
A61B005/08 |
Claims
1. A system comprising: a wearable member including: a plurality of
energy transmitters that are arranged on a surface of the wearable
member, each of the energy transmitters being configured to project
energy into tissue of a user; and a plurality of energy receivers
each of which is configured to generate a signal based on a
received portion of the energy that is projected by one or more of
the energy transmitters and reflected by the tissue of the user,
wherein at least one of the energy transmitters and the energy
receivers are multi-dimensionally arranged on the wearable member
such that energy reflected by the tissue of the user at locations
that are multi-dimensionally different is incident on the plurality
of energy receivers; and a processor configured to calculate a
biological metric based on signals generated by at least part of
the plurality of energy receivers.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] The present application is a continuation of U.S.
Nonprovisional patent application Ser. No. 15/717,737, filed Sep.
27, 2017, entitled "SYSTEMS AND METHODS FOR BIOLOGICAL METRICS
MEASUREMENT," which claims the benefit of U.S. Provisional Patent
Application No. 62/400,456, filed Sep. 27, 2016, entitled "WIDE
AREA SWITCHING PHOTOPLETHYSMOGRAM," the contents of which are all
incorporated herein by reference.
BACKGROUND
Technical Field
[0002] Embodiments of the present inventions relate generally to
blood metrics measurement. More specifically, embodiments of the
present inventions relate to non-invasive blood pressure
measurement.
Description of Related Art
[0003] Wearable activity monitoring devices are growing in
popularity. These devices aim to facilitate achieving a user's goal
such as to lose weight, to increase physical activity, or simply to
improve overall health. Many such devices may interface with
computer software to allow visualization of the recorded data.
Nevertheless, most devices are evolved cousins of pedometers, which
measure the number of steps a user takes. Even though additional
functions such as tallying the distance a user travels or
calculating calorie consumptions may be added, these devices lack
the ability to measure blood metrics.
[0004] Blood pressure is an important factor in both heart health
and overall health. For example, elevated blood pressure may result
in coronary artery disease, heart failure and hypertrophy.
Accordingly, blood pressure monitoring has become an important
component of patient health. Typically, blood pressure is monitored
using a blood pressure gauge with an inflatable cuff. However, such
devices are often uncomfortable and unable to provide continuous
blood pressure measurement.
SUMMARY
[0005] A system comprises a wearable member. The wearable member
may include a plurality of energy transmitters that are arranged on
a surface of the wearable member, each of the energy transmitters
being configured to project energy into tissue of a user. The
plurality of energy receivers may each be configured to generate a
signal based on a received portion of the energy that is projected
by one or more of the energy transmitters and reflected by the
tissue of the user, wherein at least one of the energy transmitters
and the energy receivers are multi-dimensionally arranged on the
wearable member such that energy reflected by the tissue of the
user at locations that are multi-dimensionally different is
incident on the plurality of energy receivers. The wearable member
may comprise a processor configured to calculate a biological
metric based on signals generated by at least part of the plurality
of energy receivers.
[0006] In some embodiments, the processor is configured to separate
one or more source biological signals based on the signals
generated by at least part of the plurality of energy receivers,
identify at least one artery signal from the one or more source
biological signals, and calculate the biological metric based on
the at least one artery signal. The processor may be configured to
separate two or more source biological signals based on the signals
generated by at least part of the plurality of energy receivers,
identify two or more artery signals at two different locations of
the user from the two or more source biological signals, and
calculate a blood pressure of the user as the biological metric
based on the two or more artery signals.
[0007] In various embodiments, the number of the energy
transmitters is greater than the number of the energy receivers. In
some embodiments, the number of the energy transmitters may be the
same as the number of the energy receivers. The plurality of energy
receivers may include two energy receivers arranged along a first
line, and the plurality of energy transmitters may include two
energy transmitters arranged along the first line on a first side
of the two energy receivers, two energy transmitters arranged along
the first line on a second side of the two energy receivers, and
two or more energy transmitters arranged along a second line
extending along the first line. The plurality of energy receivers
may include two groups of two energy receivers arranged along a
first line, each group of two energy receivers being disposed
between two energy transmitters arranged along the first line, and
two groups of two energy receives arranged along a second line
extending along the first line, each group of two energy receivers
along the second line being disposed between two energy
transmitters arranged along the second line.
[0008] In some embodiments, the wearable member is wearable around
a wrist of the user, and a multi-dimensional region defined by the
plurality of energy transmitters ranges at least 20 mm in a
direction of the wearable member corresponding to a circumferential
direction of the wrist. The wearable member may be wearable around
a wrist of the user, and a multi-dimensional region defined by the
plurality of energy transmitters ranges at least 15 mm in a
direction of the wearable member corresponding to an extending
direction of an arm of the user. Each of the energy transmitters
may be configured to project energy at a first wavelength and
energy at a second wavelength into the tissue of the user, and each
of the energy receivers is configured to generate a first signal
based on a first received portion of the energy at the first
wavelength and a second signal based on a second received portion
of the energy at the second wavelength. The biological metric may
be, for example, at least one of a systolic blood pressure,
diastolic blood pressure, respiratory rate, blood oxygen, or heart
rate.
[0009] An example method may comprise projecting, at a plurality of
energy transmitters that are arranged on a surface of a wearable
member that is attached to a user, energy into tissue of the user,
generating, at a plurality of energy receivers that are arranged on
the surface of the wearable member attached to the user, a signal
based on a received portion of the energy that is projected by one
or more of the energy transmitters and reflected by the tissue of
the user, wherein at least one of the energy transmitters and the
energy receivers are multi-dimensionally arranged on the wearable
member such that energy reflected by the tissue of the user at
locations that are multi-dimensionally different is incident on the
plurality of energy receivers, and calculating a biological metric
based on signals generated by at least part of the plurality of
energy receivers.
[0010] An exemplary system comprises an energy transmitter, an
energy receiver, and an analyzer. The energy transmitter may
project energy at a first wavelength and a second wavelength into
tissue of a user, the first wavelength and the second wavelength
being associated with at least one nutrient of a set of nutrients
in blood of the user. The energy receiver may generate a composite
signal based on a fraction of the energy at the first wavelength
and the second wavelength, the fraction of the energy being
received through the tissue of the user. The analyzer may separate
the composite signal into a first signal corresponding to the first
wavelength and a second signal corresponding to the second
wavelength, and detect, in the blood of the user, a concentration
of the at least one nutrient of the set of nutrients based on the
first signal and the second signal.
[0011] The fraction of the energy may be received by the energy
receiver after the fraction of the energy is reflected by the
tissue of the user. The system may comprise a wearable member. The
energy transmitter and the energy receiver may be secured to the
wearable member such that the energy transmitter and the energy
receiver are in contact or in proximity with the tissue. The
analyzer may be further configured to determine a set of blood
metrics based on the first signal and the second signal, the
concentration of at least one nutrient of the set of nutrients
being determined based on the determined set of blood metrics. The
system may further comprise a user interface configured to display
at least some of the set of blood metrics. The analyzer may be
further configured to compare a blood metric of the set of blood
metric to a threshold and to generate an alert if the blood metric
exceeds the threshold. The set of blood metrics may comprise a
blood glucose concentration.
[0012] The analyzer may be further configured to determine a first
AC component and a first DC component of the first signal, to
determine a second AC component and a second DC component of the
second signal, wherein the concentration of a nutrient of the set
of nutrients is detected based on the first AC component, the first
DC component, the second AC component, and the second DC component.
The system may further comprise a motion detector configured to
measure a level of motion, and the analyzer is configured to
compare the level of motion to a threshold and to discount a
measurement of the composite signal when the level of motion
exceeds the threshold. A nutrient of the set of nutrients may
comprise glucose.
[0013] An exemplary method may comprise projecting energy at a
first wavelength and a second wavelength into tissue of a user, the
first wavelength and the second wavelength being associated with at
least one nutrient of a set of nutrients in blood of the user,
generating a composite signal based on a fraction of the energy at
the first wavelength and the second wavelength, the fraction of the
energy being received through the tissue of the user, separating
the composite signal into a first signal corresponding to the first
wavelength and a second signal corresponding to the second
wavelength, and detecting, in the blood of the user, a
concentration of the at least one nutrient of the set of nutrients
based on the first signal and the second signal.
[0014] Another exemplary system may comprise an energy transmitter,
an energy receiver, and an analyzer. The energy transmitter may be
configured to project energy at a first wavelength and a second
wavelength into tissue of a user, the first wavelength and the
second wavelength being associated with, in blood of the user, at
least one component. The at least one component being at least one
of one of glucose, hemoglobin, triglycerides, cholesterol,
bilirubin, protein, albumin, blood pH, Hematocrit, cortisol, and/or
electrolytes. The energy receiver may be configured to generate a
composite signal based on a fraction of the energy at the first
wavelength and the second wavelength, the fraction of the energy
being received through the tissue of the user. The analyzer may be
configured to separate the composite signal into a first signal
corresponding to the first wavelength and a second signal
corresponding to the second wavelength, and to detect, in the blood
of the user, a concentration of the at least one component based on
the first signal and the second signal.
[0015] Other features and aspects of various embodiments will
become apparent from the following detailed description, taken in
conjunction with the accompanying drawings, which illustrate, by
way of example, the features of such embodiments.
[0016] Typically, blood pressure is measured non-invasively with a
sphygmomanometer. However, such devices are often uncomfortable and
do not permit continuous blood pressure measurement. Some
embodiments described herein include systems and methods for
non-invasive continuous blood pressure measurement. For example, a
blood metrics measurement apparatus may generate multi-channel
signals (e.g., PPG signals) which may be provided to a blood
pressure calculation system to calculate arterial blood pressure
values (e.g., systolic blood value pressure and/or diastolic blood
pressure value). More specifically, the blood pressure calculation
system (or the blood pressure measurement apparatus) may filter the
multi-channel signals (e.g., to remove noise from the signals),
select (or, "extract") subsets of "high quality" waves from the
multi-channel signals, select (or, "extract") sets of features from
each of the high quality waves, and generate sets of feature
vectors based on the selected sets of features. In some
embodiments, an empirical blood pressure model is used to calculate
arterial blood pressure values based on the sets of feature
vectors.
[0017] In various embodiments, a system comprises a wearable member
and a blood pressure calculation system. The wearable member may
include an energy transmitter configured to project energy at a
first wavelength and energy at a second wavelength into tissue of a
user, and an energy receiver configured to generate a first signal
based on a first received portion of the energy at the first
wavelength and a second signal based on a second received portion
of the energy at the second wavelength, the first received portion
of energy and the second received portion of energy each being
received through the tissue of the user. The blood pressure
calculation system may include a pre-processing module configured
to filter noise (e.g., motion related noise) from the first signal
and the second signal, and a wave selection module configured to
identify a first subset of waves from a first set of waves of the
first signal and a second subset of waves from a second set of
waves of the second signal, each of the first subset of waves
representing a separate approximation of an average of the first
set of waves over a predetermined amount of time and each of the
second subset of waves representing a separate approximation of an
average of the second set of waves over the predetermined amount of
time. The blood pressure calculation system may further include a
feature extraction module configured to generate a first set of
feature vectors and a second set of feature vectors, the first set
of feature vectors generated from the first subset of waves, the
second set of feature vectors generated from the second subset of
waves, wherein each of the feature vectors of the first set of
feature vectors and the second set of feature vectors include
measurement values and metric values, the measurement values
corresponding to amplitude or location points of a particular wave,
the metric values generated from metric functions that use at least
one of the measurement values. The blood pressure calculation
system may additionally include a blood pressure processing module
configured to calculate an arterial blood pressure value based on
the first set of feature vectors, the second set of feature
vectors, and an empirical blood pressure calculation model, the
empirical blood pressure calculation model configured to receive
the first set of feature vectors and the second set of feature
vectors as input values. The blood pressure calculation system may
further include a communication module configured to provide a
message including or being based on the arterial blood pressure
value.
[0018] In some embodiments, the pre-processing module is configured
to filter noise from the first signal and second signal using an
adaptive filter configured to remove motion noise from the first
and second signals.
[0019] In some embodiments, the energy transmitter includes a first
light source and a second light source, the first light source
configured to project the energy at the first wavelength, the
second light source configured to project the energy at the second
wavelength.
[0020] In some embodiments, the first light source and the second
light source are spaced at a predetermined distance from each
other, and each of the first and second light sources are
associated with a different corresponding photodiode energy
receiver. In related embodiments, the measurement values comprise a
transit time determined based on a time for blood to transit the
predetermined distance between the first and second light
sources.
[0021] In some embodiments, the measurement values include any of
wave peak locations or amplitudes, or wave valley locations or
amplitudes.
[0022] In some embodiments, the measurement values include any of
an associated wave's first or higher order derivative peak
locations or amplitudes, the associated wave's first or higher
order derivative valley locations or amplitudes, or first or higher
order moments of the associated wave.
[0023] In some embodiments, the metric functions include one or
more particular metric functions that calculate a distance between
two measurement values.
[0024] In some embodiments, the energy projected by the first light
source and the energy projected by second light source each have
the same wavelength. In related embodiments, the feature extraction
module is further configured to determine a phase shift between the
first signal and the second signal; calculate, based on the phase
shift, any of a pulse wave velocity or a pulse transit time based
on the predetermined distance; and the blood pressure calculation
module is further configured to calculate the arterial blood
pressure value based on first set of feature vectors, the second
set of feature vectors, any of the pulse wave velocity or the pulse
transit time, the empirical blood pressure calculation model, the
empirical blood pressure calculation model further configured to
receive the first set of feature vectors, the second set of feature
vectors, and any of the pulse wave velocity or the pulse transit
time as input.
[0025] In some embodiments, the first signal and the second signal
each comprise a photoplethysmogram (PPG) signal
[0026] In various embodiments, a method comprises projecting, at an
energy transmitter, energy at a first wavelength and energy at a
second wavelength into tissue of a user; generating, at the energy
transmitter, a first signal based on a first received portion of
the energy at the first wavelength and a second signal based on a
second received portion of the energy at the second wavelength, the
first received portion of energy and the second received portion of
energy each being received through the tissue of the user;
filtering, at a blood pressure calculation system, noise from the
first signal and second signal; identifying, at the blood pressure
calculation system, a first subset of waves from a first set of
waves of the first signal and a second subset of waves from a
second set of waves of the second signal, each of the first subset
of waves representing a separate approximation of an average of the
first set of waves over a predetermined amount of time and each of
the second subset of waves representing a separate approximation of
an average of the second set of waves over the predetermined amount
of time; generating, at the blood pressure calculation system, a
first set of feature vectors and a second set of feature vectors,
the first set of feature vectors generated from the first subset of
waves, the second set of feature vectors generated from the second
subset of waves, wherein each of the feature vectors of the first
set of feature vectors and the second set of feature vectors
include measurement values and metric values, the measurement
values corresponding to amplitude or location points of a
particular wave, the metric values generated from metric functions
that use at least one of the measurement values; calculating, at
the blood pressure calculation system, an arterial blood pressure
value based on the first set of feature vectors, the second set of
feature vectors, and an empirical blood pressure calculation model,
the empirical blood pressure calculation model configured to
receive the first set of feature vectors and the second set of
feature vectors as input values; and providing, from the blood
pressure calculation system, a message including or being based on
the arterial blood pressure value.
[0027] In some embodiments, the filtering noise from the first
signal and second signal comprises filtering noise from the first
signal and second signal using an adaptive filter configured to
remove motion noise from the first signal and the second
signal.
[0028] In some embodiments, the energy transmitter includes a first
light source and a second light source, the first light source
configured to project the energy at the first wavelength, the
second light source configured to project the energy at the second
wavelength. In related embodiments, the first light source and the
second light source are spaced at a predetermined distance from
each other, and each of the first and second light sources are
associated with a different corresponding photodiode energy
receiver.
[0029] In some embodiments, the measurement values comprise a
transit time determined based on a time for blood to transit the
predetermined distance between the first and second light
sources.
[0030] In some embodiments, the measurement values include any of
wave peak locations or amplitudes, or wave valley locations or
amplitudes.
[0031] In some embodiments, the measurement values include any of
an associated wave's first or higher order derivative peak
locations or amplitudes, the associated wave's first or higher
order derivative valley locations or amplitudes, or first or higher
order moments of the associated wave.
[0032] In some embodiments, the metric functions include one or
more particular metric functions that calculate a distance between
two measurement values.
[0033] In some embodiments, the energy projected by the first light
source and the energy projected by second light source each have
the same wavelength. In related embodiments, the feature extraction
module is further configured to determine a phase shift between the
first signal and the second signal; calculate, based on the phase
shift, any of a pulse wave velocity or a pulse transit time based
on the predetermined distance; and the blood pressure calculation
module is further configured to calculate the arterial blood
pressure value based on first set of feature vectors, the second
set of feature vectors, any of the pulse wave velocity or the pulse
transit time, the empirical blood pressure calculation model, the
empirical blood pressure calculation model further configured to
receive the first set of feature vectors, the second set of feature
vectors, and any of the pulse wave velocity or the pulse transit
time as input.
[0034] In various embodiments, a system comprises a communication
interface configured to receive a first signal and a second signal,
the first signal being based on a first received portion of energy
having been previously projected at a first wavelength into tissue
of a user, the second signal being based on a second received
portion of energy having been previously projected at a second
wavelength into the tissue of the user; a pre-processing module
configured to remove noise from the first signal and the second
signal; a wave selection module configured to identify a first
subset of waves from the first set of waves of a first signal and a
second subset of waves from a second set of waves of the second
signal, each of the first subset of waves representing a separate
approximation of an average of the first set of waves over a
predetermined amount of time and each of the second subset of waves
representing a separate approximation of an average of the second
set of waves over the predetermined amount of time; a feature
extraction module configured to generate a first set of feature
vectors and a second set of feature vectors, the first set of
feature vectors generated from the first subset of waves, the
second set of feature vectors generated from the second subset of
waves, wherein each of the feature vectors of the first set of
feature vectors and the second set of feature vectors include
measurement values and metric values, the measurement values
corresponding to amplitude or location points of a particular wave,
the metric values generated from metric functions that use at least
one measurement value; a blood pressure processing module
configured to calculate an arterial blood pressure value based on
the first set of feature vectors, the second set of feature
vectors, and an empirical blood pressure calculation model, the
empirical blood pressure calculation model configured to receive
the first set of feature vectors and the second set of feature
vectors as input values; and a communication module configured to
provide a message including or being based on the arterial blood
pressure value.
[0035] In various embodiments, a system comprises a processor; and
memory storing instructions that, when executed by the processor,
cause the processor to: receive a first signal and a second signal,
the first signal being based on a first received portion of energy
having been previously projected at a first wavelength into tissue
of a user, the second signal being based on a second received
portion of energy having been previously projected at a second
wavelength into the tissue of the user; filter noise (e.g., motion
related noise) from the first signal and the second signal;
identify a first subset of waves from a first set of waves of the
first signal and a second subset of waves from a second set of
waves of the second signal, each of the first subset of waves
representing a separate approximation of an average of the first
set of waves over a predetermined amount of time and each of the
second subset of waves representing a separate approximation of an
average of the second set of waves over the predetermined amount of
time; generate a first set of feature vectors and a second set of
feature vectors, the first set of feature vectors generated from
the first subset of waves, the second set of feature vectors
generated from the second subset of waves, wherein each of the
feature vectors of the first set of feature vectors and the second
set of feature vectors include measurement values and metric
values, the measurement values corresponding to amplitude or
location points of a particular wave, the metric values generated
from metric functions that use at least one of the measurement
values; calculate an arterial blood pressure value based on the
first set of feature vectors, the second set of feature vectors,
and an empirical blood pressure calculation model, the empirical
blood pressure calculation model configured to receive the first
set of feature vectors and the second set of feature vectors as
input values; and provide a message including or being based on the
arterial blood pressure value.
BRIEF DESCRIPTION OF THE DRAWINGS
[0036] FIG. 1 is a block diagram illustrating an example
environment utilizing a multispectral blood metrics measurement
apparatus in accordance with various embodiments.
[0037] FIG. 2 is a block diagram illustrating an exemplary
multispectral blood metrics measurement apparatus, such as the
multispectral blood metrics measurement apparatus illustrated in
FIG. 1.
[0038] FIG. 3 illustrates an exemplary flow diagram of a method of
measuring blood metrics in accordance with an embodiment of the
present application.
[0039] FIG. 4 illustrates an exemplary apparatus for measuring
various blood metrics in accordance with an embodiment of the
present application.
[0040] FIG. 5 illustrates a display of an assessment of a current
health index derived from data collected from or with a
multispectral blood metrics measurement apparatus in various
embodiments.
[0041] FIG. 6 illustrates a display of an assessment of an overall
health index, derived from data collected from or with a
multispectral blood metrics measurement apparatus in various
embodiments.
[0042] FIG. 7 illustrates a display of an assessment of an overall
health index, derived from data collected from or with a
multispectral blood metrics measurement apparatus in various
embodiments.
[0043] FIG. 8 is a block diagram illustrating an exemplary digital
device that can be utilized in the implementation of various
embodiments.
[0044] FIG. 9 depicts a block diagram of a system and environment
for non-invasive blood pressure measurement according to some
embodiments.
[0045] FIG. 10A depicts a block diagram of a blood metrics
measurement apparatus according to some embodiments.
[0046] FIG. 10B depicts a block diagram of a sensor system
according to some embodiments.
[0047] FIG. 11 depicts a flowchart of an example method of
operation of a blood metrics measurement apparatus according to
some embodiments.
[0048] FIG. 12 depicts a block diagram of a user device according
to some embodiments.
[0049] FIG. 13 depicts a flowchart of an example method of
operation of a user device according to some embodiments.
[0050] FIG. 14 depicts a block diagram of a blood pressure
calculation system according to some embodiments.
[0051] FIG. 15A depicts a flowchart of an example method of
operation of a blood pressure calculation system according to some
embodiments.
[0052] FIG. 15B depicts a flowchart of an example method of
pre-processing (or, "filtering") a signal according to some
embodiments.
[0053] FIG. 15C depicts a blocks diagram of an example
pre-processing system for filtering signals according to some
embodiments.
[0054] FIG. 16A depicts a flowchart of an example method for
extracting high quality waves according to some embodiments.
[0055] FIG. 16B depicts a flowchart of an example method for
extracting high quality waves using a Gaussian mixture model
according to some embodiments.
[0056] FIG. 16C depicts a flowchart of an example method of method
for extracting high quality waves using a group similarity model
according to some embodiments.
[0057] FIG. 17 depicts a block diagram of a blood metrics server
according to some embodiments.
[0058] FIGS. 18-20 depict flowcharts of example methods of
operation of a blood metrics server according to some
embodiments.
[0059] FIG. 21 depicts an example noisy PPG signal and an example
filtered PPG signal according to some embodiments.
[0060] FIG. 22 depicts an example set of waves of a PPG signal and
an example high quality wave selected from the set of waves
according to some embodiments.
[0061] FIG. 23 depicts example feature points of a wave according
to some embodiments.
[0062] FIG. 24 depicts an example feature vector according to some
embodiments.
[0063] FIGS. 25A-C show an example selected high quality wave, the
first derivative of the selected high quality wave, and the second
derivative of the selected high quality wave according to some
embodiments.
[0064] FIG. 26 depicts example tree structures of an example
empirical blood pressure calculation model according to some
embodiments.
[0065] FIG. 27 depicts an example bi-Gaussian mixture model for a
PPG signal according to some embodiments.
[0066] FIG. 28 depicts an example PPG signal including multiple
reflections according to some embodiments.
[0067] FIG. 29A-C depict block diagram of example sensor systems
according to some embodiments.
[0068] FIG. 30A-E depict arrangement of energy transmitters and
energy receivers in a sensor system according to some
embodiments.
[0069] FIG. 31A-B depict experimentations carried out to obtain an
optimum location of a sensor system, when the sensor system is used
for measuring states of a radial artery on a left wrist of a
subject.
[0070] FIG. 32 depicts a flowchart of an example method of method
for separating signals from different sources according to some
embodiments.
[0071] FIG. 33 depicts a flowchart of an example method of
calculating blood pressure according to some embodiments.
DETAILED DESCRIPTION
[0072] Biometrics including blood metrics may be measured by
minimally invasive procedures to address medical conditions such as
diabetes or in the diagnosis and discovery of diseases.
Minimal-invasive procedure based devices may have the advantages of
reducing costs and decreasing the need for invasive methods,
thereby increasing the comfort and well-being of users and
patients. Even though these devices have revolutionized patient
care, they have only been described in, and approved for, medical
purposes. Minimal-invasive procedure based devices are usually out
of reach for the general public because they are designed for
medical uses rather than non-medical purposes such as fitness,
well-being, and quality of life.
[0073] Personal devices such as sphygmomanometers or pulse
oximeters measure blood pressure or oxygen levels, respectively, on
a per-request basis. They usually cannot measure blood metrics real
time or periodically. Real-time blood metrics data (e.g., high
resolution measurements, or measurements over long periods of time)
may allow these devices to facilitate users monitoring and
controlling their energy levels and/or metabolism. Nutritionists,
people suffering from obesity, people desiring to eat healthier,
fitness enthusiasts, semi-professional athletes, people likely to
have hypoglycemia, or the vast majority of the general population
can benefit from these devices.
[0074] In various embodiments, a multispectral blood metric
measurement apparatus monitors blood metrics, fitness, and/or
metabolism levels of various users in a non-invasive manner. The
multispectral blood metric measurement apparatus may be, for
example, wearable technology. The multispectral blood metric
measurement apparatus may measure any number of blood metrics.
Blood metrics may include, for example, various nutrient blood
concentrations. Blood metrics may be, for example, monitored,
stored, tracked, and/or analyzed.
[0075] FIG. 1 is a block diagram illustrating an example
environment 100 utilizing a multispectral blood metrics measurement
apparatus 102 in accordance with various embodiments. As shown in
FIG. 1, the example environment 100 may comprise a multispectral
blood metrics measurement apparatus 102, one or more user systems
104, an optional analysis system 108, and a computer network 106
communicatively coupling together each of the multispectral blood
metrics measurement apparatus 102, one or more user devices 110,
112, and 114 (depicted as user system 104), and/or the analysis
system 108. As shown, a user system 104 may include a smartphone
110 (e.g., iPhone.RTM.), a computer 112 (e.g., a personal
computer), and/or a tablet 114 (e.g., iPad.RTM.), through the
computer network 106 (e.g., a Bluetooth.RTM. 4.0 personal area
network), can either interact directly or indirectly with the blood
metrics measurement apparatus 102.
[0076] The multispectral blood metrics measurement apparatus 102
may measure health or metabolism predictors non-invasively. The
multispectral blood metrics measurement apparatus 102 may measure
blood metrics such as concentrations of various nutrients over
time, deliver energy into tissues of various body parts of a user,
track a user's behavior pattern, detect motion, communicate various
blood metric measurements, and/or receive a user's instructions.
For instance, through the computer network 106, the multispectral
blood metrics measurement apparatus 102 may transmit one or more
blood metric measurements to, or receive instructions from, the
user system 104 or the multispectral blood measurement system 108
such as which health or metabolism predictor to measure.
[0077] In some embodiments, the multispectral blood metric 102
measurement apparatus may project energy into tissue of a user and
detect energy reflected from and/or transmitted through tissue of
the user (e.g., the wearer of the multispectral blood metric
measurement apparatus 102). The projected energy may be at multiple
wavelengths that are associated with the blood metrics of interest
to a user. The detected energy may be a fraction of the energy that
is projected into the tissue. Energy at different wavelengths may
be absorbed at a different rate that is related to a user's body
state. The user's body state (e.g., heart rate, blood pressure,
nutrient level, or the like) determines the amount of absorbed
energy. Accordingly, energy at different wavelengths may be
absorbed at different levels by a user's body. The fraction of
energy received (e.g., that is reflected by the tissue or
transmitted through the tissue) may be used to generate signals
(e.g., composite signals) at different levels. These signals may
provide information of the user's body state. This information may
be obtained by analyzing waveforms of the signal in the time domain
and/or the frequency domain.
[0078] In various embodiments, the multispectral blood metric
measurement apparatus 102 may measure many metrics, including, but
not limited to, skin conductivity, pulse, oxygen blood levels,
blood pressure, blood glucose level, glycemic index, insulin index,
Vvo2max, fat body composition, protein body composition, blood
nutrient level (e.g., iron), body temperature, blood sodium levels,
and/or naturally-produced chemical compound level (e.g., lactic
acid). Nutrients may be determined based on the blood metrics to be
measured. Nutrients may be measured may include, but are not
limited to, glucose, hemoglobin, triglycerides, cholesterol,
bilirubin, protein, albumin (i.e., egg white), and/or electrolytes
(e.g., sodium, potassium, chloride, bicarbonate, etc.)
[0079] It will be appreciated that the user's body state may change
dynamically and energy at a wavelength may be absorbed differently
by a user over the time. By monitoring and tracking detected energy
from the user's body, a user's health or condition may be more
tracked. Systems and methods described herein may monitor and store
blood metrics including concentrations of various nutrients. A
user's history health records may be generated by using blood
metrics measured at different times. In some embodiments, blood
metrics measured a given time point may be compared to the history
health records to detect any abnormal health conditions. The
multispectral blood metric measurement apparatus may comprise a
user interface where a user may input blood metrics of interest, be
presented with various health reports, and/or be alerted with
abnormal health conditions.
[0080] A user may comfortably wear a multispectral blood metric
measurement apparatus 102 over time. The multispectral blood metric
measurement apparatus 102 may comprise lightweight components. The
multispectral blood metric measurement apparatus 102 may be made of
hypoallergenic materials. The multispectral blood metric
measurement apparatus 102 may be flexibly built so that it could
fit various body parts (e.g., wrist, earlobe, ankle, or chest) of a
user.
[0081] In accordance with some embodiments, the computer network
106 may be implemented or facilitated using one or more local or
wide-area communications networks, such as the Internet, WiFi
networks, WiMax networks, private networks, public networks,
personal area networks ("PAN"), and the like. In some embodiments,
the computer network 106 may be a wired network, such as a twisted
pair wire system, a coaxial cable system, a fiber optic cable
system, an Ethernet cable system, a wired PAN constructed with USB
and/or FireWire connections, or other similar communication
network. Alternatively, the computer network 106 may be a wireless
network, such as a wireless personal area network, a wireless local
area network, a cellular network, or other similar communication
network. Depending on the embodiment, some or all of the
communication connections with the computer network 106 may utilize
encryption (e.g., Secure Sockets Layer [SSL]) to secure information
being transferred between the various entities shown in the example
environment 100.
[0082] Although FIG. 1 depicts a computer network 106 supporting
communication between different digital devices, it will be
appreciated that the multispectral blood metrics measurement
apparatus may be directly coupled (e.g., over a cable) with any or
all of the user devices 110, 112, and 114.
[0083] The user devices 110-114 may include any digital device
capable of executing an application related to measuring blood
metrics, presenting an application user interface through a display
and/or communicating with various entities in the example
environment 100 through the computer network 106. For instance,
through the computer network 106, the user device 110 may receive
one or more blood metric measurements from the multispectral blood
metrics measurement apparatus 102, track and store the blood metric
measurements, analyze the blood metric measurements, and/or provide
recommendations based on the blood metric measurements. An
application user interface may facilitate interaction between a
user of the user system 104 and an application running on the user
system 104.
[0084] In various embodiments, any of user devices 110-114 may
perform analysis of the measurements from the multispectral blood
metrics measurement apparatus 102, display results, provide
reports, display progress, display historic readings, track
measurements, track analysis, provide alerts, and/or the like.
[0085] The analysis system 108 may be any form of digital device
capable of executing an analysis application for analyzing and/or
measuring blood metrics. In some embodiments, the analysis system
108 may generate reports or generate alerts based on analysis or
measurement of blood metrics. For instance, through the computer
network 106, the analysis system 108 may receive one or more blood
metric measurements from the multispectral blood metrics
measurement apparatus 102, track and store blood metric
measurements, analyze blood metric measurements, and/or provide
recommendations based on the analysis. An application programming
interface may facilitate interaction between a user, the user
devices 110-114, and/or the multispectral blood metrics measurement
apparatus 110 with the analysis system 108.
[0086] Computing devices (e.g., digital devices) may include a
mobile phone, a tablet computing device, a laptop, a desktop
computer, personal digital assistant, a portable gaming unit, a
wired gaming unit, a thin client, a set-top box, a portable
multi-media player, or any other type of network accessible user
device known to those of skill in the art. Further, the analysis
system 108 may comprise of one or more servers, which may be
operating on or implemented using one or more cloud-based services
(e.g., System-as-a-Service [SaaS], Platform-as-a-Service [PaaS], or
Infrastructure-as-a-Service [IaaS]).
[0087] It will be understood that for some embodiments, the
components or the arrangement of components may differ from what is
depicted in FIG. 1.
[0088] Each of the multispectral blood metrics measurement
apparatus 102, one or more user devices 110, 112, and 114, and the
analysis system 108 may be implemented using one or more digital
devices. An exemplary digital device is described regarding FIG.
8.
[0089] FIG. 2 is a block diagram illustrating an exemplary
multispectral blood metrics measurement apparatus 200, such as the
multispectral blood metrics measurement apparatus 102 illustrated
in FIG. 1. The multispectral blood metrics measurement apparatus
200 comprises an analyzer 202, an energy transmitter 204, and an
energy receiver 206. Various embodiments may comprise a wearable
member. The wearable member may include, for example, a bracelet,
glasses, necklace, ring, anklet, belt, broach, jewelry, clothing,
or any other member of combination of members that allow the
multispectral blood metrics measurement apparatus 200 to be close
to or touch a body of the wearer.
[0090] The energy transmitter 204 and the energy receiver 206 may
be secured to the wearable member such that the energy transmitter
and the energy receiver may make contact or be in proximity with
tissues (e.g., skin) of a user. The analyzer 202 may be coupled to
the energy transmitter 204 and the energy receiver 206. In further
embodiments, the multispectral blood metrics measurement apparatus
200 may comprise a communication module (not shown). The
communication module may be coupled to the analyzer 202. The blood
metrics measurement apparatus 200 may further comprise a driver
(not shown) and a power source (not shown). The driver may be
coupled to the energy transmitter 204 and the analyzer 202. The
analyzer 202 may be coupled to the energy transmitter 204 via the
driver. The power source may be coupled to the energy transmitter
204 via the driver. The blood metrics measurement apparatus 200 may
further comprise an Analog-to-Digital Converter ("ADC") (not
shown). The ADC may be coupled to the energy receiver 206 and the
analyzer 202. In some embodiments, the blood metrics measurement
apparatus 200 may comprise a motion sensor (e.g., an accelerometer,
gyroscope, global positioning system, or the like) (not shown). The
motion sensor may be coupled to the analyzer 202.
[0091] In various embodiments, the energy transmitter 204 emits
energy including, but not limited to, light, into the body of the
user. The energy produced by the energy transmitter may be in the
direction of entering tissues. For example, the energy produced by
the energy transmitter 204 is in a direction 251 entering the
tissue 210. In some embodiments, the energy transmitter 204 emits
energy or light at different wavelengths. The energy transmitter
204 may comprise any number of light emission diodes ("LEDs"). In
some embodiments, the energy transmitter 204 comprises at least two
LEDs. Each LED may be configured to emit energy at one or more
wavelengths. In another example, each LED may emit light with a
peak wavelength centered around a wavelength. In one example, the
energy transmitter 204 may emit light with a peak wavelength
centered around 500 nm to 1800 nm.
[0092] Each wavelength may correspond to one or more blood metrics
of interest and/or one or more nutrients. It will be appreciated
that different components of the blood and/or different nutrients
may absorb energy at different wavelengths. In various embodiments,
a controller, driver, analyzer 202, or the like may receive a blood
metric or nutrient of interest (e.g., from a user of the
multispectral blood metrics measurement apparatus 200 and/or a user
device not shown). The controller, driver, analyzer 202 or the like
may associate the blood metric and/or nutrient of interest with one
or more wavelengths and configure one or more of the LEDs to emit
energy of at least one of the one or more wavelengths. For example,
the analyzer 202 may command the driver to deliver electric power
to one LED that is configured to emit light at the desired
wavelength.
[0093] The energy receiver 206 may detect energy associated with
the energy provided by the LEDs from tissues (e.g., skin) of the
user. In this example, received and/or detected energy is in the
direction 252 that leaves from the tissue 210. In various
embodiments, the energy receiver 206 may detect energy from the
body of the user that is a fraction of the energy produced by the
energy transmitter 204.
[0094] The energy transmitter 204 and the energy receiver 206 may
be configured such that the energy receiver 206 detects reflected
energy from tissues of the user of the multispectral blood metrics
measurement apparatus 200. For example, the energy transmitter 204
and the energy receiver 206 may be configured to be disposed on one
surface or side of a user's tissue. The energy transmitter 204 and
the energy receiver 206 may be configured such that the energy
receiver 206 detects energy from the energy transmitter 204 that
passes through or reflects from the user's tissues. In some
embodiments, the energy transmitter 204 and the energy receiver 206
may be configured to be disposed on different (e.g., opposite)
surfaces or sides of a users' tissue.
[0095] Energy detected from tissues of a user may be detected by
the energy receiver 206. The energy receiver 206 may be configured
to generate a signal in response to the detected energy. In some
embodiments, the energy receiver 206 may be triggered by the energy
received to generate an output which may be dependent or partially
dependent upon the amount of energy received. The energy receiver
206 may be configured to generate a signal (e.g., an electric
current, or an electric voltage) in response to the energy received
from the tissues.
[0096] The signal generated by the energy receiver 206 may be
associated with one or more blood metrics and/or nutrients of
interest. Energy at different wavelengths may be absorbed at a
different rate that is related to a user's body state. The user's
body state (e.g., heart rate, blood pressure, nutrient level, or
the like) may determine the amount of energy absorbed by the body.
Accordingly, energy from the user's body at different wavelengths
may be detected at different levels thereby causing different
responses of the energy receiver 206. The energy receiver 206 may,
for example, output signals based on the level of the energy
received.
[0097] The energy receiver 206 may provide information associated
with the user's body state. Blood metric information may be
determined (e.g., by the analyzer 202) from the output signal of
the energy receiver 206.
[0098] The energy receiver 206 may comprise a set of photodetectors
(e.g., a photo diode, or a photo transistor) which are configured
to output a signal dependent upon photons or the like from the
energy transmitter 204 that passed through tissues of the user. As
discussed herein, in some embodiments, the multispectral blood
metrics measurement apparatus 200 may also include a pressure
sensor. The pressure sensor may be configured to generate, detect,
and/or measure non-optical signals. For example, the pressure
sensor may non-invasively and continuously generate, detect, and/or
measure pressure pulse signals. In some embodiments, the pressure
sensor measures pressure pulse waveforms associated with arterial
pressure of one or more arteries of a user. For example,
multispectral blood metrics measurement apparatus 200 may include
the energy transmitter 204 to generate optical signals and the
energy receiver 206 to receive optical signals but not the pressure
sensor. Alternately, the multispectral blood metrics measurement
apparatus 200 may include the pressure sensor that may produce
pressure on the user's body and/or receive measurements based on
that pressure but not the energy transmitter 204 or the energy
receiver 206 to receive optical signals. In this example, the
pressure sensor may be considered to be an energy receiver and the
energy transmitter is the body of the wearer.
[0099] In various embodiments, the output signal of the energy
receiver 206 is a composite of multiple signals. Each signal of the
composite may be associated with energy at a wavelength which may
be a portion (or fraction) of the total energy emitted by the
energy transmitter 204.
[0100] The energy transmitter 204 may be configured to generate
energy at a set of wavelengths. In some embodiments, the energy
transmitter 204 is configured to generate energy such that energy
at different wavelengths is generated sequentially and/or
periodically. The energy transmitter 204 may be configured to
generate energy at each particular wavelength until energy at all
wavelengths of the set is generated. The period of time for the
energy transmitter 204 to generate energy at all wavelengths is a
generation period. Subsequent to completion of the generation
period, the energy transmitter 204 may start a new generation
period thereby allowing multiple measurements.
[0101] In some embodiments, the blood metrics measurement apparatus
200 may be or include the blood metrics measurement apparatus 102
described with regard to FIG. 1.
[0102] FIG. 3 illustrates an exemplary flow diagram of a method 300
of measuring blood metrics in accordance with an embodiment of the
present application. At step 302, energy transmitter 204 generates
and delivers energy at different wavelengths into tissues (e.g.,
skin) of a user. Different wavelengths may be associated with any
number of nutrients, which may be associated with the blood metrics
to be measured.
[0103] In some embodiments, a user may define various blood metrics
and/or nutrients to be measured. Referring back to FIG. 1, a list
of blood metrics and/or nutrients may be selected from a user
interface (e.g., displayed on an interface of the multispectral
blood metrics measurement apparatus 102, on a user device 110-114,
or through the analysis system 108). The user may select one or
more blood metrics and/or nutrients to be measured.
[0104] In some embodiments, a user may define a set of blood
metrics to be measured on the user system 104; the multispectral
blood metrics measurement apparatus 102 may provide the blood
metrics to be measured to the user system 104. For example, on any
device of the user system 104, a user may define one or more blood
metrics by selecting one or more blood metrics from a list of blood
metrics provided, for example, via the user interface.
[0105] As discussed herein, the multispectral blood metrics
measurement apparatus 200 may measure, but is not limited to, skin
conductivity, pulse, oxygen blood levels, blood pressure, blood
glucose level, glycemic index, insulin index, Vvo2max, fat body
composition, protein body composition, blood nutrient level (e.g.,
iron), body temperature, blood sodium levels, or naturally-produced
chemical compound level (e.g., lactic acid). Nutrients may be
determined based on the blood metrics to be measured. The
multispectral blood metrics measurement apparatus 200 may measure
nutrients, but is not limited to, glucose, hemoglobin,
triglycerides, cholesterol, bilirubin, protein, albumin (i.e., egg
white), or electrolytes (e.g., sodium, potassium, chloride,
bicarbonate, or the like). The multispectral blood metrics
measurement apparatus 200 may also measure oxygen, cortisol, and
Hematocrit, for example (e.g., blood components).
[0106] In various embodiments, one or more wavelengths may be
associated with a nutrient or a combination of blood components or
molecules. In some embodiments, a number of wavelengths generated
by the energy transmitter 204 are the number of blood components or
molecules to be measured plus one. For example, when a total number
of five (5) blood components and/or molecules are to be measured, a
total number of six (6) wavelengths may be determined based on the
blood components and/or molecules to be measured. Similarly, it
will be appreciated that one or more wavelengths may be associated
with a nutrient or a combination of nutrients. In some embodiments,
a number of wavelengths generated by the energy transmitter 204 are
the number of nutrients to be measured plus one. For example, when
a total number of three (3) nutrients are to be measured, a total
number of four (4) wavelengths may be determined based on the
nutrients to be measured.
[0107] In some embodiments, the multispectral blood metrics
measurement apparatus 200, user devices 110-114, and/or analysis
system 108 may comprise a reference table of blood components,
molecules, and/or nutrients and wavelengths corresponding to the
blood components, molecules, and/or nutrients. A wavelength may be
unique to or more generally associated with a nutrient. A reference
wavelength may be unique to or more generally associated with a
combination of nutrients to be measured. As such, wavelength(s) may
be determined by looking up each blood components, molecules,
and/or nutrients that is to be measured. Energy at the determined
wavelengths may be transmitted by the energy transmitter 204 into
the body.
[0108] In various embodiments, in a predetermined time duration,
energy at all desired wavelengths may be generated. For each
wavelength, the corresponding energy may be generated for a time
period equal to a predetermined time duration divided by the number
of wavelengths. For example, four (4) wavelengths may be determined
and the predetermined time duration is two (2) seconds.
Accordingly, energy for each wavelength may be generated for a
duration of half (0.5) second.
[0109] At step 304, the energy receiver 206 detects a fraction of
the energy transmitted into the user's tissue by the energy
transmitter 204. The energy receiver 206 may generate a signal
based on the fraction of energy detected (e.g., based on the amount
of the energy detected). In one example, energy detected at step
304 may be a fraction of the energy generated at step 302 reflected
by the tissue. Energy detected at step 302 may be a fraction of the
energy generated at step 302 that passes through the tissue (e.g.,
other undetected energy may be absorbed by tissue and/or otherwise
blocked). The output signal of the energy receiver 206 may be an
electric current or an electric voltage, of which the amplitude may
be related to the amount of the energy detected. In various
embodiments, steps 302 and 304 are performed simultaneously. That
is, energy generation and detection may be performed approximately
simultaneously.
[0110] In various embodiments, the output signal generated by the
energy receiver 206 is a composite signal of multiple signals, each
of which corresponds to one or more wavelengths. The output signal
produced at step 306 may be divided into individual signals, each
of which is may be associated with one or more wavelengths.
[0111] In various embodiments, analysis of the signals from the
energy receiver 206 may identify abnormal measurements. For
example, each of the measurement may be compared to a predetermined
value. If the difference between the measurement and the
predetermined value is above (or below) a threshold, then the
measurement may be determined to be abnormal. An abnormal value may
trigger additional analysis or an alert. In some embodiments, an
abnormal value is ignored (e.g., as possibly effected by noise
caused by movement of the energy transmitter 204 and/or the energy
receiver 206). In various embodiments, the abnormal value may be
discounted (e.g., the weight of the value reduced). The degree of
discount may be based, for example, on information from an
accelerometer (e.g., a large acceleration may indicate that the
abnormal value should be significantly discounted) and/or based on
historical values. It will be appreciated that the degree of
discount may be based on any number of factors.
[0112] In some embodiments, measurements may be averaged over a
period of time. A Kalman filer (e.g., a nonlinear, unscented Kalman
filter) may be applied to any number of measurements or averaged
measurements. A motion measurement (e.g., a measurement by an
accelerometer) may be considered. Upon determining a measurement is
abnormal, the motion measurement for that time point may be
inspected. A large measurement may indicate large vibrations or
accelerations that corroborate that the measurement may be
abnormal. Measurements collected in such situations are likely to
have significant electrical noises.
[0113] At step 308, the analyzer 202 may analyze signals from the
energy receiver 206 analyzed in the frequency domain to determine
blood metrics. Concentration of a nutrient in the blood may
subsequently be determined. In some embodiments, signals may be
provided to a bandpass filter that separates AC components from DC
components. An AC component may represent signal variation at the
cardiac frequency and a DC component may represent the average
overall transmitted light intensity. In some embodiments, a heart
rate and/or oxygen saturation, SpO.sub.2 may be determined. The
heart rate may be determined, for example, by averaging the maximum
frequency to determine the rate of cardiac beats in a predetermined
amount of time. The oxygen saturation SpO.sub.2 may be determined
according to Equation (1):
S.sub.pO.sub.2=110-25.times.R (1),
where R is the ratio of a red and infrared normalized transmitted
light intensity. R may be determined according to Equation (2):
R = A C R / D C R A C IR / D C IR , ( 2 ) ##EQU00001##
where the AC.sub.R is the AC component of the detected energy
corresponding to a wavelength (e.g., red light), DC.sub.R is the DC
component of the detected energy corresponding to the wavelength
(e.g., red light), AC.sub.IR is the AC component of the detected
energy corresponding to a different wavelength (e.g., infrared
light), and DC.sub.IR is the DC component of the detected energy
corresponding to the different wavelength (e.g., infrared light).
In some embodiments, the AC component may be selected as the
highest spectral line in the cardiac frequency band. Waveform
analysis may be performed to determine the R-R interval defined by
two successive AC components, an elapsed interval and the
peturbation, if there is any. It will be appreciated that analysis
may be performed by the analyzer 202 and/or any other digital
device (e.g., any of users devices 110-114 or analysis system
108).
[0114] At step 308, state space estimation and progression may be
performed to determine blood metrics. A system may be modeled
according to Equation (3):
x(n+1)=f[x(n)]+u(n)
y(n)=h[x(n)]+v(n) (3),
where x(n) represents the state of the system, u(n) is process
noise, y(n) is the vector of the observed signals, and v(n) is the
measurement noise.
[0115] Table 1 lists one or more parameters for x(n) as well as
their initial value in some embodiments:
TABLE-US-00001 TABLE 1 Parameter Symbol Initial Value Cardiac
frequency f.sub.HR 1 Hz Cardiac phase .theta..sub.HR 0 Cardiac
harmonic I.sub.Harmonic.sup.HR 0 amplitude Cardiac Pulse P.sub.HR 1
Pressure Point Blood Pressure B.sub.Point 1 Respiratory f.sub.Resp
0.3 Hz frequency Respiratory phase .theta..sub.Resp 0 Wavelength i
= 1 . . . N I.sub..lamda..sub.i.sup.AC 0.5 max_value AC peak
amplitude Wavelength i = 1 . . . N pos.sub..lamda..sub.i.sup.AC
Corresponding FFT AC peak location bin to 1 Hz Wavelength i = 1 . .
. N I.sub..lamda..sub.i.sup.DC 0.5 max_value DC Wavelength i = 1 .
. . N I.sub..lamda..sub.i.sup.p2p 1 ADC read p2p amplitude
Wavelength i = 1 . . . N .tau..sub..lamda..sub.i.sup.rise 0.1 sec
rise time Wavelength i = 1 . . . N C.sub..lamda..sub.i 1
Significance coefficient Wavelength i = 1 . . . N
T.sub..lamda..sub.i.sup.HRV 1 sec HRV Best Ratio pH BR.sub.pH 2
Best Ratio pCO2 BR.sub.pCO2 3 Best Ratio pHCO3-- BR.sub.pHCO3.sub.-
4 Acceleration I.sub.move 0 magnitude GPS velocity |v|.sub.GPS 0
GPS altitude |alt|.sub.GPS 0 GPS acceleration |a|.sub.GPS 0 GPS
incline |incline|.sub.GPS 0 Restfulness Rest 0 Hydration Hyd 0
Systolic Blood SBP 120 mmHg Pressure Diastolic Blood DBP 80 mmHg
Pressure End tidal CO2 ETCO2 40 mmHg Blood Carbon SpCO 0%
Monoxide
[0116] Table 2 lists one or more parameters for y(n) as well as
their initial value in some embodiments:
TABLE-US-00002 TABLE 2 Parameter Symbol Initial Blood pH pH 7.35
Blood PCO2 pCO.sub.2 24 mmol Blood PO2 pO.sub.2 24 mmol Blood
PHCO3-- pHCO.sub.3.sup.- 24 mmol Blood Glucose
pC.sub.6H.sub.12O.sub.6 3 mmol Cardiac Frequency f.sub.HR 1 Point
Blood Pressure P.sub.Point 1 Respiratory f.sub.Resp 0.3 Hz
Frequency GPS velocity |v|.sub.GPS 0 GPS altitude |alt|.sub.GPS 0
GPS acceleration |a|.sub.GPS 0 GPS incline |incline|.sub.GPS 0
[0117] Table 3 lists the state space model F(X(n)) between the
parameters listed in Table 1 and Table 2 in some embodiments, where
the energy wavelengths comprise 880 nm, 631 nm, 1450 nm, and 1550
nm:
TABLE-US-00003 TABLE 3 Name Symbol Equation Cardiac frequency
f.sub.HR bin_to _freq ( c .lamda. i pos .lamda. i AC c .lamda. i )
##EQU00002## Cardiac .theta..sub.HR .theta..sub.HR(n - 1) +
f.sub.s.sup.-1 * .omega..sup.*, where .omega..sup.* .di-elect cons.
[.omega._min, .omega._max] phase Cardiac harmonic amplitude
I.sub.Harmonic.sup.HR c .lamda. i I .lamda. i p 2 p c .lamda. i
##EQU00003## Cardiac Pulse Pressure P.sub.HR ( c .lamda. i .tau.
.lamda. i rise c .lamda. i ) ^ - 1 ##EQU00004## Point Blood
Pressure P.sub.Point .tau..sub..lamda..sub.1.sup.rise.sup.-1
Respiratory f.sub.Resp 3) Respiratory and Heart Rate State Models:
The fluctuations frequency in the respiratory rate .omega..sub.r(n)
and fluctuations in the heart rate .omega..sub.ca(n) that are not
due to RSA are both modeled as a first-order autoregressive process
with a mean and mild non- linearity that limit the frequencies to
know physiologic ranges .omega..sub.r(n + 1) = .omega..sub.r +
.alpha..sub.r{s.sub.r[.omega..sub.r(n)] - .omega..sub.r} +
u.sub..omega..sub.r(n) (15) .omega..sub.ca(n + 1) = .omega..sub.c +
.alpha..sub.c{s.sub.c[.omega..sub.ca(n)] - .omega..sub.c} +
u.sub..omega..sub.ca(n) (16) where .omega..sub.r and .omega..sub.c
are the a priori estimates of the expected res- piratory and
cardiac frequencies, respectively; .alpha..sub.r and .alpha..sub.c
con- trol the bandwidth of the frequency fluctuations; and
u.sub..omega..sub.r(n) and u.sub..omega..sub.ca(n) are white noise
processes that model the random variation in the respiratory and
cardiac frequencies, respectively. The instantaneous respiratory
and heart rates in units of Hz are then f r ( n ) = 1 2 .pi. T s s
T [ .omega. r ( n ) ] ( 17 ) ##EQU00005## f c ( n ) = 1 2 .pi. T s
s c [ .omega. c ( n ) ] . ( 18 ) ##EQU00006## Respiratory
.theta..sub.Resp .theta..sub.Resp(n - 1) + f.sub.s.sup.-1 *
.omega..sup.*, where .omega..sup.* .di-elect cons. [.omega._min,
.omega._max] phase .lamda. = 880 nm I.sub..lamda..sub.i.sup.AC From
FFT AC peak .lamda. = 880 nm pos.sub..lamda..sub.i.sup.AC From FFT
DC .lamda. = 880 nm I.sub..lamda..sub.i.sup.DC From Waveform
analysis p2p amplitude .lamda. = 880 nm I.sub..lamda..sub.i.sup.p2p
From Waveform analysis rise time .lamda. = 880 nm
.tau..sub..lamda..sub.i.sup.rise From Waveform analysis signal
trend .lamda. = 880 nm c.sub..lamda..sub.i From Waveform analysis
Significance coefficient .lamda. = 880 nm
T.sub..lamda..sub.i.sup.HRV From Waveform analysis HRV .lamda. =
631 nm I.sub..lamda..sub.i.sup.AC From Fast Fourier Transformation
("FFT") AC peak .lamda. = 631 nm pos.sub..lamda..sub.i.sup.AC From
FFT DC .lamda. = 631 nm I.sub..lamda..sub.i.sup.DC From Waveform
analysis p2p amplitude .lamda. = 631 nm I.sub..lamda..sub.i.sup.p2p
From Waveform analysis rise time .lamda. = 631 nm
.tau..sub..lamda..sub.i.sup.rise From Waveform analysis signal
trend .lamda. = 631 nm c.sub..lamda..sub.i From Waveform analysis
Significance coefficient .lamda. = 631 nm
T.sub..lamda..sub.i.sup.HRV From Waveform analysis HRV .lamda. =
1450 nm I.sub..lamda..sub.i.sup.AC From FFT AC peak .lamda. = 1450
nm pos.sub..lamda..sub.i.sup.AC From FFT DC .lamda. = 1450 nm
I.sub..lamda..sub.i.sup.DC From Waveform analysis p2p amplitude
.lamda. = 1450 nm I.sub..lamda..sub.i.sup.p2p From Waveform
analysis rise time .lamda. = 1450 nm
.tau..sub..lamda..sub.i.sup.rise From Waveform analysis signal
trend .lamda. = 1450 nm c.sub..lamda..sub.i From Waveform analysis
Significance coefficient .lamda. = 1450 nm
T.sub..lamda..sub.i.sup.HRV From Waveform analysis HRV .lamda. =
1550 nm I.sub..lamda..sub.i.sup.AC From FFT AC peak .lamda. = 1550
nm pos.sub..lamda..sub.i.sup.AC From FFT DC .lamda. = 1550 nm
I.sub..lamda..sub.i.sup.DC From Waveform analysis p2p amplitude
.lamda. = 1550 nm I.sub..lamda..sub.i.sup.p2p From Waveform
analysis rise time .lamda. = 1550 nm
.tau..sub..lamda..sub.i.sup.rise From Waveform analysis signal
trend .lamda. = 1550 nm c.sub..lamda..sub.i From Waveform analysis
Significance coefficient .lamda. = 1550 nm
T.sub..lamda..sub.i.sup.HRV From Waveform analysis HRV Best Ratio
BR.sub.pH Device Calibration pH Best Ratio BR.sub.pCO2 Device
Calibration pCO2 Best Ratio BR.sub.pHCO3- Device Calibration pHCO3-
Acceleration I.sub.move From Accelerometer magnitude GPS velocity
|v|.sub.GPS From GPS GPS altitude |alt|.sub.GPS From GPS GPS
|a|.sub.GPS From GPS acceleration GPS incline |incline|.sub.GPS
From GPS
[0118] Table 4 lists Y(n)=H(x(n)):
TABLE-US-00004 TABLE 4 Name Symbol Equation Blood pH pH 6.1 + log (
pHCO 3 - 0.03 pCO 2 ) ##EQU00007## Blood PCO2 pCO.sub.2 Hb CO 2 -
Hb Hb * I .lamda. CO 2 AC * I .lamda. 1 DC / ( I .lamda. 1 AC * I
.lamda. CO 2 DC ) Hb CO 2 - CO 2 CO 2 + ( CO 2 Hb - Hb Hb ) * I
.lamda. CO 2 AC * I .lamda. 1 DC / ( I .lamda. 1 AC * I .lamda. CO
2 DC ) ##EQU00008## Blood PO2 pO.sub.2 Hb O 2 - Hb Hb * I .lamda. O
2 AC * I .lamda. 1 DC / ( I .lamda. 1 AC * I .lamda. O 2 DC ) Hb O
2 - O 2 O 2 + ( O 2 Hb - Hb Hb ) * I .lamda. O 2 AC * I .lamda. 1
DC / ( I .lamda. 1 AC * I .lamda. O 2 DC ) ##EQU00009## Blood
PHCO3- pHCO.sub.3.sup.- Hb HCO 3 - - Hb Hb * I .lamda. HCO 3 - AC *
I .lamda. 1 DC / ( I .lamda. 1 AC * I .lamda. HCO 3 - DC ) Hb HCO 3
- - HCO 3 - HCO 3 - + ( HCO 3 - Hb - Hb Hb ) * I .lamda. HCO 3 - AC
* I .lamda. 1 DC / ( I .lamda. 1 AC * I .lamda. HCO 3 - DC )
##EQU00010## Blood Glucose pC.sub.6H.sub.12O.sub.6 As above Cardiac
f.sub.HR As in f(x(n)) Frequency Point Blood P.sub.Point As in
f(x(n)) Pressure Respiratory f.sub.Resp As in f(x(n)) Frequency GPS
velocity |v|.sub.GPS As in f(x(n)) GPS altitude |alt|.sub.GPS As in
f(x(n)) GPS |a|.sub.GPS As in f(x(n)) acceleration GPS incline
|incline|.sub.GPS As in f(x(n))
[0119] As illustrated in Tables 3 and 4, by generating energy at
different wavelengths, one or more blood metrics may be determined
from the detected energy. For example, cardiac frequency, cardiac
phase, cardiac harmonic amplitude, cardiac pulse pressure, point
blood pressure, respiratory frequency, respiratory phase, blood pH,
blood pCO.sub.2, blood pHCO.sub.3-, or blood glucose, may be
determined.
[0120] FIG. 4 illustrates an exemplary apparatus 400 for measuring
various blood metrics in accordance with an embodiment of the
present application. The apparatus 400 comprises a central unit
402, a sensor array 404, and a coupling means 408. The central unit
402 may be a wearable member made of elastic and/or flexible
hypoallergenic wearable material.
[0121] In the illustrated example, the sensor array 404 is coupled
to the central unit 402. The sensor array 404 may comprise any
number of energy transmitters and/or energy receivers. The sensor
array 404 may be detached from the central unit 402. In some
embodiments, the sensor array 404 may be mechanically and
electrically coupled to the central unit 402. The sensor array 404
comprises various illumination (e.g., near infra-red, infra-red, or
short infra-red) and sensing array. The sensor array 404 may
further comprise conductivity and/or capacity sensors. Different
sensor array 404 may be provided to measure different blood
metrics.
[0122] The central unit 402 may comprise an analyzer. In some
embodiments, the central unit comprises an analyzer, one or more
energy transmitter(s), and one or more energy receiver(s). The
central unit 402 may further comprise a communication module and/or
a battery compartment. The coupling means 408 are mounting screw
holes in FIG. 4, however, it will be appreciated that coupling
means may be optional. Further, coupling means 408 may include any
kind of means including a clip, hook, switch, expanding fabric,
adhesive, or the like. One of ordinary skill in the art would
understand that other mounting means may be used.
[0123] The apparatus 400 further comprises a micro-USB port 406 to
allow for communication with a digital device and a screen 410.
Various user interfaces (e.g., lights, a display, touchscreen, or
the like) may be displayed on the screen 410.
[0124] In some embodiments, the apparatus 400 may be or include the
blood metrics measurement apparatus 200 described with regard to
FIG. 2, or the blood metrics measurement apparatus 102 described
with regard to FIG. 1.
[0125] FIGS. 5-7 are screenshots illustrating an example of
presenting health analysis over a user interface in accordance with
various embodiments. Various embodiments may store blood metrics
and/or nutrient measurements. FIG. 5 illustrates a display 500 of
an assessment of a current health index derived from data collected
from or with a multispectral blood metrics measurement apparatus in
various embodiments. The display may appear on the user's
smartphone, for example. In various embodiments, the analyzer 202
or any digital device may analyze measurements collected over time
to generate a health score that can be compared to a health
threshold to provide qualitative and/or quantitative scoring.
Similarly, the analyzer 202 or any digital device may analyze
measurements recently collected to generate a current score that
can be compared to a current health threshold to provide
qualitative and/or quantitative scoring.
[0126] In some embodiments, a user interface may display a health
score 502, an option for details regarding the health score 504, a
current score 506, an option for details regarding the current
score 508, a recommendation 510, a settings option 512, and a
history of measurements 514. Options for details 504 and 506 may
describe the metrics as well as the values of the metrics that went
into the health score 504 and the current score 506,
respectively.
[0127] In some embodiments, there is a recommendation engine
configured to retrieve recommendations 510 based on the health
score 504 and/or the current score 506. The settings option 512 may
allow the user to configure metrics to be tracked and set alerts.
In some embodiments, the user may utilize the settings options 512
to secure the information (e.g., encrypt the information and/or set
passwords). The history of measurements option 514 may provide
logged metrics and analysis information over time (e.g., as a
chart).
[0128] It will be appreciated that the multispectral blood metrics
measurement apparatus 200 and/or any digital device may generate
reports based on the analysis, the metrics (e.g., blood metrics or
metrics based on nutrients), historic measurements, historic
analysis, or any other information. Further, alerts may be set by
the multispectral blood metrics measurement apparatus 200 and/or
any digital device.
[0129] It will also be appreciated that the multispectral blood
metrics measurement apparatus 200 may be taking many measurements
over time (e.g., many measurements every minute) and may track
health and changes in metrics over time and/or in the short term.
In some embodiments, if a condition is of sufficient seriousness
(e.g., heart rate shows erratic beats), the multispectral blood
metrics measurement apparatus 200 or any digital device may provide
an alert and request assistance (e.g., from emergency personnel via
the communication network).
[0130] Various health and wellness predictors such as, but not
limited to, energy level, blood iron level, blood oxygen level, and
blood glucose level are displayed. FIG. 6 illustrates a display 600
of an assessment of an overall health index, derived from data
collected from or with a multispectral blood metrics measurement
apparatus in various embodiments.
[0131] In some embodiments, a user interface may display a current
score 602, energy balance information 606, sleep quality
information 608, blood metrics information 610, and body
composition information 612 as well as other information accessible
by slider 604. Additional details may be available through buttons
614. It will be appreciated that any amount of information may be
provided. In some embodiments, the display 600 summarizes
information while more detailed information recommendations,
measurement data, analysis information, and the like may be
available through the details buttons 614 or in other screens.
[0132] Recommendations to the user based on the current and
previous measurements are provided. FIG. 7 illustrates a display
700 of an assessment of an overall health index, derived from data
collected from or with a multispectral blood metrics measurement
apparatus in various embodiments. In some embodiments, a user
interface may display a current score 702, energy level information
706, blood iron level information 708, blood oxygen level
information 710, and blood glucose level 712 as well as other
information accessible by slider 704. Additional details may be
available through buttons 714. It will be appreciated that any
amount of information may be provided. In some embodiments, the
display 700 summarizes information while more detailed information
recommendations, measurement data, analysis information, and the
like may be available through the details buttons 714 or in other
screens.
[0133] Various embodiments track and analyze blood metrics. Health
recommendations may be based on instantaneous blood metrics
measurements and history blood metrics measurement. In addition,
blood metrics and health condition of a user may be compared to
health data of the general public. For example, a user's health
condition may be compared to health condition of other similar
users such as users of the same gender and age group, users of the
same profession, friends of a user, etc.
[0134] FIG. 8 is a block diagram of an exemplary digital device
800. The digital device 800 comprises a processor 802, a memory
system 804, a storage system 806, a communication network interface
808, an I/O interface 810, and a display interface 812
communicatively coupled to a bus 814. The processor 802 is
configured to execute executable instructions (e.g., programs). In
some embodiments, the processor 802 comprises circuitry or any
processor capable of processing the executable instructions.
[0135] The memory system 804 is any memory configured to store
data. Some examples of the memory system 804 are storage devices,
such as RAM or ROM. The memory system 804 can comprise the RAM
cache. In various embodiments, data is stored within the memory
system 804. The data within the memory system 804 may be cleared or
ultimately transferred to the storage system 806.
[0136] The storage system 806 is any storage configured to retrieve
and store data. Some examples of the storage system 806 are flash
drives, hard drives, optical drives, and/or magnetic tape. In some
embodiments, the digital device 800 includes a memory system 804 in
the form of RAM and a storage system 806 in the form of flash data.
Both the memory system 804 and the storage system 806 comprise
computer readable media which may store instructions or programs
that are executable by a computer processor including the processor
802.
[0137] The communications network interface (com. network
interface) 808 can be coupled to a network (e.g., the computer
network 104) via the link 816. The communication network interface
808 may support communication over an Ethernet connection, a serial
connection, a parallel connection, or an ATA connection, for
example. The communication network interface 808 may also support
wireless communication (e.g., 802.11 a/b/g/n, WiMax). It will be
appreciated that the communication network interface 808 can
support many wired and wireless standards.
[0138] The optional input/output (I/O) interface 810 is any device
that receives input from the user and output data. The optional
display interface 812 is any device that is configured to output
graphics and data to a display. In one example, the display
interface 812 is a graphics adapter.
[0139] It will be appreciated that the hardware elements of the
digital device 800 are not limited to those depicted in FIG. 8. A
digital device 800 may comprise more or less hardware elements than
those depicted. Further, hardware elements may share functionality
and still be within various embodiments described herein. In one
example, encoding and/or decoding may be performed by the processor
802 and/or a co-processor located on a GPU (i.e., Nvidia.RTM.).
[0140] Some embodiments described herein include systems and
methods for non-invasive continuous blood pressure measurement. For
example, a blood metrics measurement apparatus may generate
multi-channel signals (e.g., PPG signals or pressure signals) which
may be provided to a blood pressure calculation system to calculate
arterial blood pressure values (e.g., systolic blood pressure
values and/or diastolic blood pressure values). In some
embodiments, the blood metrics measurement apparatus includes a
pressure sensor for measuring pressure signals. In some
embodiments, the blood pressure calculation system may pre-process
(or, "filter") the multi-channel signals (e.g., to remove noise
from the signals), select (or, "extract") subsets of "high quality"
waves from the multi-channel signals, select (or, "extract") sets
of features from the high quality waves, and generate sets of
feature vectors based on the selected sets of features. In some
embodiments, an empirical blood pressure model is used to calculate
arterial blood pressure values based on the sets of feature
vectors.
[0141] FIG. 9 depicts a block diagram of a system and environment
900 for non-invasive blood pressure measurement according to some
embodiments. In some embodiments, the system and environment 900
includes a blood metrics measurement apparatus 902, a user device
904, a blood metrics server 906, a communication network 908, and a
communication link 910.
[0142] The blood metrics measurement apparatus 902 may be
configured to facilitate non-invasive measurement of a user's blood
pressure. In some embodiments, more particularly, the blood metrics
measurement apparatus 902 facilitates non-invasive continuous
measurement of a user's blood pressure. It will be appreciated that
non-invasive continuous measurement may include measuring arterial
blood pressure in real-time without interruption (e.g., without
having to inflate and deflate a cuff) and without inserting a
device (e.g., a tube or catheter) into to the user's tissue or
body.
[0143] In some embodiments, the blood metrics measurement apparatus
902 may project energy into tissue of a user (e.g., the wearer of
the apparatus 902) and detect (or, "receive") energy reflected from
and/or transmitted through tissue of the user. In some embodiments,
the blood metrics measurement apparatus 902 may project energy at
one or more wavelengths (e.g., 523 nm, 590 nm, 623 nm, 660 nm, 740
nm, 850 nm, 940 nm, etc.) from multiple light sources (e.g.,
light-emitting diodes). The detected energy may be a fraction (or,
"portion") of the energy that is projected into the tissue. Energy
at different wavelengths may be absorbed at a different rate that
is related to a user's body state. The user's body state (e.g.,
heart rate, blood pressure, or the like) may determine the amount
of absorbed energy. Accordingly, energy at different wavelengths
may be absorbed at different levels by a user's body. The fraction
of energy received (e.g., that is reflected by the tissue or
transmitted through the tissue) may be used to generate signals,
such as photoplethysmogram (or, "PPG") signals, at different
levels. These signals may provide information of the user's body
state. This information may be obtained by analyzing waveforms of
the signal in a time domain and/or a frequency domain.
[0144] In some embodiments, the blood metrics measurement apparatus
902 may measure non-optical signals. For example, the blood metrics
measurement apparatus 902 may be configured to non-invasively
detect arterial pressure of one or more arteries of the user (e.g.,
radial artery or ulnar artery) based on pressure signals. Similar
to optical signals, the pressure signals may be measured to provide
information of the user's body state, and this information may be
obtained by analyzing waveforms of the signal in a time domain
and/or a frequency domain.
[0145] Functionality of the systems and modules described herein
may be performed similarly with respect to both optical signals
(e.g., PPG signals) and non-optical signals (e.g., pressure
signals). Accordingly, it will be appreciated that signals, as used
herein, may include optical signals, non-optical signals, or
both.
[0146] In some embodiments, a user may comfortably wear the blood
metrics measurement apparatus 902 over time. For example, the blood
metrics measurement apparatus 902 may be worn without interrupting
typical user activity (e.g., moving, walking, running, sleeping,
etc.). The blood metrics measurement apparatus 902 may comprise
lightweight components. The blood metrics measurement apparatus 902
may be made of hypoallergenic materials. The blood metrics
measurement apparatus 902 may be flexibly built so that it may fit
various body parts (e.g., wrist, earlobe, ankle, or chest) of a
user. In some embodiments, the blood metrics measurement apparatus
902 may include some or all of the functionality of the user device
904.
[0147] In some embodiments, the blood metrics measurement apparatus
902 may be or include the apparatus 400 described with regard to
FIG. 4, the blood metrics measurement apparatus 200 depicted with
regard to FIG. 2, or the blood metrics measurement apparatus 102
described with regard to FIG. 1.
[0148] The user device 904 may include any digital device (e.g.,
mobile device) capable of executing an application related to
measuring blood metrics, such as blood pressure calculation,
presenting a user interface through a display and/or communicating
with various entities in the example system and environment 900
through the communication network 908 and/or a communication link
910 (discussed further below). For example, through the
communication link 910, the user device 902 may receive one or more
blood metric measurements (e.g., one or more signals) from the
blood metrics measurement apparatus 902, track and store the blood
metric measurements, analyze the blood metric measurements, and/or
provide recommendations and/or messages based on the blood metric
measurements. An application user interface may facilitate
interaction between a user of the user device 904 and an
application running on the user device 904. The user device 904 may
be, include, or be a part of the user system 104 described with
regard to FIG. 1.
[0149] In various embodiments, the user device 904 may perform
analysis of the measurements received from the blood metrics
measurement apparatus 902 (e.g., calculate blood pressure values),
display results, provide reports, display progress, display
historic readings, track measurements, track analysis, provide
alerts (or, messages), and/or the like.
[0150] The blood metrics server 906 may be configured to generate
and/or store empirical blood pressure models. For example, the
blood metrics server 906 may comprise one or more server computers,
desktop computers, mobile devices, and/or other digital device(s).
In some embodiments, the blood metrics server 906 receives and
process user registration requests (e.g., user account registration
requests, blood metrics measurement apparatus registration
requests, etc.), provides empirical blood pressure model(s) to the
user device 902 via the communication network 908, and/or the
like.
[0151] As used in this paper, computing devices (e.g., digital
devices) may include a mobile phone, a tablet computing device, a
laptop, a desktop computer, personal digital assistant, a portable
gaming unit, a wired gaming unit, a thin client, a set-top box, a
portable multi-media player, or any other type of network
accessible user device known to those of skill in the art. Further,
the blood metrics server 908 may comprise of one or more servers,
which may be operating on or implemented using one or more
cloud-based services (e.g., System-as-a-Service [SaaS],
Platform-as-a-Service [PaaS], or Infrastructure-as-a-Service
[IaaS]).
[0152] Each of the blood metrics measurement apparatus 902, the
user device 904, and the blood metrics server 906 may be
implemented using one or more digital devices. An example digital
device is described in FIG. 8.
[0153] In some embodiments, the communication network 908
represents one or more communication network(s). The communication
network 908 may provide communication between the blood metrics
measurement apparatus 902, the user device 904, and/or the blood
metrics server 906. In some examples, the communication network 908
comprises digital devices, routers, cables, and/or other network
topology. In other examples, the communication network 908 may be
wireless and/or wireless. In some embodiments, the communication
network 908 may be another type of network, such as the Internet,
that may be public, private, IP-based, non-IP based, and so
forth.
[0154] In some embodiments, the communication link 910 represents
one or more communication network connections. The communication
link 910 may provide communication between the blood metrics
measurement apparatus 902 and the user device 904. In some
examples, the communication link 910 comprises a network connection
of the communication network 908, and/or a separate communication
network. In some embodiments, the communication link 910 comprises
a wireless communication link, such as a Bluetooth communication
link, Wi-Fi communication link, and/or the like.
[0155] FIG. 10A depicts a block diagram 1000 of a blood metrics
measurement apparatus 902 according to some embodiments. The blood
metrics measurement apparatus 902 comprises an analyzer 1002, an
energy transmitter 1004, an energy receiver 1006, a pressure sensor
1008, a motion sensor 1012, and a communication module 1014.
Various embodiments may comprise a wearable member. The wearable
member may include, for example, a bracelet, glasses, necklace,
ring, anklet, belt, broach, jewelry, clothing, or any other member
of combination of members that allow the blood metrics measurement
apparatus 902 to be close to or touch a body of the wearer. In some
embodiments, the blood metrics measurement apparatus 902 may
further comprise a driver (not shown) and a power source (not
shown). The power source may be coupled to the energy transmitter
1004 via the driver. The blood metrics measurement apparatus 902
may further comprise an Analog-to-Digital Converter ("ADC") (not
shown). The ADC may be coupled to the energy receiver 1006 and the
analyzer 202.
[0156] The analyzer 1002 may be coupled to the energy transmitter
1004, the energy receiver 1006, the pressure sensor 1008, the
motion sensor 1012, and the communication module 1014. The energy
transmitter 1004 and the energy receiver 1006 may be secured to the
wearable member such that the energy transmitter 1004 and the
energy receiver 1006 may make contact or be in proximity with
tissues (e.g., skin) of a user. In various embodiments, the energy
transmitter 1004 emits energy including, but not limited to, light,
into the body of the user. The energy produced by the energy
transmitter may be in the direction of entering tissues. For
example, the energy produced by the energy transmitter 1004 is in a
direction 1051 entering the tissue 1010. In some embodiments, the
energy transmitter 1004 emits energy or light at different
wavelengths. The energy transmitter 1004 may comprise any number of
light emission diodes ("LEDs"). In some embodiments, the energy
transmitter 1004 comprises at least two LEDs. Each LED may be
configured to emit energy at one or more wavelengths. In another
example, each LED may emit light with a peak wavelength centered
around a wavelength. In one example, the energy transmitter 1004
may emit light with a peak wavelength centered around 500 nm to
1800 nm, although the wavelength may include a variety of spectrums
(e.g., IR, near-IR, and the like).
[0157] Each wavelength may correspond to one or more blood metrics
of interest and/or one or more nutrients. It will be appreciated
that different components of the blood and/or different nutrients
may absorb energy at different wavelengths. In various embodiments,
a controller, driver, analyzer 1002, or the like may receive a
blood metric or nutrient of interest (e.g., from a user of the
blood metrics measurement apparatus 902 and/or a user device not
shown). The controller, driver, analyzer 1002 or the like may
associate the blood metric and/or nutrient of interest with one or
more wavelengths and configure one or more of the LEDs to emit
energy of at least one of the one or more wavelengths. For example,
the analyzer 1002 may command the driver to deliver electric power
to one LED that is configured to emit light at the desired
wavelength.
[0158] The energy receiver 1006 may detect energy associated with
the energy provided by the LEDs from tissues (e.g., skin) of the
user. In this example, received and/or detected energy is in the
direction 1052 that leaves from the tissue 1010. In various
embodiments, the energy receiver 1006 may detect energy from the
body of the user that is a fraction of the energy produced by the
energy transmitter 1004.
[0159] The energy transmitter 1004 and the energy receiver 1006 may
be configured such that the energy receiver 1006 detects reflected
energy from tissues of the user of the multispectral blood metrics
measurement apparatus 902. For example, the energy transmitter 1004
and the energy receiver 1006 may be configured to be disposed on
one surface or side of a user's tissue. The energy transmitter 1004
and the energy receiver 1006 may be configured such that the energy
receiver 1006 detects energy from the energy transmitter 1004 that
passes through or reflects from the user's tissues. In some
embodiments, the energy transmitter 1004 and the energy receiver
1006 may be configured to be disposed on different (e.g., opposite)
surfaces or sides of a users' tissue.
[0160] The energy transmitter 1004 may be configured to generate
energy at a set of wavelengths. In some embodiments, the energy
transmitter 1004 is configured to generate energy such that energy
at different wavelengths is generated sequentially and/or
periodically. The energy transmitter 1004 may be configured to
generate energy at each particular wavelength until energy at all
wavelengths of a set is generated. The period of time for the
energy transmitter 1004 to generate energy at all wavelengths is a
generation period. Subsequent to completion of the generation
period, the energy transmitter 1004 may start a new generation
period thereby allowing multiple measurements.
[0161] Energy detected from tissues of a user may be detected by
the energy receiver 1006. The energy receiver 1006 may be
configured to generate a signal in response to the detected energy.
In some embodiments, the energy receiver 1006 may be triggered by
the energy received to generate an output which may be dependent or
partially dependent upon the amount of energy received. The energy
receiver 1006 may be configured to generate a signal (e.g., an
electric current, or an electric voltage) in response to the energy
received from the tissues.
[0162] The signal generated by the energy receiver 1006 may be
associated with one or more blood metrics and/or nutrients of
interest. Energy at different wavelengths may be absorbed at a
different rate that is related to a user's body state. The user's
body state (e.g., heart rate, blood pressure, nutrient level, or
the like) may determine the amount of energy absorbed by the body.
Accordingly, energy from the user's body at different wavelengths
may be detected at different levels thereby causing different
responses of the energy receiver 1006. The energy receiver 1006
may, for example, output signals based on the level of the energy
received.
[0163] The energy receiver 1006 may provide information associated
with the user's body state. Blood metric information may be
determined (e.g., by the analyzer 1002) from the output signal of
the energy receiver 1006. In some embodiments, the energy receiver
1006 may comprise a set of photodetectors (e.g., a photo diode, or
a photo transistor) which are configured to output a signal
dependent upon photons or the like from the energy transmitter 1004
that passed through tissues of the user.
[0164] In various embodiments, the output signal of the energy
receiver 1006 is a composite of multiple signals. Each signal of
the composite may be associated with energy at a wavelength which
may be a portion (or fraction) of the total energy emitted by the
energy transmitter 1004.
[0165] The pressure sensor 1008 may be configured to generate,
detect, and/or measure non-optical signals. For example, the
pressure sensor 1008 may non-invasively and continuously generate,
detect and/or measure pressure pulse signals. In some embodiments,
the pressure sensor 1008 measures pressure pulse waveforms
associated with arterial pressure of one or more arteries of a
user. In various embodiments, the blood metrics measurement
apparatus 902 may include the energy transmitter 1004 to generate
optical signals and the energy receiver 1006 to receive optical
signals but not the pressure sensor 1008. Alternately, the blood
metrics measurement apparatus 902 may include the pressure sensor
1008 that may produce pressure on the user's body and/or receive
measurements based on that pressure but not the energy transmitter
1004 or the energy receiver 1006 to receive optical signals.
[0166] In some embodiments, the motion sensor 1012 may be
configured to detect position and orientation of the blood metrics
measurement apparatus 902, and detect motion of the blood metrics
measurement apparatus 902. For example, the blood metrics
measurement apparatus 902 may detect position, orientation, and
motion along an x, y, or z-axis, and measured values may include
velocity, acceleration, distance, and the like. In some
embodiments, the motion sensor 1012 may include one or more
accelerometers, gyroscope, global positioning systems, or the like.
The motion sensor may be coupled to the analyzer 1002.
[0167] The communication module 1014 may be configured to send
requests to and receive data from one or a plurality of systems.
The communication module 1014 may send requests to and receive data
from a systems through a network or a portion of a network.
Depending upon implementation-specific or other considerations, the
communication module 1014 may send requests and receive data
through a connection (e.g., the communication link 910), all or a
portion of which may be a wireless connection. The communication
module 1014 may request and receive messages, and/or other
communications from associated systems.
[0168] FIG. 10B depicts a block diagram of a sensor system 1060
according to some embodiments. In some embodiments, the sensor
system 1060 may be disposed in the blood metrics measurement
apparatus 902. For example, the energy transmitter 1004 and the
energy receiver 1006 may comprise the sensor system 1060. As shown,
the sensor system 1060 comprises two sets of multi-LED energy
transmitters (i.e., LED set 1062 and 1066 each referred to as
energy transmitters herein), and two sets of corresponding
photodiode energy receivers 1064 and 1068. It will be appreciated
that each pair of corresponding energy transmitters and energy
receivers (e.g., energy transmitter 1062 and energy receiver 1064
as well as energy transmitter 1066 and energy receiver 1068) may be
referred to as an LED-PD system, and the sensor system 1060 may
include any number of such LED-PD systems, although only two are
shown here.
[0169] In some embodiments, the energy transmitters 1062 and 1066
each comprise a set of LEDs (e.g., between 2-4 LEDs) configured to
transmit a variety of wavelengths into tissue of a user. The
corresponding energy receivers 1064 and 1068 may each comprise one
or more photodiodes which receive returning light from the
corresponding energy transmitters 1062 and 1066 after passage
through tissue. In some embodiments, the energy transmitter 1062
and the energy receiver 1064 may be spaced at a predetermined
distance (e.g., 15 mm) from the energy transmitter 1066 and the
energy receiver 1068. Similarly, each energy transmitter 1062 and
1066 may be spaced at a predetermined distance (e.g., 10 mm) from a
corresponding energy receiver 1064 and 1068.
[0170] In some embodiments, the sensor system 1060 may be mounted
on a user's wrist along the radial artery, or other area of the
user's body along arterial pathways (e.g., ulnar artery). The
sensor system 1060 may include an accelerometer and gyroscope for
detecting position and orientation information.
[0171] In some embodiments, the sensor system 1060 may include a
pressure sensor. For example, at least one LED within each LED-PD
system may be configured to sample data from tissue at a very high
frequency (upwards of 1 KHz), and the other LEDs may sample at a
lower rate (around 50-100 Hz). The channels sampling data at high
frequency may be referred to as the fast LED channels, and the
channels sampling data at lower frequency may be referred to as
slow LED channels.
[0172] In some embodiments, the sensor system 1060 may be included
as a part of the blood metrics measurement apparatus 902 described
with regard to FIGS. 9 and 10A, the apparatus 400 described with
regard to FIG. 4, the blood metrics measurement apparatus 200
described with regard to FIG. 2, or the blood metrics measurement
apparatus 102 described with regard to FIG. 1. Similarly, the
analyzer 202 may be or include a part of analyzer 1002 described
with regard to FIG. 2 or the central unit 402 described with regard
to FIG. 4. The energy transmitter 1004 may be or include a part of
energy transmitter 204 described with regard to FIG. 2 or the
central unit 402 described with regard to FIG. 4. The energy
receiver 1006 may be or include energy receiver 206 described with
regard to FIG. 2 or the central unit 402 described with regard to
FIG. 4.
[0173] FIG. 11 depicts a flowchart 1100 of an example method of
operation of a blood metrics measurement apparatus (e.g., blood
metrics measurement apparatus 902) according to some embodiments.
In this and other flowcharts described herein, the flowchart
illustrates by way of example a sequence of steps. It should be
understood the steps may be reorganized for parallel execution, or
reordered, as applicable. Moreover, some steps that could have been
included may have been removed to avoid providing too much
information for the sake of clarity and some steps that were
included could be removed, but may have been included for the sake
of illustrative clarity.
[0174] In step 1102, a blood metrics measurement apparatus projects
energy into tissue of a user (e.g., the user wearing blood metrics
measurement apparatus). The energy may be projected from a
transmitter (e.g., energy transmitter 1004) comprising a plurality
of light sources (e.g., LEDs). In some embodiments, a first light
source (e.g., one or more LEDs) may project light energy at a
plurality of different wavelengths, such as 523 nm, 590 nm, 623 nm,
660 nm, 740 nm, 850 nm, and 940 nm, and a second light source
(e.g., one or more LEDs) may project energy at the same, or
substantially similar, wavelength as one of the wavelengths
projected by the first light source (e.g., 523 nm, 590 nm, 623 nm,
660 nm, 740 nm, 850 nm, or 940 nm). It will be appreciated that
other configuration may be used (e.g., a greater number of light
sources) a greater or lesser number of wavelengths projected from
the lights sources, and so forth.
[0175] In step 1104, the blood metrics measurement apparatus
receives (or, "detects) portions of energy through the tissue of
the user. In some embodiments, an energy receiver (e.g., energy
transmitter 1006) detects a portion of the energy transmitted into
the user's tissue by the energy transmitter. The energy receiver
may generate a signal based on the portion of energy detected
(e.g., based on the amount of the energy detected). For example,
energy detected may be a portion of the energy projected at step
1102 reflected by the tissue. By way of further example, energy
detected may be a portion of the energy projected at step 1102 that
passes through the tissue (e.g., other undetected energy may be
absorbed by tissue and/or otherwise blocked). In various
embodiments, steps 1102 and 1104 are performed simultaneously or
substantially simultaneously. That is, energy projection and
detection may be performed approximately simultaneously.
[0176] In step 1106, the blood metrics measurement apparatus
generates one or more signals based on the received portions of
energy. In some embodiments, the energy receiver may generate a
multi-channel PPG signal (e.g., as mentioned above). The output
(or, "generated") signal of the energy receiver may be an electric
current or an electric voltage, of which the amplitude may be
related to the amount of the energy detected.
[0177] In various embodiments, analysis of the signals from the
energy receiver may identify abnormal measurements. For example,
each of the measurements may be compared to a predetermined value.
If the difference between the measurement and the predetermined
value is above (or below) a threshold, then the measurement may be
determined to be abnormal. An abnormal value may trigger additional
analysis or an alert. In some embodiments, an abnormal value is
ignored (e.g., as possibly effected by noise caused by movement of
the energy transmitter and/or the energy receiver). In various
embodiments, the abnormal value may be discounted (e.g., the weight
of the value reduced). The degree of discount may be based, for
example, on information from an accelerometer (e.g., a large
acceleration may indicate that the abnormal value should be
significantly discounted) and/or based on historical values. It
will be appreciated that the degree of discount may be based on any
number of factors.
[0178] In some embodiments, measurements may be averaged over a
period of time. A Kalman filer (e.g., a nonlinear, unscented Kalman
filter) may be applied to any number of measurements or averaged
measurements. A motion measurement (e.g., a measurement by an
accelerometer) may be considered. Upon determining a measurement is
abnormal, the motion measurement for that time point may be
inspected. A large measurement may indicate large vibrations or
accelerations that corroborate that the measurement may be
abnormal. Measurements collected in such situations are likely to
have significant electrical noises.
[0179] At step 1108, the blood metrics measurement apparatus
analyzes signals from the energy receiver analyzed in the frequency
domain to determine blood metrics. Concentration of a nutrient in
the blood may subsequently be determined. In some embodiments,
signals may be provided to a bandpass filter that separates AC
components from DC components. An AC component may represent signal
variation at the cardiac frequency and a DC component may represent
the average overall transmitted light intensity. In some
embodiments, a heart rate and/or oxygen saturation, SpO.sub.2 may
be determined. The heart rate may be determined, for example, by
averaging the maximum frequency to determine the rate of cardiac
beats in a predetermined amount of time. The oxygen saturation
SpO.sub.2 may be determined according to Equation (1):
S.sub.pO.sub.2=110-25.times.R (1),
[0180] where R is the ration of a red and infrared normalized
transmitted light intensity. R may be determined according to
Equation (2):
R = A C R / D C R A C IR / D C IR , ( 2 ) , ##EQU00011##
where the AC.sub.R is the AC component of the detected energy
corresponding to a wavelength (e.g., red light), DC.sub.R is the DC
component of the detected energy corresponding to the wavelength
(e.g., red light), AC.sub.IR is the AC component of the detected
energy corresponding to a different wavelength (e.g., infrared
light), and DC.sub.IR is the DC component of the detected energy
corresponding to the different wavelength (e.g., infrared light).
In some embodiments, the AC component may be selected as the
highest spectral line in the cardiac frequency band. Waveform
analysis may be performed to determine the R-R interval defined by
two successive AC components, an elapsed interval and the
probation, if there is any.
[0181] It will be appreciated that analysis may be performed by the
analyzer and/or any other digital device (e.g., user device or a
blood metrics server such as blood metrics server 906).
[0182] State space estimation and progression may be performed to
determine blood metrics. A system may be modeled according to
Equation (3):
x(n+1)=f[x(n)]+u(n)
y(n)=h[x(n)]+v(n) (3),
where x(n) represents the state of the system, u(n) is process
noise, y(n) is the vector of the observed signals, and v(n) is the
measurement noise.
[0183] Table 1 lists one or more parameters for x(n) as well as
their initial value in some embodiments:
TABLE-US-00005 TABLE 1 Parameter Symbol Initial Value Cardiac
frequency f.sub.HR 1 Hz Cardiac phase .theta..sub.hr 0 Cardiac
harmonic I.sub.Harmonic.sup.HR 0 amplitude Cardiac Pulse P.sub.HR 1
Pressure Point Blood Pressure P.sub.Point 1 Respiratory f.sub.Resp
0.3 Hz frequency Respiratory phase .theta..sub.Resp 0 Wavelength i
= 1 . . . N I.sub..lamda..sub.i.sup.AC 0.5 max_value AC peak
amplitude Wavelength i = 1 . . . N pos.sub..lamda..sub.i.sup.AC
Corresponding FFT AC peak location bin to 1 Hz Wavelength i = 1 . .
. N I.sub..lamda..sub.i.sup.DC 0.5 max_value DC Wavelength i = 1 .
. . N I.sub..lamda..sub.i.sup.p2p 1 ADC read p2p amplitude
Wavelength i = 1 . . . N .tau..sub..lamda..sub.i.sup.rise 0.1 sec
rise time Wavelength i = 1 . . . N C.sub..lamda..sub.i 1
Significance coefficient Wavelength i = 1 . . . N
T.sub..lamda..sub.i.sup.HRV 1 sec HRV Best Ratio pH BR.sub.pH 2
Best Ratio pCO2 BR.sub.pCO2 3 Best Ratio pHCO3-- BR.sub.pHCO3.sub.-
4 Acceleration I.sub.move 0 magnitude GPS velocity |v|.sub.GPS 0
GPS altitude |alt|.sub.GPS 0 GPS acceleration |a|.sub.GPS 0 GPS
incline |incline|.sub.GPS 0 Restfulness Rest 0 Hydration Hyd 0
Systolic Blood SBP 120 mmHg Pressure Diastolic Blood DBP 80 mmHg
Pressure End tidal CO2 ETCO2 40 mmHg Blood Carbon SpCO 0%
Monoxide
[0184] Table 2 lists one or more parameters for y(n) as well as
their initial value in some embodiments:
TABLE-US-00006 TABLE 2 Parameter Symbol Initial Blood pH pH 7.35
Blood PCO2 pCO.sub.2 24 mmol Blood PO2 pO.sub.2 24 mmol Blood
PHCO3-- pHCO.sub.3.sup.- 24 mmol Blood Glucose
pC.sub.6H.sub.12O.sub.6 3 mmol Cardiac Frequency f.sub.HR 1 Point
Blood Pressure P.sub.Point 1 Respiratory f.sub.Resp 0.3 Frequency
GPS velocity |v|.sub.GPS 0 GPS altitude |alt|.sub.GPS 0 GPS
acceleration |a|.sub.GPS 0 GPS incline |incline|.sub.GPS 0
[0185] Table 3 lists the state space model F(X(n)) between the
parameters listed in Table 1 and Table 2 in some embodiments, where
the energy wavelengths comprise 880 nm, 631 nm, 1450 nm, and 1550
nm:
TABLE-US-00007 TABLE 3 Name Symbol Equation Cardiac frequency
f.sub.HR bin_to _freq ( c .lamda. i pos .lamda. i AC c .lamda. i )
##EQU00012## Cardiac .theta..sub.HR .theta..sub.HR(n - 1) +
f.sub.s.sup.-1 * .omega..sup.*, where .omega..sup.* .di-elect cons.
[.omega._min, .omega._max] phase Cardiac harmonic amplitude
I.sub.Harmonic.sup.HR c .lamda. i I .lamda. i p 2 p c .lamda. i
##EQU00013## Cardiac Pulse Pressure P.sub.HR ( c .lamda. i .tau.
.lamda. i rise c .lamda. i ) ^ - 1 ##EQU00014## Point Blood
Pressure P.sub.Point .tau..sub..lamda..sub.1.sup.rise.sup.-1
Respiratory f.sub.Resp 3) Respiratory and Heart Rate State Models:
The fluctuations frequency in the respiratory rate .omega..sub.r(n)
and fluctuations in the heart rate .omega..sub.ca(n) that are not
due to RSA are both modeled as a first-order autoregressive process
with a mean and mild non- linearity that limit the frequencies to
know physiologic ranges .omega..sub.r(n + 1) = .omega..sub.r +
.alpha..sub.r{s.sub.r[.omega..sub.r(n)] - .omega..sub.r} +
u.sub..omega..sub.r(n) (15) .omega..sub.ca(n + 1) = .omega..sub.c +
.alpha..sub.c{s.sub.c[.omega..sub.ca(n)] - .omega..sub.c} +
u.sub..omega..sub.ca(n) (16) where .omega..sub.r and .omega..sub.c
are the a priori estimates of the expected res- piratory and
cardiac frequencies, respectively; .alpha..sub.r and .alpha..sub.c
con- trol the bandwidth of the frequency fluctuations; and
u.sub..omega..sub.r(n) and u.sub..omega..sub.ca(n) are white noise
processes that model the random variation in the respiratory and
cardiac frequencies, respectively. The instantaneous respiratory
and heart rates in units of Hz are then f r ( n ) = 1 2 .pi. T s s
T [ .omega. r ( n ) ] ( 17 ) ##EQU00015## f c ( n ) = 1 2 .pi. T s
s c [ .omega. c ( n ) ] . ( 18 ) ##EQU00016## Respiratory
.theta..sub.Resp .theta..sub.Resp(n - 1) + f.sub.s.sup.-1 *
.omega..sup.*, where .omega..sup.* .di-elect cons. [.omega._min,
.omega._max] phase .lamda. = 880 nm I.sub..lamda..sub.i.sup.AC From
FFT AC peak .lamda. = 880 nm pos.sub..lamda..sub.i.sup.AC From FFT
DC .lamda. = 880 nm I.sub..lamda..sub.i.sup.DC From Waveform
analysis p2p amplitude .lamda. = 880 nm I.sub..lamda..sub.i.sup.p2p
From Waveform analysis rise time .lamda. = 880 nm
.tau..sub..lamda..sub.i.sup.rise From Waveform analysis signal
trend .lamda. = 880 nm c.sub..lamda..sub.i From Waveform analysis
Significance coefficient .lamda. = 880 nm
T.sub..lamda..sub.i.sup.HRV From Waveform analysis HRV .lamda. =
631 nm I.sub..lamda..sub.i.sup.AC From Fast Fourier Transformation
("FFT") AC peak .lamda. = 631 nm pos.sub..lamda..sub.i.sup.AC From
FFT DC .lamda. = 631 nm I.sub..lamda..sub.i.sup.DC From Waveform
analysis p2p amplitude .lamda. = 631 nm I.sub..lamda..sub.i.sup.p2p
From Waveform analysis rise time .lamda. = 631 nm
.tau..sub..lamda..sub.i.sup.rise From Waveform analysis signal
trend .lamda. = 631 nm c.sub..lamda..sub.i From Waveform analysis
Significance coefficient .lamda. = 631 nm
T.sub..lamda..sub.i.sup.HRV From Waveform analysis HRV .lamda. =
1450 nm I.sub..lamda..sub.i.sup.AC From FFT AC peak .lamda. = 1450
nm pos.sub..lamda..sub.i.sup.AC From FFT DC .lamda. = 1450 nm
I.sub..lamda..sub.i.sup.DC From Waveform analysis p2p amplitude
.lamda. = 1450 nm I.sub..lamda..sub.i.sup.p2p From Waveform
analysis rise time .lamda. = 1450 nm
.tau..sub..lamda..sub.i.sup.rise From Waveform analysis signal
trend .lamda. = 1450 nm c.sub..lamda..sub.i From Waveform analysis
Significance coefficient .lamda. = 1450 nm
T.sub..lamda..sub.i.sup.HRV From Waveform analysis HRV .lamda. =
1550 nm I.sub..lamda..sub.i.sup.AC From FFT AC peak .lamda. = 1550
nm pos.sub..lamda..sub.i.sup.AC From FFT DC .lamda. = 1550 nm
I.sub..lamda..sub.i.sup.DC From Waveform analysis p2p amplitude
.lamda. = 1550 nm I.sub..lamda..sub.i.sup.p2p From Waveform
analysis rise time .lamda. = 1550 nm
.tau..sub..lamda..sub.i.sup.rise From Waveform analysis signal
trend .lamda. = 1550 nm c.sub..lamda..sub.i From Waveform analysis
Significance coefficient .lamda. = 1550 nm
T.sub..lamda..sub.i.sup.HRV From Waveform analysis HRV Best Ratio
BR.sub.pH Device Calibration pH Best Ratio BR.sub.pCO2 Device
Calibration pCO2 Best Ratio BR.sub.pHCO3- Device Calibration pHCO3-
Acceleration I.sub.move From Accelerometer magnitude GPS velocity
|v|.sub.GPS From GPS GPS altitude |alt|.sub.GPS From GPS GPS
|a|.sub.GPS From GPS acceleration GPS incline |incline|.sub.GPS
From GPS
TABLE-US-00008 TABLE 4 Table 4 lists Y(n) = H(x(n)): Name Symbol
Equation Blood pH pH 6.1 + log ( pHCO 3 - 0.03 pCO 2 ) ##EQU00017##
Blood PCO2 pCO.sub.2 Hb CO 2 - Hb Hb * I .lamda. CO 2 AC * I
.lamda. 1 DC / ( I .lamda. 1 AC * I .lamda. CO 2 DC ) Hb CO 2 - CO
2 CO 2 + ( CO 2 Hb - Hb Hb ) * I .lamda. CO 2 AC * I .lamda. 1 DC /
( I .lamda. 1 AC * I .lamda. CO 2 DC ) ##EQU00018## Blood PO2
pO.sub.2 Hb O 2 - Hb Hb * I .lamda. O 2 AC * I .lamda. 1 DC / ( I
.lamda. 1 AC * I .lamda. O 2 DC ) Hb O 2 - O 2 O 2 + ( O 2 Hb - Hb
Hb ) * I .lamda. O 2 AC * I .lamda. 1 DC / ( I .lamda. 1 AC * I
.lamda. O 2 DC ) ##EQU00019## Blood PHCO3- pHCO.sub.3.sup.- Hb HCO
3 - - Hb Hb * I .lamda. HCO 3 - AC * I .lamda. 1 DC / ( I .lamda. 1
AC * I .lamda. HCO 3 - DC ) Hb HCO 3 - - HCO 3 - HCO 3 - + ( HCO 3
- Hb - Hb Hb ) * I .lamda. HCO 3 - AC * I .lamda. 1 DC / ( I
.lamda. 1 AC * I .lamda. HCO 3 - DC ) ##EQU00020## Blood Glucose
pC.sub.6H.sub.12O.sub.6 As above Cardiac f.sub.HR As in f(x(n))
Frequency Point Blood P.sub.Point As in f(x(n)) Pressure
Respiratory f.sub.Resp As in f(x(n)) Frequency GPS velocity
|v|.sub.GPS As in f(x(n)) GPS altitude |alt|.sub.GPS As in f(x(n))
GPS |a|.sub.GPS As in f(x(n)) acceleration GPS incline
|incline|.sub.GPS As in f(x(n))
[0186] As illustrated in Tables 3 and 4, by generating energy at
different wavelengths, one or more blood metrics may be determined
from the detected energy. For example, cardiac frequency, cardiac
phase, cardiac harmonic amplitude, cardiac pulse pressure, point
blood pressure, respiratory frequency, respiratory phase, blood pH,
blood pCO.sub.2, blood pHCO.sub.3-, or blood glucose, may be
determined.
[0187] In step 1110, the blood metrics measurement apparatus
provides the generated one or more signals for blood pressure
calculation. In some embodiments, a communication module (e.g.,
communication module 1008) provides the one or more signals (e.g.,
to a blood pressure calculation system and/or user device).
[0188] FIG. 12 depicts a block diagram 1200 of a user device 904
according to some embodiments. Generally, the user device 904 may
be configured to display, or otherwise present blood pressure
values, messages, alerts, and/or the like. The user device 904 may
also provide registration features allowing a user to register a
blood metrics measurement device 902, create and/or update a user
account, and communicate with other systems of the system and
environment 900. In some embodiments, the user device 904 includes
a user interface module 1202, a registration module 1204, a blood
pressure calculation system 1206, and a communication module 1208.
In some embodiments, the blood metrics measurement device 902 may
include all or part of the blood pressure calculation system
1206.
[0189] The user interface module 1202 may be configured to present
images and/or audio corresponding to health data, such as blood
pressure values, messages, alerts, and the like. For example, the
user interface module 1202 may display one or more graphical user
interfaces (GUIs) to present a calculated blood pressure to a user.
Example user interfaces are described further with reference to
FIG. 5-7, discussed above.
[0190] The registration module 1204 may be configured to generate
registration requests to create, read, update, delete, or otherwise
access, registration records associated with user accounts (e.g., a
user account associated with a user of the user device 904 and/or
the blood metrics measurement apparatus 902) and registration
records associated with the blood metrics measurement apparatus
902. In some embodiments, a user inputs user account registration
information and blood metrics measurement apparatus registration
information via the user interface module 1202. For example, user
account registration information may include geographic attributes,
demographic attributes, psychographic attributes, and/or
behavioristic attributes. Accordingly, user account registration
information may include some or all of the following attributes:
[0191] User Account Identifier: Identifier that identifies a user
account. [0192] Password: Password, or other personal identifier,
used to authenticate the user account. For example, it may an
alphanumerical password, biometric data (e.g., fingerprint, etc.).
In some embodiments, readings or measurements from the blood
metrics measurement apparatus 902 may be used to authenticate the
user account. [0193] Device Identifier(s): Identifier(s) that
identify one or more blood metric measurement apparatus' associated
with the user account. [0194] Name: A name of the user. [0195] DOB:
A date of birth of the user. [0196] Age: An age of the user. [0197]
Gender: Gender of the user (e.g., female, male, transgender, or the
like). [0198] Weight: A weight of the user. [0199] Height: A height
of the user. [0200] Skin color: A skin color of the user. [0201]
Activity Level: An activity level of the user (e.g., sedentary,
lightly active, active, very active, and so forth). [0202]
Geographic location: A location of the user (e.g., as determined by
a location service and/or specified by the user). [0203] Blood
Pressure Profile: Hypertensive, Hypotensive, Normal or unknown.
[0204] Blood Glucose Profile (e.g., Diabetes information) [0205]
Wrist circumference: Circumference of the user's wrist.
[0206] In some embodiments, the blood metrics measurement apparatus
registration information includes some or all of the following
attributes: [0207] Apparatus Identifier: Identifier that identifies
a blood metrics measurement apparatus. [0208] User Account
Identifier: Identifier that identifies a user account associated
with the blood metrics measurement apparatus. [0209] Geographic
location: A current location of the blood metrics measurement
apparatus (e.g., as determined by a location service and/or
specified by the user). [0210] Settings: One or more settings of
the blood measurement metrics apparatus. For example, some or all
of the settings may be automatically determined based on one or
more user account attributes (e.g., height, weight, or the like)
and/or by the user.
[0211] The blood pressure calculation system 1206 may be configured
to calculate blood pressure values (e.g., systolic, diastolic), and
generate messages or alerts based on those values. An example of
the blood pressure calculation system 1206 is discussed further
below with reference to FIG. 14.
[0212] The communication module 1208 may be configured to send
requests to and receive data from one or a plurality of systems.
The communication module 1208 may send requests to and receive data
from a systems through a network or a portion of a network.
Depending upon implementation-specific or other considerations, the
communication module 1208 may send requests and receive data
through a connection (e.g., the communication network 908, and/or
the communication link 910), all or a portion of which may be a
wireless connection. The communication module 1208 may request and
receive messages, and/or other communications from associated
systems.
[0213] FIG. 13 depicts a flowchart 1300 of an example method of
operation of a user device (e.g. user device 904) according to some
embodiments.
[0214] In step 1302, a user device registers a blood metrics
measurement apparatus (e.g., blood metrics measurement apparatus
902). In some embodiments, input from a user is received by a user
interface module (e.g., user interface module 1202) that triggers a
registration module (e.g., registration module 1204) to generate a
registration request to associate the blood metrics measurement
apparatus with a user and/or the user device. The registration
request may include, for example, one or more blood metrics
measurement apparatus attributes. In some embodiments, a
communication module (e.g., communication module 1208) provides the
registration request to a blood metrics server (e.g., blood metrics
server 906) for processing.
[0215] In step 1304, the user device receives one or more signals
from the registered blood metrics measurement apparatus. In some
embodiments, the one or more signals comprise optical signals
(e.g., multi-channel PPG signals) and/or one or more non-optical
signals (e.g., multi-channel pressure pulse signals). In some
embodiments, the one or more signals may be received by the
communication module.
[0216] In step 1306, the user device calculates one or more
arterial blood pressure values (e.g., systolic values and/or
diastolic values) based on the received one or more signals. In
some embodiments, a blood pressure calculation system (e.g., blood
pressure calculation system 1206) calculates the one or more
arterial blood pressure values. Although this example depicts the
user device calculating the one or more arterial blood pressure
values, it will be appreciated that one or more other systems
having the functionality of a blood pressure calculation system may
perform the calculation. For example, in some embodiments, the
blood pressure measurement apparatus and/or the blood metrics
server may include such functionality and perform the
calculation.
[0217] In step 1308, the user device presents a blood pressure
message to the user based on at least one of the one or more
calculated arterial blood pressure values. For example, the message
may include some or all of the arterial blood pressure values,
alerts (e.g., high BP, low BP, good BP, poor BP, etc.) based on one
or more of the calculated values, and so forth. In some
embodiments, the user device presents (e.g., via images, audio,
vibrations, etc.) the blood pressure message or alert to the user
via the user interface module, or other feature of the user
device.
[0218] FIG. 14 depicts a block diagram 1400 of a blood pressure
calculation system 1206 according to some embodiments. Generally,
the blood pressure calculation system 1206 may be configured to
calculate arterial blood pressure values of a user. The blood
pressure calculation system 1206 may also store calculated arterial
blood pressure values (e.g., for health tracking, etc.), and
communicate with other systems of the system and environment 900.
In some embodiments, the blood pressure calculation system 1206
includes a management module 1402, a signal database 1404, a wave
database 1406, a wave feature database 1408, a feature vector
database 1410, a blood pressure model database 1412, a blood
pressure results database 1414, a rules database 1416, a
pre-processing module 1418, a wave selection module 1420, a feature
extraction module 1422, a blood pressure processing module 1424,
and a communication module 1426.
[0219] The management module 1402 may be configured to manage
(e.g., create, read, update, delete, or access) signal records 1428
stored in the signal database 1404, wave records 1430 stored in the
wave database 1406, wave feature records 1432 stored in the wave
feature database 1408, feature vector records 1434 stored in the
feature vector database 1410, empirical blood pressure model
records 1436 stored in the blood pressure model database 1412,
blood pressure result records 1438 stored in the blood pressure
results database 1414, and/or rules 1440-1448 stored in rules
database 1416. The management module 1402 may perform these
operations manually (e.g., by an administrator interacting with a
GUI) and/or automatically (e.g., by one or more of the modules
1418-1424). In some embodiments, the management module 1402
comprises a library of executable instructions which are executable
by a processor for performing any of the aforementioned management
operations. The databases 1404-1416 may be any structure and/or
structures suitable for storing the records 1428-1438 and/or the
rules 1440-1448 (e.g., an active database, a relational database, a
table, a matrix, an array, a flat file, and the like).
[0220] The signal records 1428 may include a variety of signals,
along with associated metadata. For example, the signals may
comprise optical signals (e.g., single-channel and/or multi-channel
PPG signals) and/or non-optical signals (e.g., pressure pulse
signals). In some embodiments, the metadata may include information
obtained from the signals, such as heart rate(s) of an associated
user. For example, the signal records 1428 may store some or all of
the following information: [0221] Signal(s) Identifier: Identifier
that identifies the stored signal(s). [0222] Signal(s): one or more
signals. The signals may be raw signals (e.g., as detected by the
associated blood pressure measurement apparatus), filtered or
pre-processed signals (e.g., to remove noise from the signals),
and/or normalized signal values (e.g., between 0-1). An example
"noisy" (i.e., unfiltered) PPG signal and an example filtered PPG
signal are shown in FIG. 21. It will be appreciated that as used in
this paper, a "signal," such as a PPG signal or pressure pulse
signal, generally refers to a filtered signal, although in some
embodiments, it may also refer to an unfiltered signal instead of,
or in addition to, the filtered signal. [0223] Set(s) of Waves: one
or more sets of waves of a predetermined time series (e.g., 8
seconds) of the signal(s). The set of waves may include raw waves,
filtered waves, and/or normalized wave values (e.g., between 0-1).
An example set of waves is shown in FIG. 22. [0224] Apparatus
Identifier: Identifier that identifies the blood metrics
measurement apparatus that generated the signals. [0225] User
Account Identifier: Identifier that identifies a user account
associated with the blood metrics measurement apparatus that
generated the signals. [0226] Metadata: Metadata obtained from the
signals, such as heart rate or other biometric data. The metadata
may also include other information of the user, such as gender,
age, height, weight, skin color (e.g., obtained from the user's
account information). Such metadata values may be used by the blood
pressure calculation module 1424 (discussed below) to facilitate
calculation of arterial blood pressure values. In some embodiments,
metadata values may be provided to an empirical blood pressure
model (discussed below) via sets of feature vectors (discussed
below) and/or be provided separately to the model.
[0227] The wave records 1430 may include sets of waves of a signal
(e.g., a signal stored in the signal database 1404), along with
subsets of those waves. The subsets of waves may comprise "high
quality" waves obtained from the waves of the signal. These subsets
of waves may provide, for example, a more accurate blood pressure
calculation that just using the signal or waves of the signal. In
some embodiments, the wave records 1430 may store some or all of
the following information: [0228] Signal Identifier: Identifier
that identifies an associated signal. [0229] Wave Identifier(s):
Identifiers for subsets of waves of the associated signal. [0230]
Subset(s) of Waves: one or more subsets of waves of the associated
signal. The subsets of waves may include raw waves, filtered waves,
and/or normalized wave values (e.g., between 0-1). The subsets of
waves may be referred to as "high quality" waves. An example wave
of a subset of waves is shown in FIG. 22. [0231] Apparatus
Identifier: Identifier that identifies the blood metrics
measurement apparatus that generated the signals. [0232] User
Account Identifier: Identifier that identifies a user account
associated with the blood metrics measurement apparatus that
generated the signals.
[0233] The wave feature records 1432 may include wave features of
associated subsets of waves of a signal. For example, wave features
may include wave peaks, wave valleys, wave edges, and/or the like.
In some embodiments, the wave feature records 1432 may store some
or all of the following information: [0234] Signal Identifier:
Identifier that identifies an associated signal. [0235] Wave
Identifier(s): Identifiers for the subsets of waves of the
associated signal. [0236] Wave Features: one or more features
obtained from the waves within the associated subsets of waves. The
wave features may include points of the waves, such as wave peaks,
wave valleys, wave edges, and/or the like. The wave features may be
stored as normalized wave values (e.g., between 0-1). Example wave
features are shown in FIG. 23, and discussed elsewhere herein.
[0237] Apparatus Identifier: Identifier that identifies the blood
metrics measurement apparatus that generated the signals. [0238]
User Account Identifier: Identifier that identifies a user account
associated with the blood metrics measurement apparatus that
generated the signals.
[0239] The feature vector records 1434 may include sets of features
generated based on the wave features of associated subsets of waves
of a signal. In some embodiments, the feature vector records 1434
may store some or all of the following information: [0240] Signal
Identifier: Identifier that identifies an associated signal. [0241]
Wave Identifier(s): Identifiers for the subsets of waves of the
associated signal. [0242] Set(s) of Feature Vectors: one or more
sets of feature vectors, each feature vector comprising features
extracted from a wave of an associated subset of waves. The values
of a feature vector may include measurement values and metric
values. For example, the measurement values may correspond to
amplitude or location points of a particular wave, and the metric
values may be generated from metric functions that use at least one
of the measurement values. The values of a feature vector may
comprise normalized values (e.g., between 0-1). An example feature
vector 2400 is shown in FIG. 24. [0243] Apparatus Identifier:
Identifier that identifies the blood metrics measurement apparatus
that generated the signals. [0244] User Account Identifier:
Identifier that identifies a user account associated with the blood
metrics measurement apparatus that generated the signals.
[0245] The blood pressure model records 1436 may include one or
more empirical blood pressure models (e.g., retrieved from the
blood metrics server 906). The models may include various types of
empirical blood pressure models. For example, a first type may be a
"non-specific" model which does not require calibration in order to
be used to calculate arterial blood pressure values. A second type
may be a "specific" model which requires calibration in order to be
used to calculate arterial blood pressure values. For example,
models of the second type may require information about the user,
such age, weight, height, gender, skin color, and/or the like. In
some embodiments, the blood pressure records 1436 may store some or
all of the following information: [0246] Model Identifier:
Identifies an empirical blood pressure model. [0247] Model Type:
Identifies a type of model (e.g., non-specific or specific). [0248]
Model Parameters: Various model parameters (e.g., decision node
parameters) and tree structures used to calculate the arterial
blood pressure values based on the sets of feature vectors and/or
other related information (e.g., gender, age, weight, height, skin
color, etc.). Example tree structures are shown in FIG. 26. [0249]
Apparatus Identifier(s): Identifier(s) that identify one or more
blood metrics measurement apparatus' using the empirical blood
pressure model. [0250] User Account Identifier(s): Identifier(s)
that identify one or more user account(s) associated with the blood
metrics measurement apparatus that use the empirical blood pressure
model.
[0251] The blood pressure results records 1438 may include one or
more calculated arterial blood pressure values. In some
embodiments, the blood pressure results records 1438 may store some
or all of the following information: [0252] Blood Pressure Result
Identifier: Identifies a set of one or more calculated arterial
blood pressure values. [0253] Blood Pressure Values: one or more
calculated arterial blood pressure values. [0254] Date: A date
and/or time the arterial blood pressure was calculated. [0255]
Messages: Message identifier and/or messages generated based on the
calculated blood pressure values. [0256] Signal Identifier:
Identifier that identifies an associated signal. [0257] Wave
Identifier(s): Identifiers for the subsets of waves of the
associated signal. [0258] Feature Vectors Identifier(s):
Identifiers for the one or more sets of feature vectors used to
calculate the arterial blood pressure values. [0259] Apparatus
Identifier: Identifier that identifies the blood metrics
measurement apparatus that generated the signals. [0260] User
Account Identifier: Identifier that identifies a user account
associated with the blood metrics measurement apparatus that
generated the signals.
[0261] Pre-Processing Rules 1440
[0262] The pre-processing rules 1440 define attributes and/or
functions for filtering signals. For example, a signal may be
corrupted with noise from various sources (e.g., high frequency
ambient noise, electronic noise, and the like). In some
embodiments, the signal may be the raw signal data from a
photodiode, output of a pressure sensor, output of an
accelerometer, output of a gyroscope, and the like. The signal may
be filtered to remove corrupting noise, as well as enhance a
strength of the signal.
[0263] The pre-processing module 1418 may be configured to execute
the pre-processing rules 1440. Thus, for example, the
pre-processing module 1418, using some or all of the associated
signals and/or values stored in the signal records 1428, may filter
signals and store the resulting filtered signals. The wave database
1406 may be configured to store the signals filtered by the
pre-processing module 1418.
[0264] Wave Selection Rules 1442
[0265] The wave selection rules 1442 define attributes and/or
functions for selecting (or, "extracting") high quality waves from
a set of waves of a signal. The high quality waves may form a
subset of waves.
[0266] In some embodiments, signal measurements are sensitive to
motion, pressure, and ambient light distortions. Noise-removal
filters may not be entirely effective when there is intense noise.
It may be helpful to select high quality signal waves from measured
time-series data to reduce or eliminate the effects of motion,
pressure, and/or ambient light distortions.
[0267] In some embodiments, the wave selection rules 1442
identifies candidate waves from a signal, and determines whether
any of the candidate waves satisfy a model match criterion (e.g., a
Gaussian mixture model or group similarity model). Candidate waves
that pass the model match criterion may be classified as high
quality waves. In some embodiments, candidate waves includes waves
having valleys whose frequency match, or substantially match, the
heart rate of the user or other heart rate value (e.g. a default
heart rate value).
[0268] A group similarity model may be preferable. For example,
while a Gaussian mixture model may perform well with "smoothed"
signals (e.g., low pass filtered at 4-6 Hz), the Gaussian mixture
model may fail, or perform poorly, if the signal includes more
features. FIG. 22 depicts an example smoothed signal 2202, and FIG.
28 depicts an example signal including more features (e.g., low
pass filtered at 10 Hz revealing multiple reflections including,
for example, secondary peaks, such as iliac and renal reflections).
In some embodiments, the Gaussian mixture model may be unable to
match more than 2 peak results in a reflection of waves that reveal
features critical in the prediction (or, "estimation) of blood
pressure. In some embodiments, additional Gaussian mixture models
may be incorporated to address such issues, although this may lead
to added complexity in optimization (e.g., model fitting), as well
as added constraints to ensure specificity to physiologically
meaningful signals against noise.
[0269] In some embodiments, a group similarity model may overcome
some or all of the limitations described above. Generally, high
quality (or high fidelity) waves that correspond with arterial
blood flow corresponding to a single cycle typically occur in
groups. In cases where there is motion, it may be unlikely that an
individual wave would have a high quality when waves before it were
not of high quality. In the group similarity model, candidate waves
may be individually matched to an existing wave buffer (e.g., of
the wave database 1406). In some embodiments, the wave buffer may
include some or all previous candidates waves added in succession
in a first in first out (FIFO) method. The size of the wave buffer
may be set to include a predetermined number of waves (e.g., 15
waves), and may be altered. For example, increasing the wave buffer
to include additional candidate waves may require that candidate
waves match a larger set of previously extracted candidate waves.
In some embodiments, in order to measure similarity, the candidate
waves may be re-parameterized to a fixed length (e.g., using a
cubic spline).
[0270] In various embodiments, the group similarity model applies a
similarity measure to determine if, and to what extent, a candidate
wave matches some or all of the existing waves in the wave buffer.
In one example, the similarity measure may be an average
correlation between the candidate wave and the individual waves in
the wave buffer. In other embodiments, the similarity measure may
include a like sum of squared differences, joint entropy or mutual
information, and the like. In this example, if the similarity
measure is greater than a threshold (e.g. 0.95 for correlation),
this wave is selected for further processing. In some embodiments,
both the candidate waves that are selected for further processing
and the candidate waves that are not selected for further
processing may be added to the wave buffer (e.g., for subsequent
applications of the group similarity model).
[0271] The group similarity model may process waves with multiple
reflections (e.g., as depicted in FIG. 28). In some embodiments,
the group similarity model may continuously update a template
(e.g., the wave buffer) against which incoming candidate waves are
matched, and that template may indicate the true underlying signal,
which may be different from user to user, instead of relying on a
predetermined template. The group similarity model may reduce false
positive identification of candidate waves relative to other models
(e.g., Gaussian mixture model).
[0272] The wave selection module 1420 may be configured to execute
the wave selection rules 1442. Thus, for example, the wave
selection module 1420, using some or all of the associated waves
and/or values stored in the signal records 1428, may identify one
or more subsets of "high quality" waves. The wave database 1406 may
be configured to store the subsets of waves identified by the wave
selection module 1420. An example result of execution of the wave
selection rules 1442 is shown in FIG. 22.
[0273] Feature Extraction Rules 1444
[0274] The feature extraction rules 1444 define attributes and/or
functions for identifying (or, "extracting") features from waves
(e.g., high quality waves). In some embodiments, features may be
concatenated to form feature vectors.
[0275] Features may include, but are not limited to, pulse transit
time (PTT) features, reflection features, signal level and range
features, signal metric features, optical ratio features, heart
rate features, wave width and derivative features, user information
features, and/or pressure features. In some embodiments, PTT
features include a transit time feature, joint entropy feature, and
wave type feature. Pulse transit time may be measured as the time
taken for the pulse pressure wave to propagate along the length of
the arterial tree. For example, this may be the difference in time
between the onset of the R-wave on ECG, and the pulse wave peak on
the finger. In other embodiments, the transit time may be measured
as the time taken for blood to travel from one LED-PD system to
another LED-PD system (e.g., as depicted in FIG. 10B), which may be
separated from each other by a predetermined distance (e.g., 15
mm). Sampling at a very high rate (>2 KHz) may help resolve
features in both LED-PD systems. In some embodiments, the transit
time may be measured as (1) the distance between the valleys in the
corresponding waves in respective LED-PD systems at the start of
the systolic cycle, and/or (2) the distance between the peaks in
first derivatives between the two LED-PD systems. The transit time
may be expressed in any units (e.g., milliseconds).
[0276] The joint entropy feature may be a measurement of similarity
between fast channels of respective LED-PD systems. High pulse
pressure may correlate with low entropy or high mutual information
shared between the fast LED channels. The joint entropy feature may
be expressed in bytes.
[0277] Wave type features may include different types of waves. For
example, different types of waves may include slow type waves and
fast type waves. In some embodiments, the wave types feature
quantifies the relative position of the systolic peak within the
wave (0<WaveType<1) for each of the fast channels in
respective LED-PD systems, where small values correspond to a slow
type waves and larger values correspond to fast type waves.
[0278] A pulse decomposition may track a pulse pressure wave. As
the pressure wave travels in the arterial systems, it may encounter
branching points where the diameter of arteries may decrease
rapidly. The pressure wave may bounce on such branching points and
send reflection waves in different (e.g., opposite) directions. In
some embodiments, the original pressure wave plus the reflections
may form the observed pressure pulse, which in turn may be observed
in a signal (e.g., PPG signal). Multiple reflections (e.g., four
reflections) may be used to identify multiple features and
amplitudes (e.g., four features and four amplitudes). The time of
occurrence of the systolic peak from the start of the systolic
cycle (i.e., systolic peak time) may also be included as a feature.
In some embodiments, a ratio between the second derivatives at the
bottom and peak of the systolic cycle may be used as a feature.
This feature may be correlated with arterial stiffness. In some
embodiments, ten features may be individually identified for each
LED channel.
[0279] The signal level and range features may comprise the mean
value and range of a signal (e.g., PPG signal) determined for each
of the channels. In some embodiments, the signal metrics features
include Hjorth parameters, perfusion, kurtosis, and energy
features. Hjorth parameters may describe activity, mobility and
complexity of the signal, and are commonly used tools in EEG
analysis. Perfusion may be measured as the ratio between the AC and
DC components of the signal (i.e. the range of the signal and the
mean). Kurtosis may be measured over the signal analysis window.
Energy features may include variance of the signal measured over
the analysis window. In some embodiments, optical ratio features
may be calculated as the ratio of the range of the LEDs (measured
in pairs across wavelengths) after normalization of the respective
signals, and heart rate features may be an actual user heart rate
or a default heart rate.
[0280] In some embodiments, the wave width and derivative features
may include time distances within a wave to its main peak at
different amplitude locations. One example is shown in FIG. 25A for
0% amplitude location. In FIG. 25A, d1 and d2 represent the
distance between the rising (falling) edge value of the wave and
its main (systolic) peak. The secondary (diastolic) peak may be
extracted from the first order derivative (FOD) of the wave. For
this, FOD may be smoothed (e.g., a simple moving average filter may
be used). In some embodiments, after extracting main (systolic) and
secondary (diastolic) peaks, reflection (augmentation) index (the
ratio of diastolic peak and systolic peak amplitudes), inflection
point area ratio (the ratio of areas under the wave that are
separated by the diastolic inflection point), and/or stiffness
index (the ratio of patient's height to the time distance between
the systolic and diastolic peaks) may be determined from the first
order derivative of the wave.
[0281] In the example of FIG. 25C, the second order derivative of
the wave has multiple peaks and valley points (e.g., labeled (a),
(b) and (e)). In some embodiments additional peak and valleys may
be present. If the sampling frequency is low or under exercise
conditions (e.g., about 25 Hz in FIG. 25C), only waves (a), (b) and
(e) may be identifiable. In some embodiments, for higher sampling
frequencies (e.g., .gtoreq.200 Hz), additional peaks and valleys
may be identifiable. The ratio of these values may be included into
a feature vector.
[0282] As indicated, an example selected (or, extracted) wave,
first order derivative of the selected wave, and the second
derivative of the selected wave are shown in FIGS. 25A-C,
respectively.
[0283] In some embodiments, if two LEDs at the same wavelength are
positioned at different locations of the same artery, a phase shift
may be obtained between the measured signals (e.g., due to blood
flow). This may facilitate calculation of pulse wave velocity (PWV)
and/or pulse transit time (PTT), both of which may be included in a
feature vector, and/or otherwise provided to the empirical blood
pressure model used to calculate arterial blood pressure
values.
[0284] The user information features may include, for example, a
user's gender, age, skin color, height and/or weight. The user
information may be included in a feature vector, and/or otherwise
provided to the empirical blood pressure model used to calculate
arterial blood pressure values. In some embodiments, pressure
features may include the mean value of the pressure signal over the
analysis window (or "baseline mean") and range of the pressure
signal over the analysis window (or, "AC").
[0285] The feature extraction module 1422 may be configured to
execute the feature extraction rules 1444. Thus, for example, the
feature extraction module 1420, using some or all of the associated
subsets of waves and/or values stored in the wave records 1430, may
identify one or more feature of the waves within the subsets of
waves, and generate corresponding sets of feature vectors. An
example of a feature vector 2400 is shown in FIG. 24. The wave
feature database 1408 may be configured to store the wave features
identified by the feature extraction module 1422, and the feature
vector database 1410 may be configured to store the generated sets
of feature vectors.
[0286] Blood Pressure Processing Rules 1446
[0287] The blood pressure processing rules 1446 define attributes
and/or functions for calculating arterial blood pressure values of
a user. In some embodiments, the blood pressure processing rules
1446 specify, identify, and/or define the empirical blood pressure
model to use for calculating arterial blood pressure of a user. The
rules 1446 may further define input values for the empirical blood
pressure model. For example, input values may comprises the sets of
features vectors, and/or other attributes of the user (e.g., age,
gender, height, weight, skin color, etc.), assuming such attributes
have not been included in the feature vectors.
[0288] The blood pressure processing module 1424 may be configured
to execute the blood pressure processing rules 1446. Thus, for
example, the blood pressure processing module 1424, using an
empirical blood pressure model stored in the blood pressure model
database 1412, along with some or all of the associated sets of
feature vectors and/or values stored in the feature vector records
1438, may calculate one or more arterial blood pressure values. The
blood pressure results database 1414 may be configured to store the
blood pressure values calculated by the blood pressure processing
module 1424.
[0289] Message Rules 1448
[0290] The message rules 1448 define attributes and/or functions
for generating messages and/or alerts based on arterial blood
pressure values. In some embodiments, the message rules 1448 may
define rules that cause the blood pressure calculation system 1206
to provide calculate blood pressure values to a user. In some
embodiments, the message rules 1448 may include threshold values
and/or conditions that when exceeded and/or satisfied, trigger a
message or alert. For example, a threshold value (or value range)
and/or threshold condition may be associated with varying blood
pressure levels (e.g., hypotension, normal blood pressure,
prehypertension, stage 1 hypertension, stage 2 hypertension, etc.),
and a calculated blood pressure value which satisfies a
corresponding threshold condition or value may trigger a message or
alert (e.g., indicating the corresponding blood pressure
level).
[0291] In some embodiments, the communication module 1426 may be
configured to execute the message rules 1448. Thus, for example,
the communication module 1420, using some or all of the blood
pressure values stored in the blood pressure results records 1438,
may generate one or more messages. The communication module 1426
may be configured to provide those messages to a user.
[0292] In some embodiments, the communication module 1426 may be
configured to send requests to and receive data from one or a
plurality of systems. The communication module 1426 may send
requests to and receive data from a systems through a network or a
portion of a network. Depending upon implementation-specific or
other considerations, the communication module 1426 may send
requests and receive data through a connection (e.g., the
communication network 908, and/or the communication link 910), all
or a portion of which may be a wireless connection. The
communication module 1426 may request and receive messages, and/or
other communications from associated systems.
[0293] FIG. 15A depicts a flowchart 1500 of an example method of
operation of a blood pressure calculation system (e.g., blood
pressure calculation system 1206) according to some
embodiments.
[0294] In step 1502, the blood pressure calculation system stores
one or more empirical blood pressure models. For example, the one
or more empirical blood pressure models may be received from a
blood metrics server (e.g., blood metrics server 906), and may
comprise specific or non-specific empirical blood pressure models.
In some embodiments, a management module (e.g., management module
1402) stores the one or more empirical blood pressure models in a
blood pressure model database (e.g., blood pressure results
database 1414), and/or a communication module (e.g., communication
module 1426) receives the one or more empirical blood pressure
models.
[0295] In step 1504, the blood pressure calculation system receives
a first signal (e.g., a single-channel or multi-channel PPG signal)
and a second signal (e.g., a single-channel or multi-channel PPG
signal). For example, the first signal may comprise a 7-channel PPG
signal generated from light energy emitted at seven different
wavelengths (e.g., 523 nm, 590 nm, 623 nm, 660 nm, 740 nm, 850 nm,
940 nm) from one or more light sources (e.g., seven different
LEDs), and the second signal may comprise a PPG signal generated
from light energy generated from an additional light source (e.g.,
LED) at the same, or substantially similar, wavelength as one the
wavelengths of the first signal. Using multi-channel signals and/or
different light sources may help ensure, for example, that good
quality signals may be obtained in a variety of circumstances
(e.g., a user moving, walking, running, sleeping, and so forth). By
way of further example, the first and/or second signals may
comprise a pressure pulse signal. In some embodiments, a
communication module communication module (e.g., communication
module 1426 and/or communication module 1208) receives the first
and second signals from a blood metrics measurement apparatus
(e.g., blood metrics measurement apparatus 902).
[0296] In step 1504, the blood pressure calculation system
pre-processes (or, filters) the first and second signals. In some
embodiments, a pre-processing module executes pre-processing rules
to filter the first and second signals. An example filtering method
is shown and described with reference to FIG. 15B.
[0297] In steps 1508-1510, the blood pressure calculation system
identifies a first subset of waves (or, "high quality" waves) from
a first set of waves of the first signal and a second subset of
waves (or, "high quality" waves) from a second set of waves of the
second signal. Each of the first subset of waves may represent a
separate approximation of an average of the first set of waves over
a first predetermined amount of time (e.g., 8 seconds). Similarly,
each of the second subset of waves may represent a separate
approximation of an average of the second set of waves over a
second predetermined amount of time (e.g., 8 seconds). In some
embodiments, the first and second predetermined amounts of time may
be the same or they may be different. In some embodiments, a wave
selection module (e.g., wave selection module 1420) identifies the
subsets of waves.
[0298] In steps 1512-1514, the blood pressure calculation system
generates a first set of feature vectors and a second set of
feature vectors. The first set of feature vectors may be generated
from the first subset of waves, and the second set of feature
vectors may be generated from the second subset of waves. Each of
the feature vectors may include measurement values and/or metric
values. For example, the measurement values may correspond to
amplitude (e.g., peak-to-peak amplitude, peak amplitude,
semi-amplitude, root mean square amplitude, pulse amplitude, etc.)
and/or location points of a particular wave (e.g., a corresponding
high quality wave), and the metric values may be generated from
metric functions that use at least one of the measurement values.
In some embodiments, a feature extraction module (e.g., feature
extraction module 1420) generates the sets of feature vectors.
[0299] In some embodiments, measurement values may include wave
peak locations and/or amplitudes, wave valley locations and/or
amplitudes, a wave's first or higher order derivative peak
locations and/or amplitudes, a wave's first or higher order
derivative valley locations and/or amplitudes, and/or or first or
higher order moments of a wave. In various embodiments, the metric
functions may include one or more particular metric functions that
calculate a distance between a plurality of measurement values. For
example, a metric function may calculate a distance between
location points of a particular wave (e.g., a particular wave peak
and a particular wave valley).
[0300] In step 1516, the blood pressure calculation system may
select an empirical blood pressure model. For example, the blood
pressure calculation system may select an empirical blood pressure
model from the one or more empirical blood pressure models stored
in the blood pressure model database. In some embodiments, a blood
pressure processing module (e.g., blood pressure processing module
1422) selects the empirical blood pressure model.
[0301] In some embodiments, the blood pressure calculation system
may operate in different modes. For example, in a first mode of
operation, the blood pressure calculation system may utilize a
non-specific type of empirical blood pressure model which does not
require further calibration in order to be used to calculation
arterial blood pressure values. Accordingly, in such a first mode
of operation, selecting the empirical blood pressure model may
comprise selecting the non-specific empirical blood pressure model
from the empirical blood pressure models stored in the blood
pressure database. To continue the example, in a second mode of
operation, the blood pressure calculation system may utilize a
specific type of empirical blood pressure model which requires at
least some calibration prior to being used to calculate arterial
blood pressure values. In such a second mode of operation,
selecting an empirical blood pressure model may comprise selecting
an empirical blood pressure model from the one or more blood
pressure models stored in the blood pressure based on one or more
attributes of the user (e.g., gender, weight, height, skin color,
and/or age) and/or other parameters. In some embodiments, a blood
pressure processing module (e.g., blood pressure processing module
1422) selects the empirical blood pressure model.
[0302] In step 1518, the blood pressure calculation system
calculates one or more arterial blood pressure values based on the
first set of feature vectors, the second set of feature vectors,
and an empirical blood pressure calculation model (e.g., the model
selected in step 1516). For example, the empirical blood pressure
calculation model may be configured to receive the first set of
feature vectors and the second set of feature vectors as input
values. In some embodiments, the blood pressure processing module
calculates the one or more arterial blood pressure values.
[0303] In step 1520, the blood pressure calculation system may
generate and/or provide a message including or being based on the
arterial blood pressure. In some embodiments, the communication
module generates and/or provides the message to a user.
[0304] FIG. 15B depicts a flowchart 1530 of an example method of
pre-processing (or, "filtering") a signal according to some
embodiments. Although only one signal is discussed here, it will be
appreciated that multiple signals may be filtered (e.g.,
simultaneously) using some or all of the steps described below.
[0305] In step 1532, a blood pressure calculation system 1206
receives a signal at least partially corrupted with noise (or, a
"noisy" signal). For example, the signal may be an output from a
photodiode, a pressure sensor, an accelerometer, a gyroscope, or
the like. The noise may include one or more types of noise, such as
high frequency ambient noise, electronic noise, and so forth. For
example, the noise may be motion-related noise associated with
venous blood movement, changes in sensor coupling with the tissue
of the user, and so forth. In some embodiments, a communication
module (e.g., communication module 1426) receives the signal from a
blood metrics measurement apparatus.
[0306] In step 1534, the blood pressure calculation system 1206
generates and/or applies a low pass filter to the signal. For
example, the blood pressure calculation system 1206 may apply a
finite impulse response (FIR) low pass filter with a predetermined
cut off frequency (e.g., 10 Hz) or frequency range (e.g., 6 Hz-12
Hz). In some embodiments, the low pass filter may be configured to
pass signals having a frequency lower than the predetermined cut
off frequency, and/or configured to attenuate signals having
frequencies higher than the cutoff frequency. In some embodiments,
a pre-processing module applies the low pass filter.
[0307] In step 1536, the blood pressure calculation system 1206
normalizes the sign. For example, the blood pressure calculation
system may normalizes the signal by subtracting and dividing by the
mean (DC) value of the signal. In some embodiments, the
pre-processing module 1418 normalizes the signal.
[0308] In step 1538, the blood pressure calculation system 1206
inverts the signal. For example, the blood pressure calculation
system may invert the signal such that an increasing in amplitude
corresponds with an increase in volume in the arteries. In some
embodiments, the pre-processing module 1418 inverts the signal.
[0309] In step 1540, the blood pressure calculation system 1206
applies a high pass filter to the signal. In some embodiments, the
high pass filter may be configured to pass signals having a
frequency higher than a certain cutoff frequency, and/or configured
to attenuate signals having frequencies lower than the cutoff
frequency. For example, the high pass filter may be an FIR filter
having a predetermined cut off frequency (e.g., 0.6 Hz). In another
example, the high pass filter may be a Savitzksy-Golay filter
configured to remove low frequency fluctuations arising from
respiration rate.
[0310] In step 1542, the blood pressure calculation system 1206
performs a blind source separation on the signal to isolate signals
associated with arteries from the venous pulsations. In some
embodiments, this step does not apply to non-optical signals. In
various embodiments, signals may typically contain both venous and
arterial components to varying degrees, and signal data collected
from a channel may be dependent on the position on the tissue of a
user where the data is collected, as well as the wavelength of the
light source, which may have an effect on the depth of penetration
of that light wavelength into tissue, as well as scattering. If a
channel is composed of two independent components (e.g., arterial
and venous signals), the components may be isolated by using
multiple channels using independent component analysis. In some
embodiments, the pre-processing module performs the blind source
separation.
[0311] In step 1544, the blood pressure calculation system 1206
generates and/or applies an adaptive filter to the signal. In some
embodiments, the adaptive filter removes noise from the signal
using one or more other signals that may be related to the noise as
a noise reference. For example, the adaptive filter may be
configured to remove motion related noise from the signal using an
accelerometer signal as a noise reference. In some embodiments,
motion information may be captured by an accelerometer along three
axis (e.g., x, y, and z-axis). Motion along each access may be
captured by a component of the accelerometer. In various
embodiments, the blood pressure calculation system calculates a sum
of the three accelerometer components, and uses the sum as a noise
reference. Alternately, the blood pressure calculation system 1206
may determine the largest varying component and uses that component
as the reference. In some embodiments, the blood pressure
calculation system may use each component successively to form
three adaptive filters in sequence. In some embodiments, the
pre-processing module generates and/or applies the adaptive
filter.
[0312] In step 1546, the blood pressure calculation system 1206
outputs a filtered signal (i.e., the signal filtered by some or all
of the steps 1532-1544). In some embodiments, the pre-processing
module 1418 outputs the filtered signal.
[0313] FIG. 15C depicts a blocks diagram 1550 of an example
filtering system 1552 for filtering signals according to some
embodiments. The filtering system 1552 includes an adaptive filter
1554 that may be continuously updated. The adaptive filter 1554 may
be dynamically modified such that its convolution with the noise
reference 1556 creates a noise component that is maximally similar
to the motion related noise in the signal 1558 corrupted with
motion noise. The filtering system 1552 may be configured to
optimize the adaptive filter 1552 to minimize the output signal
1560 from the adaptive filter 1552. For example, the output signal
1560 may be minimal when a filtered version 1562 of the noise
reference 1556 has removed a threshold portion (e.g., greater than
80%) of the corresponding noise present in the noise-corrupted
signal 1558. Various minimization methods may be used (e.g.,
recursive least square minimization) to produce the minimized
output signal 1560.
[0314] FIG. 16A depicts a flowchart 1600 of an example method for
extracting high quality waves according to some embodiments. For
example, some or all of the following steps may be applied to a
segment (e.g., an 8 second segment or 12 second segment) of each
channel of a multi-channel signal to identify subsets of waves of
the multi-channel signal.
[0315] In step 1602, the blood pressure calculation system (e.g.,
blood pressure calculation system 1206) identifies peak values
smaller (or, less) than a mean subtracted by a standard deviation
of the signal. In some embodiments, a wave selection module
performs the identification.
[0316] In step 1604, the blood pressure calculation system selects
peak valleys whose distances with a next peak valley are within a
threshold value (e.g., 5%) of a heart rate value and whose
intensities are within a threshold value (e.g., 5%) of each other.
In some embodiments, the wave selection module performs the
selection.
[0317] In step 1606, the blood pressure calculation system
determines whether there is only one identifiable peak between the
detected valleys, whose intensity value is greater than a mean plus
one standard deviation. In some embodiments, the wave selection
module performs the determination.
[0318] In step 1608, the blood pressure calculation system
identifies one or more candidate waves. For example, the one or
more candidate waves may be identified from steps 1602-1606. As
used herein, it will be appreciated that candidate waves may also
be referred to as "model candidate waves." In some embodiments, the
wave selection module performs the identification.
[0319] In step 1610, the blood pressure calculation system
determines if one or more of the candidate waves satisfy a model
match criterion (e.g., a Gaussian mixture model or a group
similarity model). An example method using a Gaussian mixture model
is depicted in FIG. 16B, and an example method using a group
similarity model is depicted in FIG. 16C. In some embodiments, the
wave selection module performs the determination.
[0320] In step 1612, the blood pressure calculation system selects
the waves satisfying the model match criterion. The selected waves
may be grouped into subsets of "high quality" waves. In some
embodiments, the selected waves may be stored in a training dataset
with the associated channel number. In some embodiments, the wave
selection module performs the selection and/or storage.
[0321] FIG. 16B depicts a flowchart 1620 of an example method for
extracting high quality waves using a Gaussian mixture model
according to some embodiments. For example, some or all of the
following steps may be applied to a segment of each channel of a
multi-channel signal to identify subsets of waves of the
multi-channel signal.
[0322] In step 1622, the blood pressure calculation system (e.g.,
blood pressure calculation system 1206) identifies one or more
candidate waves. For example, the one or more candidate waves may
be identified using steps 1602-1606. In some embodiments, a wave
selection module performs the identification.
[0323] In step 1624, the blood pressure calculation system
determines whether a candidate wave after normalization (e.g.,
0-1), satisfies a bi-Gaussian mixture model and the model mismatch
error is within a predetermined (e.g., user-defined) threshold
value (e.g., 2%). In some embodiments, the wave selection module
performs the determination. FIG. 27 depicts an example bi-Gaussian
mixture model 2700 for a PPG signal.
[0324] In step 1626, the blood pressure calculation system
determines whether the fitted model has a reflection coefficient
between minimum and maximum possible values (e.g., 0.3-0.7), where
the reflection coefficient is defined as the ratio of the diastolic
peak to the systolic peak values. In some embodiments, the wave
selection module performs the determination.
[0325] In step 1628, the blood pressure calculation system selects
the candidate waves satisfying the conditions of steps 1622-1626.
The selected candidate waves may comprise high quality waves. The
high quality waves may be stored in a wave database. In some
embodiments, the wave selection module performs the selection
and/or storage.
[0326] FIG. 16C depicts a flowchart 1640 of an example method of
method for extracting high quality waves using a group similarity
model according to some embodiments. For example, some or all of
the following steps may be applied to a segment of each channel of
a multi-channel signal to identify subsets of waves of the
multi-channel signal.
[0327] In step 1642, the blood pressure calculation system
identifies one or more candidate waves. For example, the one or
more candidate waves may be identified using steps 1602-1606. In
some embodiments, a wave selection module performs the
identification.
[0328] In step 1644, the blood pressure calculation system applies
a similarity measure to determine if at least one of the one or
more candidate waves match one or more other waves (e.g., waves
stored in a wave buffer). In some embodiments, the wave selection
module applies the similarity measure.
[0329] In step 1646, if at least one candidate wave matches the
other waves, the blood pressure calculation system selects and
identified the at least one candidate wave as high quality (step
1648), and stores the at least one candidate wave with the other
waves (step 1650). The candidate waves that do not match may also
be stored with the other waves (step 1650). In some embodiments,
the wave selection performs the selection, identification, and/or
storage.
[0330] FIG. 17 depicts a block diagram 1700 of a blood metrics
server 906 according to some embodiments. Generally, the blood
metrics server 906 may be configured to generate and/or store
empirical blood pressure models, provide empirical blood pressure
models to a blood pressure calculation system, register user
account and blood metrics measurement apparatus`, and communicate
with other systems of the system and environment 900. In some
embodiments, the blood metrics server 906 includes a management
module 1702, a registration database 1704, a blood pressure
database 1706, a model generation and selection module 1708, and a
communication module 1710.
[0331] The management module 1702 may be configured to manage
(e.g., create, read, update, delete, or access) user account
records 1714 and blood metrics measurement apparatus records 1716
stored in the registration database 1704, and blood pressure model
records 1718 stored in the blood pressure model database 1706. The
management module 1402 may perform these operations manually (e.g.,
by an administrator interacting with a GUI) and/or automatically
(e.g., by one or more of the modules 1708 or 1710). In some
embodiments, the management module 1702 comprises a library of
executable instructions which are executable by a processor for
performing any of the aforementioned management operations. The
databases 1704 and 1706 may be any structure and/or structures
suitable for storing the records 1714-1718 (e.g., an active
database, a relational database, a table, a matrix, an array, a
flat file, and the like).
[0332] The user account records 1714 may include a variety of
information associated with users, user accounts, associated blood
metrics measurement apparatus' and/or associated user devices. For
example, the user account records 1714 may store some or all of the
following information: [0333] User Account Identifier: Identifier
that identifies a user account. [0334] Password: Password, or other
personal identifier, used to authenticate the user account. For
example, it may an alphanumerical password, biometric data (e.g.,
fingerprint, etc.). In some embodiments, readings or measurements
from the blood metrics measurement apparatus 902 may be used to
authenticate the user account. [0335] Device Identifier(s):
Identifier(s) that identify one or more blood metrics measurement
apparatus' associated with the user account and/or user device.
[0336] Name: A name of the user. [0337] DOB: A date of birth of the
user. [0338] Age: An age of the user. [0339] Gender: Gender of the
user (e.g., female, male, transgender, or the like). [0340] Weight:
A weight of the user. [0341] Height: A height of the user. [0342]
Skin color: A skin color of the user. [0343] Activity Level: An
activity level of the user (e.g., sedentary, lightly active,
active, very active, and so forth). [0344] Geographic location: A
location of the user (e.g., as determined by a location service
and/or specified by the user). [0345] Blood Pressure Profile:
Hypertensive, Hypotensive, Normal or unknown. [0346] Blood Glucose
Profile (e.g., Diabetes information) [0347] Wrist circumference:
Circumference of the user's wrist.
[0348] The blood metrics measurement apparatus registration records
1716 may include a variety of information associated with users,
user accounts, associated blood metrics measurement apparatus'
and/or associated user devices. For example, the blood metrics
measurement apparatus registration records 1716 may store some or
all of the following information: [0349] Apparatus Identifier:
Identifier that identifies a blood metrics measurement apparatus.
[0350] User Account Identifier: Identifier that identifies a user
account and/or user device associated with the blood metrics
measurement apparatus. [0351] Geographic location: A current
location of the blood metrics measurement apparatus (e.g., as
determined by a location service and/or specified by the user).
[0352] Settings: One or more settings of the blood measurement
metrics apparatus.
[0353] For example, some or all of the settings may be
automatically determined based on one or more user account
attributes (e.g., height, weight, etc.) and/or by the user.
[0354] The blood pressure model database 1706 may include one or
more empirical blood pressure model records 1718. The models may
include various types of empirical blood pressure models. For
example, a first type may be a non-specific model which does not
require calibration in order to be used to calculate arterial blood
pressure values. A second type may be a specific model which
requires calibration in order to be used to calculate arterial
blood pressure values. For example, models of the second type may
require information about the user, such age, weight, height,
gender, skin color, and/or the like. In some embodiments, the blood
pressure records 1718 may store some or all of the following
information: [0355] Model Identifier: Identifies an empirical blood
pressure model. [0356] Model Type: Identifies a type of model
(e.g., non-specific or specific). [0357] Model Parameters: Various
model parameters (e.g., decision node parameters) and tree
structures used to calculate the arterial blood pressure values
based on the sets of feature vectors and/or other related
information (e.g., gender, age, weight, height, skin color, etc.).
Example tree structures are shown in FIG. 26. [0358] Apparatus
Identifier(s): Identifier(s) that identify one or more blood
metrics measurement apparatus' using the empirical blood pressure
model. [0359] User Account Identifier(s): Identifier(s) that
identify one or more user account(s) associated with the blood
metrics measurement apparatus that use the empirical blood pressure
model.
[0360] The model generation and selection module 1708 may be
configured to generate empirical blood pressure models and/or
identify an empirical blood pressure models stored in the records
1718 to provide to a blood pressure calculation system. For
example, an empirical blood pressure model may be identified in
response to a request from a user device or a blood pressure
calculation system. In various embodiments, the model generation
and selection module 1708 identifies a non-specific empirical blood
pressure model which does not require further calibration prior to
being used by a blood pressure calculation system to calculate
arterial blood pressure values. In some embodiments, the model
generation and selection module 1708 includes some or all of the
functionality of a wave selection module, feature extraction
module, blood pressure processing module, along with associated
features and/or functionality (e.g., a signal database, wave
database, and so forth).
[0361] In one example, an empirical blood pressure model may be
identified from a set of models (e.g., stored in the records 1718)
by the model generation and selection module 1708 based upon a
modified Random Forests algorithm using random decision trees in a
regression mode. For example, decision trees may comprise a
plurality of nodes for decision making. Two examples of decision
trees are shown in FIG. 26. Each internal node may correspond to
one comparison point of the input values (e.g., one feature
(F.sub.i, i=1, . . . ,N) a feature vector) and edges may represent
the path of the outcomes. Leaves (the end points) may represent
desired target points. In this example, target points are
equivalent to particular blood pressure values.
[0362] In some embodiments, a specified (or, predetermined) number
of decision trees are constructed. Each decision tree may result in
a blood pressure target, but majority voting may give a single
blood pressure estimate (or, calculation). In a model training (or,
"calibration") phase, the decision node parameters (a.sub.1, . . .
,a.sub.N, b.sub.1, . . . ,b.sub.N, . . . ), combination of
features, and the structure of the decision trees may be learned,
for example, by randomly subsampling the training data and the
features, and tracking the error in an unobserved part of the
training data. In some embodiments, these parameters and tree
structures may comprise the empirical blood pressure models
generated and/or stored the blood metrics server 906, and/or used
by the blood pressure calculation system 1206 to calculate arterial
blood pressure values.
[0363] In some embodiments, grown decision trees may learn highly
complex patterns. However, they may be prone to overfitting (e.g.,
sensitive to noise). The model generation and selection module 1708
may address this problem. For example, a tree bagging (or,
bootstrap aggregating) technique, in which samples are randomly
selected with replacement or without replacement from a training
list. The assignment of unseen samples may be performed by taking a
majority vote. Although one decision tree may be sensitive to
noise, using multiple decision trees and averaging results may
significantly decrease the variance as long as the trees are not
correlated. In some embodiments, the model generation and selection
module 1708 combines tree bagging with feature bagging where the
subset of features are also selected randomly with replacement. In
some embodiments, the model generation and selection module 1708
may randomly subsample the training dataset without replacement. In
some embodiments, the model generation and selection module 1708
may include several parameters to be tuned for a minimum number of
leaves and/or number of trees.
[0364] In some embodiments, during a model calibration phase, the
datasets coming from different subjects may be randomly partitioned
to training sets and test sets. In some embodiments, generally,
70-80% of the datasets may be used in training, and the datasets of
the same subject may not be both training and testing sets, i.e.,
test sets and training sets should be disjointed. If the size of
the data (number of patients or users) is limited (e.g.,
.ltoreq.150), a bucketwise randomization may be applied (e.g., to
help sure that there is enough training data for certain ranges of
blood pressure). For example, the bucket edges may be selected as
80, 100, 130, 160, 180, 230. Then, for example, random partitioning
may be separately applied to data sets within a certain bucket so
that the ratio of training and test samples will be the same.
[0365] In some embodiments, the parameters of the tree bagging
technique are optimized using k-fold cross validation. Generally,
in k-fold cross validation, the existing dataset may be randomly
partitioned into training and testing k-times (e.g., k.gtoreq.1).
This may be done, for example, to decrease the bias of the results
to the selected test and training sets. Error metrics between the
ground truth and estimated blood pressure values in test dataset
are averaged k-times. Those metrics may include, and are not
limited to, mean square error (MSE, Example Equation 1, shown
below), root mean square error (RMSE, Example Equation 2, shown
below), median absolute deviation (MAD, Example Equation 3, shown
below), and/or coefficient of determination (R.sup.2, Example
Equation 4, shown below). In some embodiments, the model giving
optimal (or, the lowest) error values may be selected and used by
the blood pressure calculation system 1206 to calculate arterial
blood pressure values.
MSE ( y ^ ) = 1 N i = 1 N ( y i - y ^ i ) 2 Example Equation 1 RMSE
( y ^ ) = 1 N i = 1 N ( y i - y ^ i ) 2 Example Equation 2 MAD ( y
^ ) = median ( y - y ^ ) Example Equation 3 R 2 ( y ^ ) = 1 - M S E
( y ^ ) var ( y ) Example Equation 4 ##EQU00021##
[0366] In some embodiments, the model generation and selection
module 1708 minimizes overfitting. For example, each fold (or,
k-fold) may be individually trained. Feature sets may be identified
for each fold through backward or floating search optimizations
where features are added or removed (e.g., to improve cost). In
some embodiments, in order to enable cross-validation (CV) or
bootstrapping (BS) performance match an unseen test set accurately,
a measure of test-train cost may be calculated. This may be
performed, for example, to inform feature selection approaches for
adding or removal of features. For example, Test-Train cost may be
defined as
C T T = 1 - R M S E Train R M S E T e s t , ##EQU00022##
where C.sub.TT may be close to "0" if test error and train error
tracked each other closely, and may be "1" if train error was
smaller than test (e.g., overfitting). This may be applied, for
example, when a new feature is added (e.g., not present in the
current optimal feature set), or when an existing featured is
removed (e.g., from the current optimal feature set). In some
embodiments, the corresponding condition which must be met to
continue adding or removing a feature may be calculated as current
cost<(previous cost+.alpha. F C.sub.TT), where .alpha. is the
weight associated with the Test-Train cost, and F is "1" for
testing feature removal and "-1" for testing feature addition.
While this may have the effect of reducing model complexity while
increasing RMSE, model complexity is not directly minimized, which
could have the desired effect of allowing model complexity to be
relatively large without penalty as long as C.sub.TT is relatively
small.
[0367] In some embodiments, the communication module 1710 may be
configured to send requests to and receive data from one or a
plurality of systems. The communication module 1710 may send
requests to and receive data from a systems through a network or a
portion of a network. Depending upon implementation-specific or
other considerations, the communication module 1710 may send
requests and receive data through a connection (e.g., the
communication network 908, and/or the communication link 910), all
or a portion of which may be a wireless connection. The
communication module 1710 may request and receive messages, and/or
other communications from associated systems.
[0368] FIG. 18 depicts a flowchart 1800 of an example method of
operation of a blood metrics server (e.g., blood metrics server
906) according to some embodiments.
[0369] In step 1802, the blood metrics server receives and
processes user account and/or blood pressure measurement apparatus
registration requests. In some embodiments a communication module
(e.g., communication module 1712) receives the requests. An example
registration method is shown in FIG. 20.
[0370] In step 1804, the blood metrics server receives and
processes a request for an empirical blood pressure model. In some
embodiments, the communication module receives the request from a
user device (e.g., user device 904) and/or blood pressure
calculation system (e.g., blood pressure calculation system 1206).
For example, the user device and/or blood pressure calculation
system may periodically request an updated model.
[0371] In step 1806, the blood metrics server selects an empirical
blood pressure model. For example, the model may be selected in
response to the request, although in some embodiments, the blood
metrics server may automatically select a model in order to push
down an updated model to the user device. In some embodiments, a
model generation and selection module (e.g., model selection 1708)
performs the selection.
[0372] In step 1808, the blood metrics server provides the selected
empirical blood pressure to the user device and/or blood pressure
calculation system. In some embodiments, the communication module
provides the selected empirical blood pressure model.
[0373] FIG. 19 depicts a flowchart 1900 of an example method of
operation of a blood metrics server (e.g., blood metrics server
906) according to some embodiments.
[0374] In step 1902, the blood metrics server receives a first
signal (e.g., a single-channel or multi-channel PPG signal) and a
second signal (e.g., a single-channel or multi-channel PPG signal)
for each of a plurality of users (e.g., training subjects). For
example, the first signal may comprise a 7-channel PPG signal
generated from light energy emitted at seven different wavelengths
(e.g., 523 nm, 590 nm, 623 nm, 660 nm, 740 nm, 850 nm, 940 nm) from
one or more light sources (e.g., seven different LEDs), and the
second signal may comprise a single-channel PPG signal generated
from light energy generated from an additional light source (e.g.,
LED) at the same, or substantially similar, wavelength as one the
wavelengths of the first signal. Using multi-channel signals and/or
different light sources may help ensure, for example, that good
quality signals may be obtained in a variety of circumstances (a
user moving, walking, running, sleeping, and so forth). In some
embodiments, a communication module (e.g., communication module
1712 receives the first and second signals from a blood metrics
measurement apparatus (e.g., blood metrics measurement apparatus
902) and/or other blood pressure measurement devices, such as other
types of non-invasive devices (e.g., sphygmomanometer) and/or
invasive devices (e.g., catheters, tubes, etc.).
[0375] In step 1904, the blood metrics server pre-processes (or
filters) the first and second signals. In some embodiments, a
pre-processing module filters the first and second signals.
[0376] In step 1906-1908, the blood metrics server identifies, for
each of the plurality of users, a first subset of waves (or, "high
quality" waves) from a first set of waves of the first signal and a
second subset of waves (or, "high quality" waves) from a second set
of waves of the second signal, each of the first subset of waves
representing a separate approximation of an average of the first
set of waves over a predetermined amount of time and each of the
second subset of waves representing a separate approximation of an
average of the second set of waves over the predetermined amount of
time. In some embodiments, a model generation and selection module
(e.g., model generation and selection module 1708) identifies the
subsets of waves.
[0377] In step 19010-1912, the blood metrics server generates, for
each of the plurality of users, a first set of feature vectors and
a second set of feature vectors. The first set of feature vectors
may be generated from the first subset of waves, and the second set
of feature vectors may be generated from the second subset of
waves. Each of the feature vectors may include measurement values
and metric values. For example, the measurement values may
correspond to amplitude (e.g., peak-to-peak amplitude, peak
amplitude, semi-amplitude, root mean square amplitude, pulse
amplitude, etc.) or location points of a particular wave (e.g., a
corresponding high quality wave), and the metric values may be
generated from metric functions that use at least one of the
measurement values. In some embodiments, the model generation and
selection module generates the sets of feature vectors.
[0378] In step 1914, the blood metrics server generates a plurality
of empirical blood pressure models based on the feature vectors. In
some embodiments, the model generation and selection module
generates the models.
[0379] In step 1916, the blood metrics server identifies a
particular one of the plurality of empirical blood pressure models.
The identified model may be used to calculate arterial blood
pressure values without requiring further calibration (e.g., a
non-specific type of empirical blood pressure model). In some
embodiments, the model generation and selection module performs the
identification.
[0380] In step 1918, the blood metrics server stores the particular
one of the plurality of empirical blood pressure models. In some
embodiments, a management module (e.g., management module 1702)
stores the particular model in a database (e.g., blood pressure
model database 1706).
[0381] FIG. 20 depicts a flowchart 2000 of an example method of
operation of a blood metrics server (e.g., blood metrics server
906) according to some embodiments.
[0382] In step 2002, the blood metrics server receives a user
account registration request from a user device (e.g., user device
904) via a communication network (e.g., communication network 908).
The user account registration request may include, for example, a
username (e.g., "jsmith2015), password, full name (e.g., "John
Smith"), birthdate, gender, physical characteristics (e.g., height,
weight, etc.), and so forth. In some embodiments a communication
module (e.g., communication module 1716) receives the user account
registration request.
[0383] In step 2004, if the blood metrics server approves the
registration request, the blood metrics server may create a new
user account registration record (e.g., user account registration
record 1714) in a registration database (e.g., registration
database 1704) and/or an account for the user. In some embodiments,
a management module (e.g., management module 1702) creates the user
account registration record.
[0384] In step 2006, the blood metrics server receives a log-in
request from the user device. The log-in request may include, for
example, the username and password. If the log-in credentials are
correct, the blood metrics server logs the user account into the
user device. In some embodiments the communication module receives
the log-in request.
[0385] In step 2008, the blood metrics server receives a blood
metrics measurement apparatus registration request from the user
device. The blood metrics measurement apparatus registration
request may include, for example, an apparatus identifier that
identifies the blood metrics measurement apparatus, and a user
account identifier associated the user account and/or user device.
The apparatus identifier may be obtained by the user device by a
variety of methods such as scanning a physical feature (e.g., a
tag, code, or the like) of the blood metrics measurement apparatus,
entering the identifier manually via the user device, and/or the
like. In some embodiments the communication module receives the
blood metrics measurement apparatus registration request.
[0386] In step 2010, the blood metrics server updates the
registration database with the blood metrics measurement apparatus
registration request by either creating a new record (e.g., a
record 1716) or, if the apparatus identifier is already stored in
one of the records (e.g., records 1716), updating that particular
record. In some embodiments, the management module updates the
registration database.
[0387] In step 2012, the blood metrics server provides a
registration success message to the user device when the
registration database has been updated. In some embodiments the
communication module provides the message.
[0388] FIG. 21 depicts an example noisy PPG signal 2100 and an
example filtered PPG signal 2150 according to some embodiments.
[0389] FIG. 22 depicts an example set of waves 2200 of a PPG signal
and an example high quality wave 2202 selected from the set of
waves 2200 according to some embodiments. FIG. 22 further depicts
an example bi-Gaussian fitted signal 2204 according to some
embodiments.
[0390] FIG. 23 depicts example feature points 2302a-u of a wave
2300 according to some embodiments. For example, the feature points
2302a-u may comprise location points and/or amplitudes. In some
embodiments, some or all of the features points 2302a-u may
correspond to measurement values which may be used by one or more
metric functions.
[0391] FIGS. 25A-C show an example selected high quality wave 2500,
the first derivative 2502 of the selected high quality wave 2500,
and the second derivative 2504 of the selected high quality wave
2500 according to some embodiments.
[0392] FIG. 26 depicts example tree structures 2602 and 2604 of an
example empirical blood pressure calculation model 2600 according
to some embodiments.
[0393] It will be appreciated that a "user device," "apparatus,"
"server," "module," "system," and/or "database" may comprise
software, hardware, firmware, and/or circuitry. In one example, one
or more software programs comprising instructions capable of being
executable by a processor may perform one or more of the functions
of the user devices, servers, modules, systems, and/or databases
described herein. In another example, circuitry may perform the
same or similar functions. Alternative embodiments may comprise
more, less, or functionally equivalent user devices, apparatus,
servers, modules, systems, and/or databases, and still be within
the scope of present embodiments. For example, the blood metrics
measurement apparatus 902 may include some or all of the
functionality of the user device 904 (e.g., user interface module
1202, blood pressure calculation system 1206, etc.), the blood
metrics server 906 may include some or all of the functionality of
the blood pressure calculation system 1206, and so forth.
[0394] Various embodiments described herein eliminate or reduce
sensitivity by using a combined array of photodiodes (energy
receivers) and LEDs (energy transmitters). An array of photodiodes
and LEDs may be utilized to choose an optimal or preferred path.
Using this method, a light path area may be chosen to yield the
preferred signal in a wider area.
[0395] FIG. 29A depicts a schematic diagram of an example sensor
system 2900 according to some embodiments. The sensor system 2900
may be disposed in the blood metrics measurement apparatus 200 (see
FIG. 2) or the blood metrics measurement apparatus 902 (see FIG.
10). For example, the sensor system 2900 may comprise the energy
transmitter 204 and the energy receiver 206 shown in FIG. 2. In
another example, the sensor system 2900 comprises the energy
transmitter 1004 and the energy receiver 1006.
[0396] The sensor system 2900 may include an optical sensor array
2902 including one or more energy transmitters 2904 (e.g., LEDs or
other lights sources) and one or more corresponding energy
receivers 2906 (e.g., photodiodes or other photosensors). It will
be appreciated that each pair of corresponding energy transmitters
and energy receivers (e.g., energy transmitter 2904 and energy
receiver 2906) may be referred to as an LED-PD system. The sensor
system 2900 may include any number of such LED-PD systems.
[0397] The energy transmitters 2904 may each comprise any number of
LEDs (e.g., between 2-6 LEDs) configured to transmit a variety of
wavelengths into tissue of a user. The energy transmitters 2904 may
be or include the energy transmitter 204 (see FIG. 2) and/or the
energy transmitters 1004 (see FIG. 10A). The corresponding energy
receivers 2906 may each comprise one or more photodiodes which
receive returning light from the corresponding energy transmitters
2904 after passage through the user's tissue. The energy receivers
2906 may be or include the energy receiver 206 (see FIG. 2) and/or
the energy receiver 1006 (see FIG. 10A).
[0398] In some embodiments, the energy transmitter 2904 and the
energy receiver 2906 may be spaced from each other at a
predetermined distance (e.g., 2 mm), and/or adjacent pairs of the
energy transmitters 2904 and the energy receivers 2906 may be
spaced at a predetermined distance (e.g., 15 mm). In various
embodiments, by measuring a pulse transit time (PTT.sub.m) based on
two signal patterns obtained from at least two pairs of the energy
transmitters and energy receivers, it is possible to obtain a pulse
wave velocity (PWV) by dividing a distance between the two
measuring areas (e.g., measuring points). Measuring areas may
correspond to mid-points between any energy transmitter and any
energy receiver (e.g., between any energy transmitter and any
energy receiver of the two pairs). The pulse wave velocity (PWV)
may be used to obtain blood metrics including, but not limited to,
blood pressure or other blood metric.
[0399] In various embodiments, the sensor system 2900 may be
mounted on a user's wrist along the radial artery 2908, and/or
another area of the user's body along arterial pathways (e.g., the
ulnar artery). The sensor system 2900 may include an accelerometer
and gyroscope (e.g., motion sensor 1012 of FIG. 10) for detecting
position and orientation information. The sensor system 2900 may
include a pressure sensor (e.g., pressure sensor 1008 of FIG.
10).
[0400] In some embodiments, at least one LED within each LED-PD
system may be configured to sample data from tissue at a very high
frequency (upwards of 1 KHz), and other LEDs may sample at a lower
rate (around 50-100 Hz). The channels sampling data at high
frequency may be referred to as the fast LED channels, and the
channels sampling data at lower frequency may be referred to as
slow LED channels.
[0401] It will be appreciated that the velocity of blood moving
through a user's arteries may vary within a range of 5 m/s to 15
m/s. This velocity may be correlated with various factors such as,
for example, age, arterial stiffness, and/or blood pressure (some
of which may be interrelated). Different sensor locations in a PPG
measurement device may cause a significant difference in signal
characteristics. Some locations of sensors may prevent PPG sensors
from receiving a high-quality signal that enables high resolution
analysis in time and frequency domains. The signal shape of
capillary tissue, venous tissue and of arterial tissue may be
different. In some embodiments, a 5 mm translation may be the
difference between an arterial signal and a signal that is
practically unusable, and even simple measurements (e.g., heart
rate measurement) may become difficult.
[0402] In order to obtain an arterial signal with a higher degree
of flexibility in device placement, the blood metrics measurement
apparatus 2902 may utilize an array of LED/photodiode pairs (or,
"channels") that facilitate measurement of PPG signals at any
number of locations (e.g., 12 different locations). FIG. 29A shows
an example configuration of an LED-PD sensor array used in the
blood metrics apparatus 2902. FIG. 29A shows a sideways profile of
the blood metrics apparatus 2902 of a user. FIG. 29B shows an
example configuration of sensors 2904 and 2906 mounted on the left
wrist (facing the palm) mounted along the direction of the radial
artery 2910 of a user.
[0403] In the example of FIGS. 29A and 29B, the LED-PD pairs 2904
and 2906 are separated by a known fixed distance (e.g., 15 mm),
which allows a pulse wave velocity (or "PWV") to be derived from a
modified pulse transit time. It will be appreciated that the LED-PD
pairs may be separated by any number of fixed distances, including,
for example, 8 mm-20 mm.
[0404] The pairs of LED-PD systems or channels may operate at a
high sampling rate (e.g. 1.4 KHz) where the LEDs are activated at
this sampling rate, and the photodiode measures light returned from
tissue. In one example. twelve PPG time series are generated in
this manner, one from each LED-photodiode pair. In one example, in
order to be able to resolve a 3 mms time shift, a signal of at
least about 700 Hz may be utilized. To avoid introducing error when
sampling that signal, the sampling rate may be set at the nyquist
frequency for that signal, which may correspond to 1.4 KHz.
[0405] FIG. 29C depict a reflection-based photoplethysmogram sensor
layout in some embodiments. Energy transmitter 2912 (e.g., energy
transmitter 2904) may emit light into tissue of a user. The light
is reflected, gathered, and sensed by energy receiver 2916 (e.g.,
energy receiver 2906). The cross 2914 denotes the center point
(e.g., measuring area) between these elements where the light is
reflected. Blood vessels in the close vicinity of the cross 2914
(center point) may determine the majority of the signal's
characteristics. The penetration depth of light may depend on the
energy transmitter's directionality and direction, as well as its
wavelength. Having a single point (or a single area) of measurement
can be highly sensitive to positioning and motion.
[0406] FIG. 30A depicts an example arrangement of energy
transmitters and energy receivers in a sensor system 370 according
to some embodiments. As discussed regarding FIG. 29A, the sensor
system 370 may be disposed in the blood metrics measurement
apparatus 200. For example, the sensor system 370 may comprise the
energy transmitters 204 and the energy receivers 206 shown in FIG.
2. The sensor system 370 may include an optical sensor array
including a plurality of energy transmitters 3002a-3002h
(collectively referred to as 3002) (e.g., LEDs or other lights
sources) and a plurality of energy receivers 3004a-3004b
(collectively referred to as 3004) (e.g., photodiodes or other
photosensors). In this example, the number of the energy
transmitters 3002 is different from the number of the energy
receivers 3004. The horizontal direction 3007 of FIG. 30A
corresponds to a circumferential direction of a wrist of a subject,
when the sensor system 370 is attached on a surface of the
wrist.
[0407] The layout of FIG. 30A may yield high sensitivity across a
wide area. The area 3008 is an area in which a maximum or increased
signal quality point can be identified and sensed.
[0408] In one example, since the energy emitted by one of the
energy transmitters 3002 is reflected by tissue of a user and the
reflected energy is incident on one of the two energy receivers
3004, the energy incident on the energy receiver 3004 can be used
to measure a state approximately at a midpoint (e.g., a measuring
area) 3006 between the energy transmitters 3002 and the energy
receiver 3004. The following are examples from FIG. 30A:
(1) Energy transmitter 3002a may emit energy that is generally
reflected by measuring area 3006a and subsequently received by
energy receiver 3004a. (2) Energy transmitter 3002b may emit energy
that is generally reflected by measuring area 3006b and
subsequently received by energy receiver 3004b. (3) Energy
transmitter 3002c may emit energy that is generally reflected by
measuring area 3006c and subsequently received by energy receiver
3004a. (4) Energy transmitter 3002d may emit energy that is
generally reflected by measuring area 3006d and subsequently
received by energy receiver 3004b. (5) Energy transmitter 3002e may
emit energy that is generally reflected by measuring area 3006e and
subsequently received by energy receiver 3004a. (6) Energy
transmitter 3002f may emit energy that is generally reflected by
measuring area 3006f and subsequently received by energy receiver
3004b. (7) Energy transmitter 3002g may emit energy that is
generally reflected by measuring area 3006g and subsequently
received by energy receiver 3004a. (8) Energy transmitter 3002h may
emit energy that is generally reflected by measuring area 3006h and
subsequently received by energy receiver 3004b.
[0409] The measuring areas 3006a-h (collectively referred to as
3006) may each correspond to a midpoint between one of the energy
transmitters 3002a-h and one of the energy transmitters 3004a-b,
respectively. In some embodiments, energy is generated from one
energy transmitter (e.g., 3002a) at a different time than when
energy is generated by another energy transmitter (e.g., 3002e). An
energy receiver (e.g., 3004a) operates to receive energy (e.g.,
from one or more of the energy transmitters) in order to measure a
state at one or more (e.g., two) measuring areas. Since a state at
an arbitrary point within an area defined by the measuring areas
3006a-h can be obtained from a linear combination of states at two
or more measuring areas 3006a-h, the sensor system 3000 may be
capable of measure a state at points within a measuring area 3008
defined by the measuring areas 3006a-h.
[0410] When using a sensor system for measurement of a state (e.g.,
through arterial tissue) of a user, it may be important to measure
the state of tissue at a specific measuring area, because signal
shape received through a specific tissue (e.g., arterial tissue) is
largely different from signal shape received through tissue of
other types (e.g., capillary tissue and venous tissue). It will be
appreciated that measuring an inappropriate tissue may cause
inaccurate measurement. Advantageously, by arranging a plurality of
energy transmitters 3002 and a plurality of energy receiver 3004 in
multi-dimensional directions (e.g., in a circumferential direction
of a wrist and an extending direction of an arm), it is possible to
obtain a multi-dimensional measuring area 3008, which allows for
multi-dimensional displacement of the sensor system 370 on a
measuring surface of the user. Furthermore, since all or some of
the multi-dimensional measuring area 3008 may be obtained, it is
possible to obtain measurement signals at two or more different
measuring points along a single blood vessel (e.g., a radial
artery) even if the blood vessel does not extend along a
predetermined arrangement direction of the measuring points. As a
result, in order to obtain some blood metrics (e.g., blood
pressure), the system is enabled to utilize signals at two or more
different measuring areas.
[0411] In some embodiments, signals are obtained at any number of
measuring areas 3006a-h. A quality of signal obtained at each of
the measuring area 3006a-h may be quantitatively assessed according
to a predetermined parameter (e.g., SNR). Signals of which quality
that are above a certain threshold may be selected. Thereafter,
among the selected signals, two signals of which measuring areas
3006 are closest to each other may be selected. Subsequently, the
two signals obtained from the closest measuring areas 3006 are
averaged, and the averaged signal may be identified as the best
quality signal reflecting a state in interest.
[0412] In various embodiments, to choose one or more signal points
(e.g., signals generated by energy reflected by a measurement
area), signal quality may be assessed. Signals may be associated
with a measurement area (e.g., one of measurement areas 3006a-h)
and only those signals are selected that are above a specific
threshold. Of those signals, two that are reflected by measurement
areas that are close together may be selected. The signals (or
properties thereof) may be averaged and the point of measurement
may be at the measurement area.
[0413] Although the measurement area is depicted as crosses in FIG.
30A, it will be appreciated that energy reflected at the
measurement area may be somewhat dispersed and energy may be
received in an area larger than a specific point.
[0414] FIG. 30B depicts an example arrangement of energy
transmitters and energy receivers in a sensor system 3010 according
to some embodiments. The sensor system 3010 may be disposed in the
blood metrics measurement apparatus 200. For example, the sensor
system 3010 may comprise the energy transmitters 204 and the energy
receivers 206 shown in FIG. 2. The sensor system 3010 may include a
top optical sensor array 3012a (e.g., including energy transmitters
3014a-h and energy receivers 3016a and b) and a bottom optical
sensor array 3012b (e.g., including energy transmitters 3014a-d and
i-l and energy receivers 3016c and d). It will be appreciated that
energy transmitters 3014a-d may be utilized in both sensor arrays
3012a-b thereby allowing fewer energy transmitters when compared to
two separate arrays that do not share energy transmitters.
[0415] In this example, the top optical sensor array 3012a may
include a plurality of energy transmitters (e.g., LEDs or other
lights sources) and a plurality of energy receives (e.g.,
photodiodes or other photosensors). The bottom optical sensor array
3012b may include a plurality of energy transmitters (e.g., LEDs or
other lights sources) and a plurality of energy (e.g., photodiodes
or other photosensors), in a symmetrical manner. The sensor system
3010 includes energy transmitters 3014a-d (collectively referred to
as 3014), and the energy transmitters 3014a-d are shared by the top
optical sensor array 3012a and the bottom sensor array 3012b.
[0416] Advantageously, since the energy transmitters 3014a-d are
shared by the top optical sensor array 382a and the bottom sensor
array 382b, the number of the energy transmitters 384a-d can be
reduced but obtain measurement at two separate measuring areas 3018
and 3020, which leads to energy saving for transmission of energy
from energy transmitters 3014a-d. Also, since the number of the
energy transmitters can be reduced, measurement at two separate
measuring areas can be achieved with a smaller device coverage on
the user.
[0417] Furthermore, since all or some of the multi-dimensional
measuring areas 3018 and 3020 may be obtained, it is possible to
obtain measurement signals at two or more different measuring
points along a single blood vessel (e.g., a radial artery) even if
the blood vessel does not extend along a predetermined arrangement
direction of the measuring points.
[0418] FIG. 30C depicts an example arrangement of energy
transmitters and energy receivers in a sensor system 3022 disposed
on a circuit board of an apparatus according to some embodiments.
For example, the sensor system 3022 may comprise the circuit board,
and the energy transmitters 3024a-h and energy receivers a-h. In
this example, the top array comprises an energy transmitter 3024a
positioned next to energy receivers 3026a-b followed by two more
energy transmitters 3024b-c. The energy transmitters 3024b-c are
followed by energy receivers 3026c-d which is followed in turn by
energy transmitter 3024d. The bottom array comprises an energy
transmitter 3024e positioned next to energy receivers 3026e-f
followed by two more energy transmitters 3024f-g. The energy
transmitters 3024f-g are followed by energy receivers 3026g-h which
is followed in turn by energy transmitter 3024h. These arrays
enable measuring areas for receiving signals. The measuring areas
allow for arteries (e.g., radial artery) or veins to be traverse
the arrays in many directions (e.g., not just vertically).
[0419] FIG. 30D depicts an example of a single array in some
embodiments. The array includes LED 1 (e.g., energy transmitter)
followed by PD A-B (e.g., photodiodes), followed by LEDs 2-3,
Photodiodes C-D, and LED 4. Measuring areas are represented by
circles. At the first measuring area (starting on the left of FIG.
30D), energy transmitted by LED 1 (at any number of wavelengths) is
reflected by tissue of a user to photodiode B. At the second
measuring area, energy transmitted by LED 2 (at any number of
wavelengths) is reflected by tissue of a user to photodiode A.
Similarly, at the third measuring area, energy transmitted by LED 3
is reflected by the tissue of the user and received by PD B. At the
fourth measuring area, energy transmitted by LED 2 is reflected by
the tissue of the user and received by PD C. At the fifth measuring
area, energy transmitted by LED 3 is reflected by the tissue of the
user and received by PD D and at the sixth measuring area, energy
transmitted by LED 4 is reflected by the tissue of the user and
received by PD C.
[0420] LEDs 1-4 may correspond to energy transmitters 3024a-d or
energy transmitters 3024e-h. PDs A-D may correspond to energy
receivers 3026a-d or energy receivers 3026e-h.
[0421] It will be appreciated that an energy receiver may provide
energy (e.g., at any number of wavelengths) into tissue of a user
in different directions and be received by different energy
receivers. For example, LED2 may project first energy in a first
direction that is reflected at the second measuring area (LED 2 PD
A) to be received by PD A. Similarly, LED2 may project energy at a
second direction that is reflected at the fourth measuring area
(LED 2 PD C) to be received by PD C. In another example, LED3 may
project first energy in a first direction that is reflected at the
third measuring area (LED 3 PD B) to be received by PD B.
Similarly, LED3 may project energy at a second direction that is
reflected at the fifth measuring area (LED 3 PD D) to be received
by PD D. This creases a larger measuring area to enable tracking of
any number of arteries and/or any number of veins even if the
arter(ies) and/or vein(s) do not necessarily travel laterally up
the user's wrist.
[0422] FIG. 30E is an enlarged view of the energy transmitters and
energy receivers in the sensor system 3028. The sensor system 3028
may include a sensor array including a plurality of energy
transmitters 3030a-3030h (e.g., LEDs or other lights sources) and a
plurality of energy receivers 3032a-3032h (e.g., photodiodes or
other photosensors) which means that the number of the energy
transmitters 3030 is the same as the number of the energy receivers
3032.
[0423] According to the arrangement of the energy transmitters
3030a-3030h (collectively referred to as 3030) and the energy
receivers 3032a-3032h (collectively referred to as 3032), twelve
measuring areas are obtained. Those twelve measuring areas include
six measuring points (denoted by crosses) on a top row
corresponding to: i) a pair of the energy transmitter 3030a and the
energy receiver 3032b; ii) a pair of the energy transmitter 3030b
and the energy receiver 3032a; iii) a pair of the energy
transmitter 3030c and the energy receiver 3032b; iv) a pair of the
energy transmitter 3030b and the energy receiver 3032c; v) a pair
of the energy transmitter 3030c and the energy receiver 3032d; and
vi) a pair of the energy transmitter 3030d and the energy receiver
3032c. Also, the twelve measuring points include six measuring
points (denoted by crosses) on a bottom row corresponding to: i) a
pair of the energy transmitter 3030e and the energy receiver 3032f;
ii) a pair of the energy transmitter 3030f and the energy receiver
3032e; iii) a pair of the energy transmitter 3030g and the energy
receiver 3032f; iv) a pair of the energy transmitter 3030f and the
energy receiver 3032g; v) a pair of the energy transmitter 3030g
and the energy receiver 3032h; and vi) a pair of the energy
transmitter 3030h and the energy receiver 3032g. As a result, a
measuring area 3040 defined by the twelve measuring points is
obtained.
[0424] Accordingly, similarly to the sensor system 3000 depicted in
FIG. 30A and the sensor system 3010 depicted in FIG. 30B, the
sensor systems (e.g., 3022 and 3028) depicted in FIGS. 30C-E can
obtain multi-dimensional measuring area by multi-dimensional
arrangement of the energy transmitters 3030 and the receivers 3032,
which allow for more flexible placement of the sensor system on a
surface of a user.
[0425] Also, similarly to the sensor system 3000 depicted in FIG.
30A and the sensor system 3010 depicted in FIG. 30B, timing to
generate energy from one energy transmitter (e.g., 3030a) may be
differentiated from timing to generate energy from another energy
transmitter (e.g., 3030c) when an energy receiver (e.g., 3032b)
operates to receive energy, in order to measure the state of tissue
at a single measuring point. In some embodiments, the energy
transmitters 3030 and the energy receivers 3032 are arranged such
that the measuring points are arranged at a constant distance
(e.g., 5 mm) in each row, and that a constant distance (e.g., 15
mm) is formed between the rows, so as to sufficiently cover a
region of tissue in interest (e.g., a radial artery).
[0426] In the embodiment shown in FIG. 30E, each of the top row and
the bottom row of the arrangement of the energy transmitters 3030
and the energy receivers 3032, the sensor system 3028 is configured
such that two energy receivers 3032 are consecutively arranged and
the two energy receives 3032 are disposed between two the energy
transmitters 3030. Such an arrangement of the energy transmitters
3030 and the energy receivers 3032 allows for more dense
arrangement thereof, in comparison to regular arrangement of pairs
of one energy transmitter 3030 and one energy receiver 3032 (i.e.,
the energy transmitters 3030 and the energy receivers 3032 are
alternately arranged in each of top and bottom rows). Further,
since two energy receivers 3032 are consecutively arranged and two
energy transmitters 3030 are consecutively arranged in the
configuration of the sensor system 3028, some circuit components
may be shared by the two consecutive energy receivers 3032, some
circuit components may be shared by the two consecutive energy
transmitters 3030.
[0427] FIG. 31A-B depict experimentations carried out to obtain an
optimum or preferred location of a sensor system (such as the
sensor systems 3000, 3010, 3022, and 3028 depicted in FIG. 30A-E),
when the sensor system is used for measuring states of a radial
artery on a wrist (e.g., left wrist) of a subject (e.g., a user).
Measurement of the state of the radial artery on a wrist is useful
for measuring metrics such as pulse rate, blood pressure, and
oxygen saturation, because the radial artery on a wrist is close to
a body surface of the subject and thus more accessible by the
sensor system employing optical signal transmission.
[0428] FIG. 31A depicts spacing of energy transmitters and energy
receivers in some embodiments. Spacing may be based on a
methodology of a test to obtain an optimum or preferred measuring
area to measure states of a radial artery on a left wrist of
subjects. The x-y coordinate shown in FIG. 31A corresponds to an
area on a surface of a left wrist when a left palm faces upward. In
this example, the x and y axes of FIG. 31A correspond to a
direction of a crease between the left wrist and left palm and a
direction in which a left arm extends, respectively. The origin of
the x-y coordinate in this example corresponds to a left edge of
the left wrist on the crease. Units in FIG. 31A are in millimeters
(mm). Two smaller squares correspond to locations of energy
transmitters (e.g., LEDs and/or other lights sources). Two larger
black squares correspond to location of energy receivers (e.g.,
photodiodes and/or other photosensors). The circle corresponds to
the center of the sensor system.
[0429] In the test, the width (W) of the left wrist was measured
for each subject, and then the center of the sensor system was
positioned at W/2 in +x axis, and at (.times.0+15) in -y axis
(where x.sub.0 is a distance of a measuring gauge from the crease
and initially 10 mm). Thereafter, while adjusting y.sub.0 (where
y.sub.0 is a distance of the center from the wrist median W/2) and
x.sub.0, three measuring points that obtained high quality signals
(as compared to other obtained signals from transmitters and
receivers in other positions) were identified. According to at
least one test, it was found that the three highest quality signals
were obtained in a range of 20-30 mm in the crease direction from
left edges of left wrists, and in a range of 15-40 mm in an
arm-extending direction from the creases between left wrists and
left palms.
[0430] FIG. 31B depicts optimum or preferred measuring areas to
measure states of a radial artery on a left wrist of subjects. In
the tests results shown in FIG. 31B, contours of left wrists of
subjects (about 30 subjects) were traced, and the traced contours
of left lists were delineated and reparameterized. Then, left edges
of the left wrists were aligned (e.g., rotated and/or translated to
minimize distance) to a common template. The horizontal axis of
FIG. 31B corresponds to a distance in a crease direction of left
wrists in millimeters (mm), and vertical axis of FIG. 31B
corresponds to a height of left wrists in millimeters (mm). The
circles in FIG. 31B shows most optimal or preferred measuring area
(where the most high-quality signal was obtained) of each subject.
According to the test, it was found that the most high-quality
signals were obtained in a range of 5-20 mm in the crease direction
from left edges of left wrists.
[0431] According to the tests depicted in FIG. 31A-B, in an
embodiment, a measuring area defined by measuring points of a
sensor system (e.g., the sensor systems 3000, 3010, 3022, and 3028
depicted in FIG. 30A-E) is configured to cover at least part of
5-30 mm in a crease direction from a left edge of left wrists of
subjects and at least part of 15-40 mm in an arm-extending
direction from the creases between left wrists and left palms of
subjects. In a more particular embodiment, a multi-dimensional
region defined by the plurality of energy transmitters (e.g., any
number of the energy transmitters 3030a-h in FIG. 30E) ranges at
least 20 mm in a direction of a wearable member (e.g., central unit
402 in FIG. 4) corresponding to a circumferential direction of the
wrist. In another more particular embodiment, a multi-dimensional
region defined by the energy transmitters (e.g., any number of the
energy transmitters 3030a-h in FIG. 30E) ranges at least 15 mm in a
direction of the wearable member (e.g., central unit 402 in FIG. 4)
corresponding to an extending direction of an arm of the user.
[0432] In another embodiment, the sensor array 404 of the apparatus
400 depicted in FIG. 4 is located to cover this optimum or
preferred range, for example, when apparatus 400 is attached to
left wrists of subjects such that the screen 410 faces along the
back of left hands. In a more specific implementation, a center the
sensor array 404 in a circumferential direction of wrist is
positioned at or adjustable to a position that is offset from
180.degree. opposite position of the center of the screen 410, when
the apparatus is wound around left wrists with fit such that the
apparatus 400 does not freely move around wrists.
[0433] FIG. 32 depicts a flowchart 3200 of an example method of
separating source signals from measured signals by a sensor system
(e.g., the sensor systems 3000, 3010, 3022, and 3028 depicted in
FIG. 30A-E) and identifying at least an arterial signal from the
separated source signals according to some embodiments. In one
example, the source separating method according to the flowchart
3200 depicted in FIG. 32 may be carried out during the blind source
separation performed in step 1542 of the flow chart 1530 depicted
in FIG. 15B.
[0434] In step 3202, a wearable blood metrics measurement apparatus
(e.g., blood metrics measurement apparatus 200 or sensor systems
3000, 3010, 3022, or 3028 depicted in FIG. 30A-E) measures channel
signals each of which corresponds to a state of tissue at a
measuring area (e.g., midpoint) of one of energy transmitters and
one of energy receivers of a sensor system of the apparatus for a
certain period of time (e.g., 10 sec). The wearable blood metrics
measurement apparatus measures the channel signals for each of
different wavelengths (e.g., infrared, red, green, and blue lights,
or the like) and the following steps 3204-3214 may be carried out
for each wavelength. For example, in the embodiment of the sensor
system shown in FIG. 31E, twelve channel signals corresponding to
twelve measuring points are obtained for each of different
wavelengths.
[0435] In step 3204, the wearable blood metrics measurement
apparatus selects two or more channel signals that have high
quality (e.g., high SNR or high signal-to-noise ratio) from the
measured channel signals. In some embodiments, a processor of or
associated with the wearable blood metrics measurement apparatus
selects channel signals that have an SNR higher than a certain
threshold from the measured channel signals. Some channel signals
may include relatively large noise because of poor positioning of
the sensor system, loose sensor placement, and/or interference by
foreign materials (such as dusts, sweats, scabs, and tattoos). By
choosing channel signals with better SNR, it is possible to obtain
more accurate source signals. In some other embodiments, the
wearable blood metrics measurement apparatus selects channel
signals from which the high quality waves are obtainable through
the method depicted in one of the flowcharts 1600, 1620, and 1640
depicted in FIG. 16A-C, respectively, as the high-quality channel
signals.
[0436] In step 3206, the wearable blood metrics measurement
apparatus phase-shifts the selected channel signals to co-phase the
selected channel signals. Depending on the measurement points, the
channel signals may be slightly offset in time due to pulse wave
velocity. By co-phasing the channel signals, the aspect of the
pulse wave velocity can be removed, and more accurate calculation
of source signals can be carried out.
[0437] In step 3208, the wearable blood metrics measurement
apparatus determines values of one or more parameters to calculate
source signals. The parameters to be used may include distance
between measuring points of channel signals, correlation of signal
wave form between multiple channel signals, O.sub.2 saturation
(SpO.sub.2) corresponding to a channel signal (if obtained), rise
time corresponding to each channel signal, and respiratory power
ratio of a low respiratory frequency range (e.g., 0.1-0.6 Hz) to
the entire respiratory frequency range (e.g., 0-2.5 Hz). For
example, a long distance between a plurality of measuring points
may reduce a parameter value for association of multiple channel
signals, because it is more likely that the channel signals come
from different blood vessels. In another example, high correlation
of wave forms among multiple channel signals may increase a
parameter value for association of multiple channel signals,
because it is more likely that the channel signals come from the
similar signal sources, such as arterial blood flow, a venous blood
flow, a capillary blood flow, or motion artifacts.
[0438] In step 3210, the wearable blood metrics measurement
apparatus determines values of a mixing matrix based on the
determined values of the one or more parameters. The values of the
mixing matrix are determined such that one or more source signals
include only signal components thereof and exclude signal
components of the other source signals and noises.
[0439] In step 3212, which is an optional step, the wearable blood
metrics measurement apparatus may update a previously-determined
values of a mixing matrix with the values of the mixing matrix
calculated in step 3210. Since it is possible to assume that a
subject's physiology does not change significantly in a short
period of time (e.g. few ten seconds), but may change in a longer
period of time (e.g., one hour), depending on the health condition
of the subject. It may be beneficial to use a previously-determined
values of a mixing matrix to save power to determine the values of
the mixing matrix. Thus, it may be preferable to carry out the
determination of the mixing matrix values for each certain period
that is sufficiently long (e.g., few minutes), especially when the
wearable blood metrics measurement apparatus uses a mobile battery.
However, calculating source signals based on a
previously-determined values of a mixing matrix that was obtained a
long time ago (e.g., one hour) may lead to inaccurate calculation
of source signals. By updating the mixing matrix, it is possible to
more accurately calculate the source signals.
[0440] In step 3214, the wearable blood metrics measurement
apparatus calculates each of one or more source signals based on
the selected channel signals and the determined values of the
mixing matrix. In one embodiment, the wearable blood metrics
measurement apparatus calculates each of one or more source signals
by multiplying the values of the selected channel signals with the
values of the mixing matrix.
[0441] In step 3216, the wearable blood metrics measurement
apparatus identifies one or more source signals corresponding to an
arterial signal, and optionally one or more source signals
corresponding to a venous signal, one or more source signals
corresponding to a capillary signal, and/or one or more source
signals corresponding to a respiratory signal.
[0442] Some methods may identify the arterial signal from the other
signals, in particular, the venous signal. In one example, a method
utilizing O.sub.2 saturation (SpO.sub.2) obtained from source
signals of different wavelengths (e.g., infrared and read lights)
is employed. According to the O.sub.2 method, source signals are
sorted based on the value of O.sub.2 saturation (SpO.sub.2), and
then source signals with the highest O.sub.2 saturation (or higher
than a certain threshold) are identified as an arterial signal and
source signals with the lowest O.sub.2 saturation (or lower than a
certain threshold) are identified as a venous signal.
[0443] In another example, a rise-time method utilizing a rise time
(or "crest" time), which is an average time period of valley to
peak in each waveform of the source signal, is employed. According
to the rise-time method, source signals are sorted based on the
value of the rise time, and then source signals with the shortest
rise time (or shorter than a certain threshold) are identified as
an arterial signal and source signals with the longest rise time
(or longer than a certain threshold) are identified as a venous
signal.
[0444] In another example, a respiratory rate method utilizing a
ratio of a low frequency component in a respiratory frequency range
(0-2.5 Hz) is employed. According to the respiratory rate method, a
power ratio of a low respiratory frequency component (0.1-0.6 Hz)
of each source signal with respect to the entire respiratory
frequency range (0-2.5 Hz) is calculated, and then source signals
with the highest power ratio (or higher than a certain threshold)
are identified as a venous signal, and source signals with the
lowest power ratio (or lower than a certain threshold) are
identified as an arterial signal.
[0445] In step 3218, the wearable blood metrics measurement
apparatus calculates one or more biological metrics such as blood
metrics (e.g., blood pressure, O.sub.2 saturation, arterial
stiffness, and so on) based on source signals that are identified
as an arterial signal. In addition the wearable blood metrics
measurement apparatus calculates one or more biological metrics
such as a respiratory rate based on one or more source signals that
are identified as a venous signal, an O2 consumption rate based on
a source signal that is identified as an arterial signal and a
source signal that is identified as a venous signal. Furthermore,
when the wearable blood metrics measurement apparatus includes a
motion sensor (e.g., an accelerometer, gyroscope, global
positioning system, or the like), the wearable blood metrics
measurement apparatus can more accurately calculates the
respiratory rate, because repetitive motion due to respiration can
be obtained from the motion sensor. Moreover when the wearable
blood metrics measurement apparatus includes the motion sensor, the
wearable blood metrics measurement apparatus includes the motion
sensor can calculate step counts based on input from the motion
sensor, and perform gain analysis.
[0446] FIG. 33 depicts a flowchart 3300 of an example method of
calculating blood pressure according to some embodiments.
[0447] In step 3302, a wearable blood metrics measurement apparatus
(e.g., blood metrics measurement apparatus 200) selects at least
two signals at different locations corresponding to an arterial
signal, which is obtainable through the source separation process
described above, the at least two signals associated with
corresponding optical sensors of the wearable blood metrics
measurement apparatus. In some embodiments, a blood pressure
processing module (e.g., blood pressure processing module 1424) of
a blood pressure calculation system (e.g., blood pressure
calculation system 1206) uses one or more signal selection rules to
perform the selection.
[0448] In step 3304, the wearable blood metrics measurement
apparatus obtains first signal data (e.g., PPG signal data) from a
first signal of the at least two signals (e.g., a pair of signals)
over a predetermined period of time. In some embodiments, the blood
pressure processing module of the blood pressure calculation
systems uses one or more modified pulse transit time rules to
obtain the first signal data.
[0449] In step 3306, the wearable blood metrics measurement
apparatus obtains second signal data from a second signal of the at
least two signals over the predetermined period of time. In some
embodiments, the blood pressure processing module of the blood
pressure calculation systems uses one or more modified pulse
transit time rules to obtain the first signal data to obtain the
second signal data.
[0450] In step 3308, the wearable blood metrics measurement
apparatus applies a frequency transform function (e.g., a Fourier
transform) to each of the first signal data and the second signal
data to a transform the first signal data and the second signal
data to a frequency domain. In some embodiments, the blood pressure
processing module of the blood pressure calculation systems uses
one or more modified pulse transit time rules to apply the
frequency transform function.
[0451] In step 3310, the wearable blood metrics measurement
apparatus determines a first phase value for a first frequency
component of the first signal data in the frequency domain. In some
embodiments, the blood pressure processing module of the blood
pressure calculation systems uses one or more modified pulse
transit time rules to perform the determination.
[0452] In step 3312, the wearable blood metrics measurement
apparatus determines a second phase value for a second frequency
component of the second signal data in the frequency domain
[0453] In step 3314, the wearable blood metrics measurement
apparatus determines a phase difference value between the first
phase value and the second phase value. In some embodiments, the
blood pressure processing module of the blood pressure calculation
systems uses one or more modified pulse transit time rules to
perform the determination.
[0454] In step 3316, the wearable blood metrics measurement
apparatus determines a time shift value between the first signal
data and the second signal data based on the phase difference
value. In some embodiments, the blood pressure processing module of
the blood pressure calculation systems uses one or more modified
pulse transit time rules to perform the determination.
[0455] In step 3318, the wearable blood metrics measurement
apparatus determines a modified pulse transmit time based on the
time shift value between the first signal data and the second
signal data, the modified pulse transit time representing a transit
time for a pressure wavefront to travel from a first optical sensor
of the corresponding optical sensors and a second optical sensor of
the corresponding optical sensors. In some embodiments, the blood
pressure processing module of the blood pressure calculation
systems uses one or more modified pulse transit time rules to
perform the determination.
[0456] In step 3320, the wearable blood metrics measurement
apparatus determines a pulse wave velocity based on the modified
pulse transit time and a distance between the at least two
locations. In some embodiments, the blood pressure processing
module of the blood pressure calculation systems uses one or more
modified pulse transit time rules to perform the determination.
[0457] In step 3322, the wearable blood metrics measurement
apparatus calculates an arterial blood pressure value based on the
pulse wave velocity. In some embodiments, the blood pressure
processing module of the blood pressure calculation systems uses
one or more modified pulse transit time rules and/or one or more
blood pressure processing rules (e.g., blood pressure processing
rules 1446) to perform the calculation.
[0458] In step 3324, the wearable blood metrics measurement
apparatus provides a message including or being based on the
arterial blood pressure value. In some embodiments, a user
interface module of the blood metrics measurement apparatus
provides the message using one or more message rules (e.g., message
rules 1448), for example, by displaying the message on a screen
(e.g., screen 410).
[0459] The present invention(s) are described above with reference
to example embodiments. It will be appreciated that various
modifications may be made and other embodiments may be used without
departing from the broader scope of the present invention(s).
Therefore, these and other variations upon the example embodiments
are intended to be covered by the present invention(s).
* * * * *