U.S. patent application number 15/704615 was filed with the patent office on 2018-01-04 for assessing cardiovascular function using an optical sensor.
This patent application is currently assigned to Google Inc.. The applicant listed for this patent is Google Inc.. Invention is credited to Brian Derek DeBusschere, Jeffrey L. Rogers.
Application Number | 20180000354 15/704615 |
Document ID | / |
Family ID | 57217849 |
Filed Date | 2018-01-04 |
United States Patent
Application |
20180000354 |
Kind Code |
A1 |
DeBusschere; Brian Derek ;
et al. |
January 4, 2018 |
Assessing Cardiovascular Function Using an Optical Sensor
Abstract
This document describes assessing cardiovascular function using
an optical sensor, such as through sensing relevant hemodynamics
understood by pulse transit times, blood pressures, pulse-wave
velocities, and, in more breadth, ballistocardiograms and
pressure-volume loops. The techniques disclosed in this document
use various optical sensors to sense hemodynamics, such as skin
color and skin and other organ displacement. These optical sensors
require little if any risk to the patient and are simple and easy
for the patient to use.
Inventors: |
DeBusschere; Brian Derek;
(Los Gatos, CA) ; Rogers; Jeffrey L.; (San Carlos,
CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Google Inc. |
Mountain View |
CA |
US |
|
|
Assignee: |
Google Inc.
Mountain View
CA
|
Family ID: |
57217849 |
Appl. No.: |
15/704615 |
Filed: |
September 14, 2017 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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14681625 |
Apr 8, 2015 |
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15704615 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
A61B 5/0261 20130101;
A61B 5/0205 20130101; A61B 5/02125 20130101; A61B 5/02427 20130101;
A61B 5/02433 20130101; A61B 5/0082 20130101; A61B 5/021 20130101;
A61B 5/0077 20130101; A61B 5/1032 20130101; A61B 5/1102
20130101 |
International
Class: |
A61B 5/0205 20060101
A61B005/0205; A61B 5/021 20060101 A61B005/021; A61B 5/00 20060101
A61B005/00; A61B 5/024 20060101 A61B005/024; A61B 5/11 20060101
A61B005/11; A61B 5/103 20060101 A61B005/103 |
Claims
1. A computer-implemented method comprising: receiving, from one or
more optical sensors, a first skin color or a first skin
displacement captured at a first region of a patient and a second
skin color or a second skin displacement captured at a second
region of the patient; determining a circulatory distance between
the first and second regions of the patient; determining a time
correlation between capture of the first color or the first skin
displacement and capture of the second color or the second skin
displacement; and determining, based on the circulatory distance
and the time correlation, a pulse-wave velocity for blood
circulation through the patient.
2. The method of claim 1, wherein determining the circulatory
distance is based on: a linear distance between the first and
second regions; an axial distance between the first and second
regions, the axial distance oriented relative to an axis of the
patient's spine; or an arterial-path distance, the arterial-path
distance based on an arterial structure of the patient between the
first and second region.
3. The method of claim 2, wherein determining the circulatory
distance based on the linear distance, the axial distance, or the
arterial-path distance, comprises determining the linear distance,
the axial distance, or the arterial-path distance, respectively,
and is further based on surface information captured by the one or
more optical sensors.
4. The method of claim 1, wherein determining the time correlation
is based on a time at which a maximum or minimum blood volume or
rate of change in blood volume is determined for each of the first
and second regions.
5. The method of claim 1, wherein determining the pulse-wave
velocity divides the circulatory distance by the time correlation
for a same heartbeat of the patient.
6. The method of claim 1, further comprising determining, based on
the pulse-wave velocity, a blood pressure for the patient or a
long-term trend in arterial stiffness.
7. The method of claim 1, further comprising calibrating the one or
more optical sensors with one or more external sensors effective to
calibrate the skin colors or displacements to a calibration
pulse-wave velocity, the calibrating prior to determining the
pulse-wave velocity, and wherein determining the pulse-wave
velocity is based at least in part on the calibration.
8. The method of claim 1, further comprising: receiving, from one
or more other optical sensors, a third skin color or a third skin
displacement captured at a third region of the patient and a fourth
skin color or a fourth skin displacement captured at a fourth
region of the patient; determining a second circulatory distance
between the third and fourth regions of the patient; determining a
second time correlation between the detection of the third color or
the third skin displacement and the detection of the fourth color
or the fourth skin displacement; determining, based on the second
circulatory distance and the second time correlation, a second
pulse-wave velocity for blood circulation through the patient; and
altering the first pulse-wave velocity based on the second
pulse-wave velocity to improve an accuracy or robustness of the
first pulse-wave velocity.
9. The method of claim 1, further comprising: performing the
operations again at a later time to determine a later-time
pulse-wave velocity; and determining a cardiac trend for the
patient based on the later-time pulse-wave velocity and the
pulse-wave velocity.
10. The method of claim 1, wherein: the first skin color or the
first skin displacement is a first skin color, the first skin color
usable to determine blood in skin of the first region of the
patient; the second skin color or the second skin displacement is a
second skin color, the second skin color usable to determine blood
in skin of the second region of the patient; and the blood
determined in the skin of the first and second regions of the
patient indicates, for multiple images captured at each of the
first and second regions, first and second blood waveforms for the
first and second regions, respectively.
11. The method of claim 1, wherein: the first skin color or the
first skin displacement is a first skin displacement, the first
skin displacement usable to determine blood flow of the first
region of the patient; the second skin color or the second skin
displacement is a second skin displacement, the second skin
displacement usable to determine blood flow of the second region of
the patient; and the blood determined in the skin of the first and
second regions of the patient indicates, for multiple images
captured at each of the first and second regions, first and second
blood waveforms for the first and second regions, respectively.
12. A system comprising: one or more optical sensors capable of
detecting skin color or skin displacement at two or more regions of
a patient; a computer processor; and one or more computer-readable
storage media having instructions stored thereon that, responsive
to execution by the computer processor, performs operations
comprising: receiving, from the one or more optical sensors, a
first skin color or a first skin displacement captured at a first
region of a patient and a second skin color or a second skin
displacement captured at a second region of the patient;
determining a circulatory distance between the first and second
regions of the patient; determining a time correlation between
capture of the first color or the first skin displacement and
capture of the second color or the second skin displacement; and
determining, based on the circulatory distance and the time
correlation, a pulse-wave velocity for blood circulation through
the patient.
13. The system of claim 12, wherein determining the circulatory
distance is based on: a linear distance between the first and
second regions; an axial distance between the first and second
regions, the axial distance oriented relative to an axis of the
patient's spine; or an arterial-path distance, the arterial-path
distance based on an arterial structure of the patient between the
first and second region.
14. The system of claim 13, wherein determining the circulatory
distance based on the linear distance, the axial distance, or the
arterial-path distance, comprises determining the linear distance,
the axial distance, or the arterial-path distance, respectively,
and is further based on surface information captured by the one or
more optical sensors.
15. The system of claim 12, the operations further comprising:
receiving, from one or more other optical sensors, a third skin
color or a third skin displacement captured at a third region of
the patient and a fourth skin color or a fourth skin displacement
captured at a fourth region of the patient; determining a second
circulatory distance between the third and fourth regions of the
patient; determining a second time correlation between the
detection of the third color or the third skin displacement and the
detection of the fourth color or the fourth skin displacement;
determining, based on the second circulatory distance and the
second time correlation, a second pulse-wave velocity for blood
circulation through the patient; and altering the first pulse-wave
velocity based on the second pulse-wave velocity to improve an
accuracy or robustness of the first pulse-wave velocity.
16. The system of claim 12, the operations further comprising:
performing the operations again at a later time to determine a
later-time pulse-wave velocity; and determining a cardiac trend for
the patient based on the later-time pulse-wave velocity and the
pulse-wave velocity.
17. The system of claim 12, wherein: the first skin color or the
first skin displacement is a first skin color, the first skin color
usable to determine blood in skin of the first region of the
patient; the second skin color or the second skin displacement is a
second skin color, the second skin color usable to determine blood
in skin of the second region of the patient; and the blood
determined in the skin of the first and second regions of the
patient indicates, for multiple images captured at each of the
first and second regions, first and second blood waveforms for the
first and second regions, respectively.
18. The system of claim 12, wherein: the first skin color or the
first skin displacement is a first skin displacement, the first
skin displacement usable to determine blood flow of the first
region of the patient; the second skin color or the second skin
displacement is a second skin displacement, the second skin
displacement usable to determine blood flow of the second region of
the patient; and the blood determined in the skin of the first and
second regions of the patient indicates, for multiple images
captured at each of the first and second regions, first and second
blood waveforms for the first and second regions, respectively.
19. One or more computer-readable storage media having instructions
stored thereon that, responsive to execution by a computer
processor, performs operations comprising: receiving, from one or
more optical sensors, a first skin color or a first skin
displacement captured at a first region of a patient and a second
skin color or a second skin displacement captured at a second
region of the patient; determining a circulatory distance between
the first and second regions of the patient; determining a time
correlation between capture of the first color or the first skin
displacement and capture of the second color or the second skin
displacement; and determining, based on the circulatory distance
and the time correlation, a pulse-wave velocity for blood
circulation through the patient.
20. The computer-readable media of claim 19, wherein the
instructions further perform operations comprising: performing the
operations again at a later time to determine a later-time
pulse-wave velocity; and determining a cardiac trend for the
patient based on the later-time pulse-wave velocity and the
pulse-wave velocity.
Description
RELATED APPLICATIONS
[0001] This Application is a divisional of U.S. patent application
Ser. No. 14/681,625, filed Apr. 8, 2015, entitled "Assessing
Cardiovascular Function Using an Optical Sensor," the entire
disclosure of which is hereby incorporated by reference.
BACKGROUND
[0002] Cardiovascular disease is the leading cause of morbidity and
mortality worldwide. At the same time this chronic disease is
largely preventable. Medical science knows how to save most of
these lives by removing the major risk factors of smoking,
diabetes, and hypertension. And many people are told just what they
need to do to reduce these risk factors--stop smoking, reduce sugar
intake, eat healthier, reduce alcohol intake, increase
cardiovascular exercise, lose weight, and, if needed, take
blood-pressure medication. But many people do not follow this good
advice. Because of this, millions of people needlessly die from
cardiovascular disease.
[0003] People don't follow this good medical advice because they
think they are different, they do not want to change their
behaviors that are causing the disease, or they do not know what to
change in their particular case. When a physician tells them that
they are at risk from heart disease because they are overweight,
for example, many people know that this judgment is not necessarily
specific to them--it is based on averages and demographics. So
being a particular weight may not negatively affect a particular
patient's heart. Further, a lack of feedback that their behavior is
harming their heart results in a lack of incentive for them to
change their behavior.
[0004] This lack of incentive to follow good advice can be
addressed by monitoring the state of the patient's cardiovascular
system over time to show trends in heart health. Hard data often
motivates patients to modify their behavior, such as data
indicating that their heart shows measurable signs of heart
disease. Unfortunately, current methods for measuring heart health
can be inconvenient, stressful, and expensive. Simple home monitor
products exist for measuring heart rate and blood pressure, but
long-term user compliance is a problem due to inconvenience. More
advanced cardiovascular monitoring, such as heart rate variability,
arterial stiffness, cardiac output, and atrial fibrillation,
involve expensive and time-consuming trips to a medical facility
for a skilled assessment. Because of this, only patients that
demonstrate late stage symptoms of heart disease are likely to
receive these tests, which is generally too late to make simple
lifestyle changes that would avoid a chronic disease.
[0005] Another reason that people don't follow this good advice, or
don't follow it for long enough to prevent heart disease, is
because they do not see the benefit. When people take the advice of
changing their diet and habits--which most people do not want to
do--they often don't see the improvement before they lose the
motivation to continue monitoring their cardiovascular status.
Because of this, many people go back to their old habits only to
later die of heart disease.
SUMMARY
[0006] This document describes assessing cardiovascular function
using an optical sensor, such as through sensing relevant
hemodynamics understood by heart and respiration rates, heart rate
variability, blood pressures, pulse-wave velocities, arterial
stiffness, cardiac valve timing, ballistocardiogram force,
photo-plethysmograms, blood oxygenation, and pressure-volume loops.
The techniques disclosed in this document use various optical
sensors to sense the effects of cardiovascular hemodynamics, such
as skin color or displacement at multiple spatial locations on the
body. These optical sensors require little if any risk to the
patient and are simple and easy for the patient to use.
[0007] Further, the techniques described herein can determine blood
flow asymmetries, which may indicate a stroke or other
cardiovascular disease or pressure waveforms, which may indicate
cardiac abnormalities, such as atrial fibrillation. These
techniques may also determine trends in a patient's cardiovascular
health. These trends can aid a patient by helping them know if the
effort they are expending to improve their heart health is actually
making a difference. Further, negative trends or conditions, such
as cardiac irregularities or some asymmetries can be found that can
spur people to improve their health or to get medical attention. By
so doing, these techniques may save many people from dying of heart
disease.
[0008] This summary is provided to introduce simplified concepts
concerning the techniques, which are further described below in the
Detailed Description. This summary is not intended to identify
essential features of the claimed subject matter, nor is it
intended for use in determining the scope of the claimed subject
matter.
BRIEF DESCRIPTION OF THE DRAWINGS
[0009] Embodiments of techniques and devices for assessing
cardiovascular function using an optical sensor are described with
reference to the following drawings. The same numbers are used
throughout the drawings to reference like features and
components:
[0010] FIG. 1 illustrates an example environment in which the
techniques can be implemented.
[0011] FIG. 2 illustrates an example computing device of FIG.
1.
[0012] FIG. 3 illustrates an example optical sensor of FIG. 1.
[0013] FIG. 4 illustrates a method for assessing cardiovascular
function using an optical sensor, including determination of a
pulse-wave velocity for a patient.
[0014] FIG. 5 illustrates a male patient having various regions of
which images are captured by optical sensors.
[0015] FIG. 6 illustrates various circulatory distances that can be
used, along with time correlations, to determine a pulse-wave
velocity.
[0016] FIG. 7 illustrates a method for determining circulatory
distances, such as those described in FIG. 6.
[0017] FIG. 8 illustrates a method for assessing cardiovascular
function using an optical sensor based on size, volume, or location
of an organ or structure of a patient.
[0018] FIG. 9 illustrates an example device embodying, or in which
techniques may be implemented that assess cardiovascular function
using an optical sensor.
DETAILED DESCRIPTION
Overview
[0019] This document describes techniques using, and devices
enabling, assessment of cardiovascular function using an optical
sensor. Through use of optical sensors a patient's skin color and
displacement over time can be accurately measured, including by
comparing colors and displacements at different regions of the
patient. For example, an optical sensor can measure a color change
at a patient's cheek and, based on that color change, the
techniques can determine that the patient's heart beat has produced
a peak blood-pressure flow at some particular instant at the cheek.
Another optical sensor can measure a color change or displacement
at the patient's wrist for the same heartbeat, which the techniques
can determine indicates a peak blood-pressure flow at the wrist at
some other instant. By comparing the times and distance between
these regions, a pulse-wave velocity can be assessed. This
pulse-wave velocity can then be used to determine arterial
stiffness, blood pressure, and other measurements of cardiovascular
function. Simultaneously, those two measurement points can be used
to independently measure other vitals like heart rate and
respiration rate, with the combination of the two used to improve
the measurement by compensating for movements, illumination
changes, or occlusions.
[0020] In addition to assessing cardiovascular heath at some
snapshot in time, the techniques may also measure trends in
cardiovascular function. By way of one example, assume that a
patient has an optical sensor in her bathroom that is capable of
measuring color and displacement at multiple regions, such as her
neck, palm, and forehead. This optical sensor measures skin color
variations between or within a region, which can indicate
differential blood volume to provide a photo-plethysmogram (PPG).
If the patient has other optical sensors, such as one in her
computing spectacles and another in her smartphone, these can
further aid the accuracy and robustness of the measurements. Using
these sensors, assume that over the course of a new diet and
exercise routine that the techniques, using data from the optical
sensors, determine that her heart's stroke volume (an important
measure of heart health) has improved 6% in four weeks. With this
positive feedback, this patient may continue her diet and exercise
routine, thereby likely reducing the chances that she will die of
heart disease.
[0021] For another case, assume that the techniques determine that
there is an asymmetry in blood flow within a patient's face. This
asymmetry can be indicated to the patient or a medical professional
sufficient to perform further testing, as asymmetry can indicate a
stroke (a deadly disease that, with a fast diagnosis and treatment
can save the patient's life or quality of life) or other vascular
disease.
[0022] These are but a few examples in which assessing
cardiovascular function using an optical sensor can be performed,
other examples and details are provided below. This document now
turns to an example environment, after which example optical
sensors and methods, cardiovascular functions and trends, and an
example computing system are described.
EXAMPLE ENVIRONMENT
[0023] FIG. 1 is an illustration of an example environment 100 in
which assessing cardiovascular function using an optical sensor can
be employed. Environment 100 illustrates a patient 102 that is a
subject of the health monitoring, as well as a medical professional
104, family member, or other caretaker that, in some cases, will
receive results of the health monitoring. This example employs
optical sensors 106, a color and displacement optical sensor 106-1
(sensor 106-1), which is part of computing device 108, and a
hyperspectral sensor 106-2 (sensor 106-2), which is located within
mirror 110.
[0024] Sensor data 112 is provided by each of optical sensors 106
to some computing device. As shown, sensor data 112 is passed from
sensor 106-2 to computing device 108 while sensor 106-1 is integral
with computing device 108 and need not be passed if the techniques
are performed at that device. Computing device 108 then performs
some or all of the techniques, or passes that sensor data to some
other computing device, such as a remote server through a
communication network (not shown).
[0025] As shown with this example environment 100, a sensing milieu
(e.g., optical sensors 106 in patient 102's bathroom) in which a
patient lives can be used that are capable of determining a
cardiovascular function of a human cardiovascular system. This
sensing milieu is capable of non-invasively and remotely
determining this cardiovascular function and trends in this
cardiovascular function. This sensing milieu senses various regions
of the patient, which can then be compared, time correlated,
aggregated, averaged, and so forth to determine a cardiovascular
function. These cardiovascular functions can be represented by
cardiovascular asymmetries (e.g., due to a stoke), cardiac
irregularities (e.g. atrial fibrillation), blood pressure,
pulse-wave velocity, waveforms of circulating blood,
photo-plethysmograms (PPG), ballistocardiograms, and
pressure-volume loops, to name a few.
[0026] With regard to the example computing device 108 of FIG. 1,
consider a detailed illustration in FIG. 2. Computing device 108
can be one or a combination of various devices, here illustrated
with seven examples: a smartphone 108-1, a server 108-2, a
computing watch 108-3, computing spectacles 108-4, a laptop 108-5,
a tablet computer 108-6, and a desktop 108-7, though other
computing devices and systems, such as a netbook or set-top box may
also be used. As noted above, in some embodiments the techniques
operate, in whole or in part, through a remote device such as
server 108-2. In such cases, some computing can be forgone locally,
e.g., through a communication device having limited computing
operations or even directly from optical sensors 106 to server
108-2.
[0027] Computing device 108 includes or is able to communicate with
a display 202 (six are shown in FIG. 2), a transceiver 204, one or
more processors 206, and computer-readable storage media 208 (CRM
208). Transceiver 204 is capable of sending and receiving data
directly or through a communication network, such as sensor data
112 from optical sensors 106 through a local area, wide area,
functional area, cellular, or near-field network.
[0028] CRM 208 includes cardiovascular-function module 210, which
includes or has access to sensor data 112 from one or more of
multiple optical sensors 106. This sensor data 112 can be
associated with particular times 212, such that simultaneously
received sensor data 112 can be correlated to determine
cardiovascular functions 214 of human cardiovascular systems and
trends 216 can be determined based on sensor data 112 changing over
time. CRM 208 also includes or has access to a user interface 218,
that, while not required, can be used to present determined trends,
health, and medical advice to patient 102.
[0029] Generally, cardiovascular-function module 210 is capable of
determining, based on sensor data 112, a cardiovascular function of
a cardiovascular system of a patient, such as patient 102 of FIG.
1. With this cardiovascular function, cardiovascular-function
module 210 may alert patient 102 or medical professionals 104 or
family members/caretakers of a negative health condition needing
immediate care, for example. Medical professional 104, or a
specialized machine intelligence, could schedule an in-person
appointment or remotely adjust patient care through changes in
medication or lifestyle. Cardiovascular-function module 210 is also
configured to determine trends based on the current cardiovascular
function and prior-determined cardiovascular functions, such as
those determined at prior times.
[0030] More specifically, cardiovascular-function module 210 is
capable of receiving and using optical sensor data indicating a
skin, organ, or structure's color or displacement. This data may
come from single or multiple optical sensors covering the same or
different wavelengths observing multiple locations on the patient's
body. With this data, cardiovascular-function module 210 can
determine timing relationships, pulse pressure waveforms, and
asymmetries in a patient's cardiovascular system. With this data
and a circulatory distance between data from different regions of
the patient, as well as time correlations between the data,
cardiovascular-function module 210 can determine a pulse-wave
velocity and various simple or highly sophisticated measures of
cardiovascular function, including charts of blood pressure, a
ballistocardiogram, a photo-plethysmogram (PPG), and
pressure-volume loops. Capabilities of cardiovascular-function
module 210 are addressed further in methods described below.
[0031] With regard to optical sensors 106, two examples of which
are shown in FIG. 1, consider a detailed illustration in FIG. 3.
Generally, optical sensors 106 are capable of detecting blood
volume, color, and/or displacement at one or more regions of a
patient. Optical sensors 106 may include a standard RGB (red,
green, blue) sensor, a monochrome sensor, a hyperspectral sensor, a
stereoscopic sensor, a structured light sensor, or combinations of
multiple sensors, along with a combination of illumination sources
such as uniform, infrared, tangential, modulated/coded, or coherent
(laser). Optical sensors 106 may also have a fixed camera position
or consist of one or more cameras and light sources on mechanical
targeting platforms or those that simply move due to being part of
a mobile device. Optical sensors 106 may also be separated into
physically and spatially distinct devices capable of monitoring the
body from multiple view angles or observing different regions of
the body. Thus, one of optical sensors 106 may capture an image
indicating blood volume at two different regions of patient 102,
which then can be compared, by cardiovascular-function module 210,
to determine a blood-volume asymmetry or other cardiac function. In
the case of a blood-volume asymmetry, a difference in vascular
function between the regions may indicate a cardiac-related health
problem, such as a stroke. Optical sensors 106 provide various
types of information, and are not limited to determining
asymmetries.
[0032] In more detail, optical sensor 106 can be one or a
combination of various devices, here illustrated with color and
displacement optical sensor 106-1 (e.g., a camera of computing
device 108), sensor 106-2, which is stationary and located within
mirror 110, a wearable color and displacement optical sensor 106-3,
which is part of computing spectacles 108-4, structured-light or
stereoscopic sensor system 106-4, and optic sensor 106-5 of laptop
108-5. The cameras can also be motorized to accurately point at
specific points on the body.
[0033] As noted in part, sensor 106-2 is capable of capturing
images in an ultraviolet, visible, or infrared optical wavelength.
Images recording these wavelengths can be used to determine various
changes in blood movement or as calibration signals to detect
changes in illumination or patient movement. In some cases blood
perfusion and oxygen content can be ascertained, thereby further
enabling robust measurement of cardiac function. Due to
differential wavelength absorption between human tissue and blood,
a hyperspectral sensor can also be used to penetrate the skin to
map out veins and arteries to target closer examination for
displacement and other measurements.
[0034] Structured-light sensor system 106-4 is capable of
projecting structured light at patient 102 and sensing, often with
two or more optical sensors, the projected structured light on
patient 102 effective to enable capture of images having surface
information. This surface information can be used to calculate
depth and surface changes for a region of patient 102, such as
skin, another organ, or other structure. These changes can be
highly accurate, thereby indicating small vibrations and other
changes in an organ or structure caused by the cardiovascular
system, and thus how that system is operating. Structured-light
sensor system 106-4 can, alternatively, be replaced with or
supplemented with a targeted, coherent light source for
more-accurate displacement measurements. This may include LIDAR
(e.g., "light radar" or the process measuring distance by
illuminating a target with a laser and analyzing light reflected
from the target), laser interferometry, or a process of analyzing
light speckle patterns produced by a coherent light on a skin's
surface through optical tracking, which enables detection of very
small skin displacements.
[0035] These optical sensors 106 can capture images with sufficient
resolution and at sufficient shutter speeds to show detailed colors
and displacement, and thus enable determination of mechanical
movements or vibrations. These mechanical movements and mechanical
vibrations are sufficient to determine a ballistocardiogram (BCG)
showing patient 102's cardiac function. Other sensing manners, such
as color change or skin displacement in a different region of a
patient's body, can be used to establish motion frequency bands to
amplify, as well as a timing reference for aggregating multiple
heartbeat measurements to improve accuracy of a BCG motion. This
BCG information can also be used to provide reference timing
information about when a blood pressure pulse leaves the left
ventricle and enters the aorta, which combined with the other
measurements across the body allows for more-precise estimates of
pulse transit times and pulse-wave velocities.
[0036] While the BCG signal indicates the timing of the aortic
valve, the timing of the atrial valve can be monitored by tracking
atrial pressure waveforms visible in the external or internal
jugular. This also allows for the opportunity to detect atrial
fibrillation by detecting missing atrial-pressure pulses.
Additionally, aortic-wall stiffness has proven prognostic value in
predicting cardiovascular morbidity and mortality. Measuring the
pulse-transit time from the start of ejection from the left
ventricle into the aorta and up the carotid allows an estimate of
that aortic stiffness as well as trending of changes in that
stiffness. Thus, determination of arterial-wall stiffness can made
independent of blood pressure measurements.
[0037] In more detail, optical sensors 106 are configured to
capture sufficient information for the techniques to determine
blood asymmetries and other cardiac function, including a
pulse-wave velocity of patient 102's blood. This pulse-wave
velocity is a measure of a patient's arterial health. In healthy
arteries the pulse-wave velocity is low due to the elasticity of
the arteries but, as they harden and narrow, the pulse-wave
velocity rises. Additionally, as blood pressure increases and
dilates the arteries, the walls become stiffer, increasing the
pulse-wave velocity. While a particular pulse-wave velocity as a
snapshot in time may or may not accurately indicate cardiovascular
health (e.g., a one-time test at a doctor's office), a change in
this pulse-wave velocity (that is, a trend), can be an accurate
measure of a change in patient 102's cardiovascular health. If a
positive trend, this can reinforce patient 102's healthy habits
and, if negative, encourage changes to be made.
[0038] In more detail, each of the color-sensing optical sensors
106 is configured to record colors in a patient's skin sufficient
to determine a photo-plethysmogram. This PPG measures variations in
a size or color of an organ, limb, or other human part from changes
in an amount of blood present in or passing through it. These
colors and color variations in a patient's skin can show heart rate
and efficiency.
[0039] These examples show some ways in which the techniques can
provide substantially more-valuable (or at least different) data by
which to assess a patient's cardiac function than those provided in
a medical office or hospital. As noted, conventional health
monitoring is often performed at a hospital or medical
practitioner's office. Health monitoring at a hospital or office,
however, cannot monitor a patient during their normal course of
life or as often as desired. This can be a serious limitation
because a snapshot captured at a hospital or office may not
accurately reflect the patient's health or may not performed at all
due to the infrequency of a patient's visits. Even if testing at a
hospital or medical office is performed often, it can be inaccurate
due to it being of a short duration or due to the testing being in
an artificial environment. Note that this does not preclude the use
of the techniques disclosed herein at a hospital or medical office,
where they may prove valuable in supplementing or replacing
conventional measurements, and in the case of in-patient care, may
provide a manner for continuous monitoring of patients that are
critically (or otherwise) ill.
[0040] Returning to FIG. 3, optical sensor 106 generally may have
various computing capabilities, though it may instead be a
low-capability device having little or no computing capability.
Here optical sensor 106 includes one or more computer processors
302, computer-readable storage media 304, image capture element
306, and a wired or wireless transceiver 308 capable of receiving
and transmitting information (e.g., to computing device 108). Image
capture element 306 may include simple or complex cameras, such as
those having low or high shutter speeds, low or high frame rates,
low or high resolutions, and having or not having non-visible
imaging capabilities. Computer-readable storage media 304 includes
optics manager 310, which is capable of processing sensor data and
recording and transmitting sensor data, as well as receive or
assign appropriate time markers by which to mark or compare the
time of various captured images. Optics manager 310 and
cardiovascular-function module 210 may also calibrate image capture
element 306 through use of an external sensor. This can aid in
calibrating skin colors or displacements to a calibration color or
displacement, or even to a cardiac function, such as to a blood
pressure or pulse-wave velocity. Thus, while one of optical sensors
106 captures images for two regions, a blood pressure between those
regions is also measured through a different device, thereby
enabling more-accurate determination of cardiac functions for the
optical sensor and for that patient. Other potential calibration
sensors include, but are not limited to, ECG, conventional BCG,
digital stethoscopes, ultrasound, and the like. Another example is
the use of an external blood pressure meter to calibrate the
pulse-wave velocity over time to determine long-term changes in
arterial-wall stiffness by separating arterial stiffness due to
blood pressure versus that due to the dilation by blood
pressure.
[0041] These and other capabilities, as well as ways in which
entities of FIGS. 1-3 act and interact, are set forth in greater
detail below. These entities may be further divided, combined, and
so on. The environment 100 of FIG. 1 and the detailed illustrations
of FIGS. 2 and 3 illustrate some of many possible environments
capable of employing the described techniques.
EXAMPLE METHODS
[0042] FIGS. 4 and 8 depict methods that assess cardiovascular
function using an optical sensor. These methods are shown as sets
of blocks that specify operations performed but are not necessarily
limited to the order or combinations shown for performing the
operations by the respective blocks. In portions of the following
discussion reference may be made to environment 100 of FIG. 1 and
entities detailed in FIGS. 2 and 3, reference to which is made for
example only. The techniques are not limited to performance by one
entity or multiple entities operating on one device.
[0043] At 402, skin colors or skin displacements are received from
one or more optical sensors. These skin colors or displacements are
captured at regions of a patient, such as a color captured at a
patient's skin on her forehead and a displacement of skin on her
neck or on her clavicle. Optionally, as part of operation 402,
cardiovascular-function module 210 or optics manager 310 may
automatically determine which regions of a patient are fully
visible or partially occluded, and thereby determine better regions
of a patient to capture images.
[0044] By way of illustration, consider FIG. 5, which shows a male
patient 502 having various regions 504 of which images are
captured. These regions 504 include, by way example, a cheek region
504-1, a neck region 504-2, an outer wrist region 504-3, an outer
hand region 504-4, an inner wrist region 504-5, a palm region
504-6, a front ankle region 504-7, and an inner ankle region 504-8,
to name but a few. By way of an ongoing example, assume that one
optical sensor captures a color change or displacement of skin at
neck region 504-2 and another color change or displacement of skin
at inner wrist region 504-5.
[0045] At 404, a circulatory distance is determined between the
regions of the patient at which the colors or displacements are
captured. This circulatory distance can be an approximation based
on a linear distance between the regions, such as a linear distance
based on an axial distance oriented relative to an axis of the
patient's spine, or simply a vertical distance with the patient
standing. In some cases, however, the techniques determine or
approximate a circulatory distance based on an arterial-path
distance. This arterial-path distance can be determined or
approximated using an arterial structure of the patient or
determined based on a skeletal structure of the patient, including
automatically by optical visualization of the patient.
[0046] By way of illustration of the various circulatory distances
that can be used, consider FIG. 6. Here assume that multiple images
are captured of patient 502's neck region 504-2 (also shown in FIG.
5) sufficient to determine a neck waveform 602. Multiple images are
also captured of inner wrist region 504-5 sufficient to determine
an inner-wrist waveform 604. At operation 404,
cardiovascular-function module 210 determines the circulatory
distance from neck region 504-2 and inner wrist region 504-5 in one
of the following four manners. In the first, a vertical distance
D.sub.v is calculated with the patient standing. In the second, an
axial distance D.sub.axial is calculated based on the distance
relative to an axis of the patient's spine--here it is similar to
the vertical distance, D.sub.v, but if the person is oriented at an
angle, the distances are different. In the third,
cardiovascular-function module 210 calculates the distance as a
point-to-point between the regions, here shown as D.sub.ptp. In the
fourth, cardiovascular-function module 210 calculates or
approximates the distance that blood travels through patient 502's
arteries, D.sub.path. This arterial-path distance can be determined
based on the arteries themselves or an approximation based on a
skeletal structure or an overall body shape of the person. Data for
skeletal structure and overall body shape can be determined using
images captured for the regions and structures in between the
regions, optically or otherwise. In some cases radar can be used
that penetrates clothing to track bony surfaces, thereby providing
a skeletal structure from which arterial distance can be
approximated.
[0047] While not required, operation 404 may be performed, in whole
or in part, using method 700 illustrated in FIG. 7, which is
described following method 400 below. By way of overview, in this
example method, the techniques determine one or more of the
distances illustrated in FIG. 6.
[0048] The more-accurate distance calculations provide a better
pulse-wave velocity, and thus indicate a current cardiovascular
function. While potentially valuable, more-accurate distances are
not necessarily required to show trends in cardiovascular function.
Trends are provided by consistently calculated distances more than
accurate distances, and for a specific individual, should not
change significantly over time for same measurement points. If the
measurement points vary due to visibility issues (such as
clothing), then distance measurement estimates increase in
importance for accurate trending.
[0049] At 406, a time correlation between capture of the colors and
displacements is determined. This time correlation is between the
instant of capture at the regions, as this time correlation is
later used. Cardiovascular-function module 210 may determine the
time correlation based on a time at which a maximum or minimum
blood volume is determined for each of the regions, or some other
consistent and comparable point in a waveform, such as a beginning
of a pressure increase in the waveform (show in FIG. 6). In more
detail, this time correlation can be considered a temporal distance
between multiple images capturing some measure of cardiac
operation, such as blood volume at each of the regions. Thus, by
comparing various images for a region cardiovascular-function
module 210 can determine a maximum, minimum, or median color at the
region as well as at another region, and by comparing these and
times at which each were taken, can determine the time correlation
for a same heartbeat.
[0050] Note that waveforms 602 and 604 can be determined through
color, or in some locations of the body, related waveforms can be
determined through displacement. Cardiovascular-function module 210
can determine, based on a change in color to regions over time, a
waveform. These color changes indicate a peak or crest of a wave
based on blood content at the organ and thus can be used to
determine a shape of the wave. While a shape of a wave can differ
at different regions, they can still be compared to find a time
correlation. In the case of lower-than-desired optical frame rates
due to sensitivity or processing limitations, interpolation or
curve fitting can be used to improve the estimate of the waveform
for improved time correlation. Repeated measurements, which are
time shifted relative to the pressure wave either naturally by the
optical sampling frequency or intentionally by adjusting the
sampling frequency, can build up a super-sampled estimate of the
waveform. The higher timing-resolution waveform can be used for
more-accurate timing measurements. Additionally, displacements,
either through direct distance measurements or tangential shading,
can show signals related to the pressure waveforms as the arteries
and veins expand and contract. These waveforms can further reveal
cardiac activity, such as valve timing, valve leakage
(regurgitation), fibrillation, stroke volume, and the like.
[0051] At 408, a pulse-wave velocity for blood circulation through
the patient is determined based on the circulatory distance and the
time correlation, as well as the skin colors or displacements. As
shown in FIG. 6, the time correlation is based on similar points in
a waveform and the circulatory distance is some calculation or
approximation of the distance blood travels from regions at which
images are captured. In more detail, a pulse-wave velocity is the
circulatory distance divided by the time correlation.
[0052] Pulse-wave velocity is a good measure of cardiac function.
It can indicate, for example, an arterial stiffness of a patient
(the faster the pulse-wave velocity, the higher the arterial
stiffness), a blood pressure, and a mean arterial pressure for the
patient. In more detail, the techniques can determine blood
pressure based on the pulse-wave velocity using the Bramwell-Hill
equation, which links pulse-wave velocity to compliance, blood mass
density, and diastolic volume. Each of these are measures of
cardiac function that can indicate a patient's cardiac health. As
noted above, the techniques can provide these cardiac functions to
a patient, thereby encouraging the patient to make changes or, in
some cases, seek immediate medical care.
[0053] Note that, in some cases, three or more different regions
are measured at operation 402. In these cases,
cardiovascular-function module 210 may determine which of the
regions are superior to others, such as due to data captured for
those regions being noisy or incomplete or otherwise of inferior
quality. Those that are superior can be used and the others
discarded, or cardiovascular-function module 210 may weigh the
determined pulse-wave velocity between different regions based on
the quality of the data used to determine those pulse-wave
velocities. This can be performed prior to or after recording those
pulse-wave velocities as described below.
[0054] Following determination of the pulse-wave velocity at
operation 408, the techniques may proceed to record the pulse-wave
velocity at operation 410 and the repeat operations 402-410
sufficient to determine a trend at operation 412. In some cases,
however, the determined pulse-wave velocity is provided, at
operation 414, to the patient or medical professional. Optionally,
calibration data from an external sensor can be used to improve
performance. For example, an external blood pressure monitor could
be used to train the system to correlate PWV with blood pressure.
The device could be captured through an electronic network
(Bluetooth.TM. or the like) or the optical system could scan the
user interface and perform OCR to read the results. Machine
learning could be applied to create a patient specific model for
estimating blood pressure from PWV.
[0055] At 412, a cardiovascular trend for the patient is determined
based on multiple pulse-wave velocity measurements, such as
comparing prior and later-time determined pulse-wave velocities.
This can simply show a trend of pulse-wave velocities rising or
falling, such as with velocity rising due to increased arterial
stiffness. Multiple locations across the body can be measured to
map changes over time. Cardiovascular-function module 210 may also
determine other measures of cardiac function, such as changes in
flow asymmetries or pulse pressure waveforms over time.
[0056] At 414, as noted, this trend determined at operation 412, or
a pulse-wave velocity determined at operation 408, is provided to
the patient or a medical professionals, e.g., patient 102 or 600
and medical professional 104, of FIG. 1 or 6.
[0057] In some cases skin color, skin displacement, or both are
used by the techniques in method 400. Thus, color changes can
indicate blood flow over time, as can displacement changes.
Furthermore, use of color and displacement both can indicate an
amount of blood in capillaries in the skin while displacement can
indicate a change to a volume of the skin or an organ under the
skin, such as vein or artery, and thus an amount of blood in the
skin or near it can be determined.
[0058] Note also that the techniques may repeat operations of
method 400 for various other regions. Doing so may aid in altering
the pulse-wave velocity to improve its accuracy or robustness by
determining another pulse-wave velocity between two other regions
or between another region and one of the regions for which images
are captured. Thus, the techniques may determine a pulse-wave
velocity for the patient based on two pulse-wave velocities between
regions, such as regions 504-3 and 504-1, 504-7 and 504-1, and/or
504-8 and 504-2.
[0059] As noted above, method 400 can be supplemented, and
operation 404 may be performed, in whole or in part, using method
700 illustrated in FIG. 7. In this example method, the techniques
determine one or more of the distances illustrated in FIG. 6. For
operations 702-706, a patient's circulatory distances between
regions are establish for later use as a manner in which to
calibrate the patient's distances. While calibration for a single
sensing milieu to determined trends may not be required, use of
different sensing milieus or to determine a cardiovascular function
with quantitative precision both aid from use of calibration.
Operation 708 and 710 can be used as one way in which the
techniques may perform operation 404 of method 400.
[0060] At 702, a distance between various regions is measured,
optically, manually, or in other manners. Consider, for example,
capturing an image of patient 502 of FIG. 5. Assume that some
physical data is available, such as a distance between the optical
sensor capturing the image and patient 502, or a height of patient
502, and so forth. With this physical data, the distance can be
determined from the image. Generally, this distance is from
point-to-point, and is later analyzed for circulatory distance.
Other manners can also or instead be used, such as a nurse
measuring patient 502, either from point-to-point or along
structures, such as from a wrist to an elbow, elbow to shoulder,
and from shoulder to heart. A patient may also interact with
optical sensor 106 and cardiovascular-function module 210 to
calibrate distances between regions, such as standing at a
particular location relative to optical sensor 106 and so forth.
Various other technologies can be used as well, such as structured
light optical sensors, radar, LIDAR, and SODAR (measuring distance
through use of sound through air).
[0061] At 704, a circulatory distance is determined using the
measured distance. In some cases the measured distance is simply
used as the circulatory distance, such as measuring D.sub.ptp and
then using D.sub.ptp (of FIG. 6) as the circulatory distance. As
noted in part herein, however, other circulatory distances may be
determined, such as measuring a point-to-point where patient 502's
arm is bent, and thus calculating a fully extended point-to-point
to maintain consistency of circulatory distance. Other examples
include measuring D.sub.v and then, based on data about patient
502, determining an arterial-path distance (D.sub.path).
[0062] At 706, these various determined circulatory distances are
associated with the patient's identity. The identity of the patient
can be entered, queried from the patient, or simply associated with
some repeatable measure of identity, even if the person's name is
not known. Examples include determining identity using fingerprints
or facial recognition, and then associating distances with that
fingerprint or facial structure.
[0063] At 708, the patient's identity is determined. This can be
performed as part of operation 404. With this identity, at 710
circulatory distances between regions are determined. For example,
cardiovascular-function module 210 may use facial recognition to
identify patient 502 and, after determining patient 502's identity,
find previously determined cardiovascular distances between each of
regions 504 by simply mapping the relevant regions to previously
stored distances. When cardiovascular time correlations are
determined at operation 406, a pulse-wave velocity can be
determined using the mapped-to cardiovascular distance for the
regions measured.
[0064] FIG. 8 depicts a method for assessing cardiovascular
function using an optical sensor based on size, volume, or location
of an organ or structure of a patient. In method 800, images are
captured over 2 to 10 millisecond-range or faster timeframes,
thereby providing multiple images relating to an organ or structure
of the patient. Note that sub-millisecond timeframes can also be
useful for measure acoustic vibrations and are optional. Method 800
may operate, in whole or in part, in conjunction with method 400,
though this is not required.
[0065] At 802, structured light is projected onto an organ or
structure of a patient. Note that this is optional, though in some
cases use of structured light aids in accurate measurement of
movement and displacement of a region of the patient.
Alternatively, tangential light may be used to generate shadowing
to detect skin displacement, or a coded light source could be used
to reject external interference. For example, an alternating on and
off light source at the frame rate would allow sampling and
canceling of the background illumination. Further, light reflected
from background objects or patient clothing can be used to track
changes in lighting over time or in different conditions, e.g.,
daylight vs night, light bulb luminosity degradation over time, and
so forth. With this data, ambient light and its effect on images
captured can be calibrated and for which cardiovascular-function
module 210 can adjust for the various methods described herein.
[0066] At 804, multiple images are received that capture an organ
or structure of a patient. As noted, the images captured may
include capture of structured light to aid in determining
displacement using surface information captured. This surface
information can be from one or multiple devices. These multiple
images can be received from one or multiple optical sensors and
over various timeframes, such as those captured at
millisecond-range or faster timeframes.
[0067] At 806, changes in the size, volume, or location of the
organ or structure of the patient are determined. These changes are
determined by comparing sizes, volumes, or locations of the organ
or structure of the patient recorded by the various multiple images
captured over time. Note that these changes can be used in
coordination with, or to compensate for, data from methods 400, and
vice-versa. Thus, data from one portion of the body captured in any
of the various manners described herein, can be used to compensate
for other data, such as using a color or waveform determined at
method 400 to compensate for motion artifacts in the data of method
800.
[0068] At 808, a cardiac function of the patient is determined
based on the changes. This cardiac function can be one of the many
described above, including heart rate, blood pressure, pulse-wave
velocity, pressure volume loops, blood-volume and other
asymmetries, and so forth, as well as respiration rate.
[0069] By way of a first example, consider a case where an
asymmetry is determined between to different regions of the
patient. In some cases this asymmetry is determined by blood-volume
differences, which can be indicated by size or color. To determine
an asymmetry, cardiovascular-function module 210 may compare the
different cardiovascular pulse times of the regions, where one of
the pulse times for a same heart beat is different, as it is
further from the patient's heart. Alternatively, the waveform's
peak, median, or trough of blood volume can be accurately compared.
Thus, assume that a right wrist and a left wrist of a patient have
different blood volumes at each of their peaks, with one being a
lower peak blood volume that the other, thereby indicating some
difference in vascular function.
[0070] Cardiac function trends, as noted in part above, can greatly
aid in helping patients maintain or change their habits to improve
their cardiac health. Consider, for example, a trend showing a
change to a cardiovascular function over weeks, months, or years
using the techniques. This trend can show cardiac function in many
ways superior to the best invasive cardiac testing because a trend
need not require perfect accuracy--instead consistency is used.
Furthermore, this can be performed by the techniques without
interrupting the patient's day, making the patient perform a test,
or requiring the patient to go see a medical professional. By so
doing, many lives can be saved.
[0071] In more detail, consider the techniques in the context of
FIGS. 1-3. Here various kinds of optical sensors 106 sense regions
(e.g., regions 504 of FIG. 5) of a patient (e.g., patient 102 of
FIG. 1 or patient 502 of FIGS. 5 and 6) through image capture
elements 306. This sensor data (e.g., images) are then processed
and/or stored by optics manager 310 (e.g., to mark the images with
times), after which they are passed, through wired/wireless
transceiver 308 as sensor data 112 to cardiovascular-function
module 210 operating on computing device 108 of FIG. 2. Also passed
are indications of the region and the times 212 at which the sensor
data 112 was captured.
[0072] Cardiovascular-function module 210 then performs operations
of method 400 and/or method 800 to determine cardiac function, as
noted above. Consider, for example, a case where
cardiovascular-function module 210 determines that a cardiac
function meets or exceeds a safety threshold. Example safety
thresholds include a blood pressure being too high, a heart rate
being too rapid or irregular, or a low blood-oxygen level. This
safety threshold can also be complicated or more difficult to
determine, such as a patient's heart showing an end-diastolic
volume ejected out of a ventricle during a contraction being less
than 0.55 (this is a measure of ejection fraction (EF) and low
fractions can indicate a heart attack is imminent). These are but a
few of the many safety thresholds for cardiac function enabled by
the techniques. If a safety threshold is exceeded, medical
professional 104 (or family/caretaker) and patient 102 can be
informed, such by operation 810 of method 800.
[0073] The preceding discussion describes methods relating to
assessing cardiovascular function using an optical sensor for a
human cardiovascular system. Aspects of these methods may be
implemented in hardware (e.g., fixed logic circuitry), firmware,
software, manual processing, or any combination thereof. These
techniques may be embodied on one or more of the entities shown in
FIGS. 1-3 and 9 (computing system 900 is described in FIG. 9
below), which may be further divided, combined, and so on. Thus,
these figures illustrate some of the many possible systems or
apparatuses capable of employing the described techniques. The
entities of these figures generally represent software, firmware,
hardware, whole devices or networks, or a combination thereof.
EXAMPLE COMPUTING SYSTEM
[0074] FIG. 9 illustrates various components of example computing
system 900 that can be implemented as any type of client, server,
and/or computing device as described with reference to the previous
FIGS. 1-8 to implement techniques for assessing cardiovascular
function using an optical sensor. In embodiments, computing system
900 can be implemented as one or a combination of a wired and/or
wireless wearable device, System-on-Chip (SoC), and/or as another
type of device or portion thereof. Computing system 900 may also be
associated with a user (e.g., a patient) and/or an entity that
operates the device such that a device describes logical devices
that include users, software, firmware, and/or a combination of
devices.
[0075] Computing system 900 includes communication devices 902 that
enable wired and/or wireless communication of device data 904
(e.g., received data, data that is being received, data scheduled
for broadcast, data packets of the data, etc.). Device data 904 or
other device content can include configuration settings of the
device, media content stored on the device, and/or information
associated with a user of the device. Media content stored on
computing system 900 can include any type of audio, video, and/or
image data, including complex or detailed results of cardiac
function determination. Computing system 900 includes one or more
data inputs 906 via which any type of data, media content, and/or
inputs can be received, such as human utterances, user-selectable
inputs (explicit or implicit), messages, music, television media
content, recorded video content, and any other type of audio,
video, and/or image data received from any content and/or data
source.
[0076] Computing system 900 also includes communication interfaces
908, which can be implemented as any one or more of a serial and/or
parallel interface, a wireless interface, any type of network
interface, a modem, and as any other type of communication
interface. Communication interfaces 908 provide a connection and/or
communication links between computing system 900 and a
communication network by which other electronic, computing, and
communication devices communicate data with computing system
900.
[0077] Computing system 900 includes one or more processors 910
(e.g., any of microprocessors, controllers, and the like), which
process various computer-executable instructions to control the
operation of computing system 900 and to enable techniques for, or
in which can be embodied, assessing cardiovascular function using
an optical sensor. Alternatively or in addition, computing system
900 can be implemented with any one or combination of hardware,
firmware, or fixed logic circuitry that is implemented in
connection with processing and control circuits which are generally
identified at 912. Although not shown, computing system 900 can
include a system bus or data transfer system that couples the
various components within the device. A system bus can include any
one or combination of different bus structures, such as a memory
bus or memory controller, a peripheral bus, a universal serial bus,
and/or a processor or local bus that utilizes any of a variety of
bus architectures.
[0078] Computing system 900 also includes computer-readable media
914, such as one or more memory devices that enable persistent
and/or non-transitory data storage (i.e., in contrast to mere
signal transmission), examples of which include random access
memory (RAM), non-volatile memory (e.g., any one or more of a
read-only memory (ROM), flash memory, EPROM, EEPROM, etc.), and a
disk storage device. A disk storage device may be implemented as
any type of magnetic or optical storage device, such as a hard disk
drive, a recordable and/or rewriteable compact disc (CD), any type
of a digital versatile disc (DVD), and the like. Computing system
900 can also include a mass storage media device 916.
[0079] Computer-readable media 914 provides data storage mechanisms
to store device data 904, as well as various device applications
918 and any other types of information and/or data related to
operational aspects of computing system 900. For example, an
operating system 920 can be maintained as a computer application
with computer-readable media 914 and executed on processors 910.
Device applications 918 may include a device manager, such as any
form of a control application, software application,
signal-processing and control module, code that is native to a
particular device, a hardware abstraction layer for a particular
device, and so on.
[0080] Device applications 918 also include any system components,
modules, or managers to implement the techniques. In this example,
device applications 918 include cardiovascular-function module 210
or optics manager 310.
CONCLUSION
[0081] Although embodiments of techniques for, and apparatuses
enabling, assessing cardiovascular function using an optical sensor
have been described in language specific to features and/or
methods, it is to be understood that the subject of the appended
claims is not necessarily limited to the specific features or
methods described. Rather, the specific features and methods are
disclosed as example implementations of these techniques.
* * * * *