U.S. patent application number 12/861888 was filed with the patent office on 2011-03-17 for estimation of blood flow and hemodynamic parameters from a single chest-worn sensor, and other circuits, devices and processes.
This patent application is currently assigned to TEXAS INSTRUMENTS INCORPORATED. Invention is credited to Edwin Randolph Cole, Keya R. Pandia, Sourabh Ravindran.
Application Number | 20110066042 12/861888 |
Document ID | / |
Family ID | 43731246 |
Filed Date | 2011-03-17 |
United States Patent
Application |
20110066042 |
Kind Code |
A1 |
Pandia; Keya R. ; et
al. |
March 17, 2011 |
ESTIMATION OF BLOOD FLOW AND HEMODYNAMIC PARAMETERS FROM A SINGLE
CHEST-WORN SENSOR, AND OTHER CIRCUITS, DEVICES AND PROCESSES
Abstract
An electronic monitoring device includes an electronic processor
(520) having at least one signal input for body monitoring, and a
memory (530) holding instructions for the electronic processor
coupled to the electronic processor so that the electronic
processor is operable to isolate a cardiac signal including cardiac
pulses combined with other cardiac signal variations, and the
electronic processor further operable to execute a filter (730)
that separates a varying blood flow signal from the cardiac pulses
and to output information (790) based on at least the varying blood
flow signal. Other devices, sensor assemblies, electronic circuit
units, and processes are also disclosed.
Inventors: |
Pandia; Keya R.; (Stanford,
CA) ; Ravindran; Sourabh; (Dallas, TX) ; Cole;
Edwin Randolph; (Highland Park, TX) |
Assignee: |
TEXAS INSTRUMENTS
INCORPORATED
Dallas
TX
|
Family ID: |
43731246 |
Appl. No.: |
12/861888 |
Filed: |
August 24, 2010 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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61242688 |
Sep 15, 2009 |
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61262336 |
Nov 18, 2009 |
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61262331 |
Nov 18, 2009 |
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Current U.S.
Class: |
600/484 ;
600/513; 600/526 |
Current CPC
Class: |
A61B 5/725 20130101;
A61B 2560/0475 20130101; A61B 5/113 20130101; A61B 5/318 20210101;
A61B 2505/07 20130101; A61B 5/316 20210101; A61B 7/008 20130101;
A61B 2562/0219 20130101; A61B 5/7207 20130101; A61B 2562/028
20130101; A61B 5/6831 20130101; A61B 5/1102 20130101; A61B 5/029
20130101; A61B 7/00 20130101; A61B 5/7278 20130101 |
Class at
Publication: |
600/484 ;
600/513; 600/526 |
International
Class: |
A61B 5/0205 20060101
A61B005/0205; A61B 5/0402 20060101 A61B005/0402; A61B 5/026
20060101 A61B005/026; A61B 5/029 20060101 A61B005/029 |
Claims
1. An electronic monitoring device comprising: an electronic
processor having at least one signal input for body monitoring; and
a memory holding instructions for said electronic processor coupled
to said electronic processor so that said electronic processor is
operable to isolate a cardiac signal including cardiac pulses
combined with other cardiac signal variations, and said electronic
processor further operable to execute a filter that separates a
varying blood flow signal from the cardiac pulses and to output
information based on at least the varying blood flow signal.
2. The electronic monitoring device claimed in claim 1 wherein said
electronic processor is operable to also separate cardiac pulses
from the cardiac signal and produce an electronic representation of
a time interval between a selected cardiac pulse and an event on
the varying blood flow signal.
3. The electronic monitoring device claimed in claim 2 wherein the
selected cardiac pulse is an S1 pulse.
4. The electronic monitoring device claimed in claim 2 wherein the
event is a peak in the varying blood flow signal after the selected
cardiac pulse.
5. The electronic monitoring device claimed in claim 2 wherein said
electronic processor is operable to produce a measurement of jitter
across heart beats of the time interval.
6. The electronic monitoring device claimed in claim 1 wherein said
electronic processor is operable to output an electronic
representation of iso-volumic contraction interval using the
varying blood flow signal.
7. The electronic monitoring device claimed in claim 6 wherein said
electronic processor is operable to produce an electronic
representation of contractility using the iso-volumic contraction
interval.
8. The electronic monitoring device claimed in claim 1 wherein the
filter for said blood flow signal passes an oscillatory cardiac
signal component and substantially rejects cardiac S1 and S2 signal
peaks, thereby to separate the varying blood flow signal.
9. The electronic monitoring device claimed in claim 1 wherein the
filter for said blood flow signal smoothes the cardiac signal using
a low-order polynomial filter with a time window approximately
comparable in size to a period of oscillation of the varying blood
flow signal.
10. The electronic monitoring device claimed in claim 9 wherein
said electronic processor is operable to subtract from the cardiac
signal the result of the smoothing filter to deliver a residue
signal primarily featuring the cardiac pulses separated from the
cardiac signal.
11. The electronic monitoring device claimed in claim 10 wherein
said electronic processor is operable to count the cardiac pulses
to deliver a heart rate signal.
12. The electronic monitoring device claimed in claim 1 wherein the
filter for said blood flow signal smooths the cardiac signal using
an approximately-4th order Savitzky-Golay polynomial filter with a
window size approximately 200 milliseconds.
13. The electronic monitoring device claimed in claim 1 wherein
said electronic processor is operable to digitally low pass filter
an input thereof with a cutoff lower than a power line frequency to
isolate the cardiac signal.
14. The electronic monitoring device claimed in claim 1 further
comprising at least two analog signal paths coupled to said
electronic processor, each analog signal path including an AC
coupled amplifier feeding a low pass anti-alias filter feeding a
sampling analog-to-digital converter coupled to said electronic
processor.
15. The electronic monitoring device claimed in claim 1 wherein the
filter for said blood flow signal smoothes the cardiac signal using
a low-order polynomial filter to obtain the varying blood flow
signal, and said electronic processor instructions also define a
substantially higher-order polynomial filter concurrently operable
to deliver a residue signal having cardiac pulses.
16. The electronic monitoring device claimed in claim 1 wherein the
electronic processor is further operable to perform electronic peak
detection on the varying blood flow signal to identify a flow peak
amplitude and a time location F1 of that peak.
17. The electronic monitoring device claimed in claim 16 wherein
the electronic processor is further operable to separate the
cardiac pulses and identify their first peak location in time P1
for a same heart beat as applies to the time location F1.
18. The electronic monitoring device claimed in claim 17 wherein
the electronic processor is further operable to generate a
hemodynamic parameter based on the time difference between time P1
and the time F1.
19. The electronic monitoring device claimed in claim 17 wherein
the electronic processor is further operable to generate
beat-by-beat values of the time difference between time P1 of the
peak of an S1 cardiac pulse and the time F1 of the peak of the
varying blood flow signal.
20. The electronic monitoring device claimed in claim 19 wherein
said electronic processor is further operable to derive a
respiration-related signal from variations in the beat-by-beat
values of the time difference.
21. The electronic monitoring device claimed in claim 1 wherein
said electronic processor is further operable to perform electronic
peak detection on the varying blood flow signal to identify a flow
peak amplitude PAmp and generate a hemodynamic parameter as a
function of the flow peak amplitude PAmp.
22. The electronic monitoring device claimed in claim 21 wherein
said electronic processor is further operable to derive a signal,
related to at least one of respiration, intrapleural pressure, and
intrathoracic pressure, from variations in beat-by-beat values of
the flow peak amplitude PAmp.
23. The electronic monitoring device claimed in claim 21 wherein
said electronic processor is further operable to obtain the cardiac
pulses and a heart rate HR therefrom, and further to generate a
hemodynamic parameter as a function of the flow peak amplitude PAmp
times the heart rate HR.
24. The electronic monitoring device claimed in claim 1 further
comprising a modem coupled to said electronic processor.
25. The electronic monitoring device claimed in claim 24 wherein
said modem is selected from the group consisting of: 1) cellular,
2) Wi-Fi, 3) wireline, 4) telephone modem.
26. The electronic monitoring device claimed in claim 1 wherein
said electronic processor is also operable to substantially filter
out baseline-wander below about one-half Hertz from the blood flow
signal.
27. An accelerometer sensor assembly having a broadside portion and
comprising an accelerometer sensor circuit having an axis of
acceleration sensitivity parallel to the broad side and orientable
on the chest to deliver an input signal representing a component of
acceleration approximately parallel to a head-to-feet body axis and
including heart pulses and other variations mixed together; an
electronic circuit responsive to said accelerometer sensor circuit
and operable to execute a filter that delivers a varying blood flow
signal from said input signal at least when that axis of
acceleration sensitivity is approximately parallel to the
head-to-feet body axis, the varying blood flow signal substantially
freed of heart pulses; and an output circuit operable to send
information based on the varying blood flow signal.
28. The accelerometer sensor assembly claimed in claim 27 further
comprising a display coupled to said electronic circuit.
29. The accelerometer sensor assembly claimed in claim 27 wherein
said accelerometer sensor circuit has a second axis of acceleration
sensitivity substantially perpendicular to the broad side and
orientable on the chest to deliver a second input signal to said
electronic circuit representing a component of acceleration
approximately parallel to a dorsal-ventral direction.
30. The accelerometer sensor assembly claimed in claim 27 wherein
said accelerometer sensor circuit has another axis of acceleration
sensitivity substantially parallel to the broad side and
substantially perpendicular to the first-recited axis of
acceleration sensitivity, and the electronic circuit coupled to
receive another input signal from said accelerometer sensor circuit
representing that other axis of acceleration sensitivity.
31. The accelerometer sensor assembly claimed in claim 30 wherein
said electronic circuit is operable to combine signals from said
accelerometer sensor circuit representing at least both of those
axes of acceleration sensitivity.
32. The accelerometer sensor assembly claimed in claim 27 further
comprising an electrode for electrical skin contact and made
physically part of the assembly.
33. The accelerometer sensor assembly claimed in claim 27 wherein
said accelerometer sensor circuit includes at least two analog
signal conditioning paths.
34. The accelerometer sensor assembly claimed in claim 27 wherein
the varying blood flow signal has a spindle-shaped oscillatory
waveform during each heartbeat and oscillating at a frequency
higher than both a respiration frequency and a heart rate.
35. The accelerometer sensor assembly claimed in claim 27 wherein
said output circuit includes a modem coupled to said electronic
circuit.
36. The accelerometer sensor assembly claimed in claim 27 wherein
said output circuit is selected from the group consisting of: 1)
Bluetooth, 2) Zigbee, 3) serial interface.
37. The accelerometer sensor assembly claimed in claim 27 wherein
said electronic circuit is operable to provide a hemodynamic
parameter, a heart rate, and a respiration parameter, all based on
said accelerometer sensor circuit.
38. An electronic monitoring process comprising: electronically
bandpassing digital signals representing living-body monitoring
signals in a range selective for a cardiac signal; and executing a
filter that delivers a varying blood flow signal from the cardiac
signal.
39. The electronic monitoring process claimed in claim 38 further
comprising separating cardiac pulses from the cardiac signal and
delivering an electronic representation of the time interval
between a cardiac pulse and an event on the varying blood flow
signal.
40. The electronic monitoring process claimed in claim 38 further
comprising post-processing the varying blood flow signal as from a
2nd order system model.
41. The electronic monitoring process claimed in claim 38 further
comprising post-processing the varying blood flow signal and the
cardiac signal into electronically-represented displays of
physiological function.
42. A hemodynamic monitoring device comprising: an electronic
processor having at least one signal input for body monitoring; and
a memory holding instructions for said electronic processor coupled
to said electronic processor so that said electronic processor is
operable to obtain a cardiac pulse signal having a varying
amplitude, and said electronic processor further operable to
post-process the cardiac pulse signal to provide a time-varying
output representing an estimation for at least one hemodynamic
parameter based on the amplitude and selected from the group
consisting of stroke volume SV and cardiac output CO.
43. The hemodynamic monitoring device claimed in claim 42 wherein
said electronic processor is also operable to separate a
respiration signal also based on the amplitude from the cardiac
pulse signal.
44. The hemodynamic monitoring device claimed in claim 42 further
comprising a display coupled to said electronic processor to
display the at least one hemodynamic parameter.
Description
CROSS-REFERENCES TO RELATED APPLICATIONS
[0001] This application is related to U.S. patent applications as
follows:
[0002] This application is related to U.S. patent application:
"Heart Monitors And Processes With Accelerometer Motion Artifact
Cancellation, And Other Electronic Systems" Ser. No. 12/______
(TI-68518) filed Aug. 24, 2010 simultaneously herewith, for which
priority is claimed under 35 U.S.C. 120 and all other applicable
law, and which is incorporated herein by reference in its
entirety.
[0003] This application is related to U.S. patent application
"Motion/Activity, Heart-Rate And Respiration From A Single
Chest-Worn Sensor, Circuits, Devices, Processes And Systems" Ser.
No. 12/______ (TI-68552) filed Aug. 24, 2010 simultaneously
herewith, for which priority is claimed under 35 U.S.C. 120 and all
other applicable law, and which is incorporated herein by reference
in its entirety.
[0004] This application is related to provisional U.S. patent
application "Motion Artifact Cancellation to Obtain Heart Sounds
from a Single Chest-Worn Accelerometer" Ser. No. 61/242,688
(TI-68518PS) filed Sep. 15, 2009, for which priority is claimed
under 35 U.S.C. 119(e) and all other applicable law, and which is
incorporated herein by reference in its entirety.
[0005] This application is related to provisional U.S. patent
application "Motion/Activity, Heart-rate and Respiration From a
Single Chest-worn Sensor" Ser. No. 61/262,336 (TI-68552PS) filed
Nov. 18, 2009, for which priority is claimed under 35 U.S.C. 119(e)
and all other applicable law, and which is incorporated herein by
reference in its entirety.
[0006] This application is related to provisional U.S. patent
application "Estimation of Blood Flow and Hemodynamic Parameters
from a Single Chest-worn Sensor" Ser. No. 61/262,331 (TI-68553PS)
filed Nov. 18, 2009, for which priority is claimed under 35 U.S.C.
119(e) and all other applicable law, and which is incorporated
herein by reference in its entirety.
[0007] This application is related to provisional U.S. patent
application "Heart Rate Detection In High Noise Conditions" Ser.
No. 61/104,030 (TI-66732PS) filed Oct. 9, 2008, for which priority
is claimed under 35 U.S.C. 119(e) and all other applicable law, and
which is incorporated herein by reference in its entirety.
[0008] This application is related to U.S. patent application
Publication "Heart Rate Detection In High Noise Conditions"
20100094150, dated Apr. 15, 2010 (TI-66732) for which priority is
claimed under 35 U.S.C. 120 and all other applicable law, and which
is incorporated herein by reference in its entirety.
[0009] This application is related to provisional U.S. patent
application "Robust Heart Rate Detection in the Presence of
Pathological Conditions" Ser. No. 61/023,581, filed on Jan. 25,
2008 (TI-65798PS), for which priority is claimed under 35 U.S.C.
119(e) and all other applicable law, and which is incorporated
herein by reference in its entirety.
[0010] This application is related to U.S. patent application
Publication "Method and System for Heart Sound Identification"
20090192401, dated Jul. 30, 2009 (TI-65798) for which priority is
claimed under 35 U.S.C. 120 and all other applicable law, and which
is incorporated herein by reference in its entirety.
[0011] This application is related to U.S. patent application
Publication "Method and Apparatus for Heart Rate Monitoring" Ser.
No. 12/768,488 filed Apr. 27, 2010 (TI-67877), which is
incorporated herein by reference in its entirety.
[0012] This application is related to U.S. patent application
"Parameter Estimation for Accelerometers, Processes, Circuits,
Devices and Systems" Ser. No. 12/398,775 (TI-65353) filed Mar. 5,
2009, and which is incorporated herein by reference in its
entirety.
[0013] This application is related to the US patent application
titled "Processes for More Accurately Calibrating E-Compass for
Tilt Error, Circuits, and Systems" Ser. No. 12/398,696 (TI-65997)
filed Mar. 5, 2009, and which is incorporated herein by reference
in its entirety.
STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT
[0014] Not applicable.
COPYRIGHT NOTIFICATION
[0015] Portions of this patent application contain materials that
are subject to copyright protection. The copyright owner has no
objection to the facsimile reproduction by anyone of the patent
document, or the patent disclosure, as it appears in the United
States Patent and Trademark Office, but otherwise reserves all
copyright rights whatsoever.
FIELD OF TECHNOLOGY
[0016] The field of technology is in the areas of monitoring of the
human body, automatic analysis and display of monitoring data
locally for medical and other purposes and telecommunication
remotely for tele-medicine, and processes, circuits and devices for
body monitoring of heart function, circulatory function,
respiration, or other physiological processes. Biomedical
instrumentation and signal processing are further fields.
BACKGROUND
[0017] Ambulatory measurement of cardiac activity can facilitate
home health monitoring of older adults and of patients with a
history of cardiovascular conditions. Evaluating cardiovascular
performance of patients in ICU (intensive care unit) and hospital
settings, in mobile ambulances, and at accident and trauma sites
also involves or can involve ambulatory cardiac measurement.
[0018] Most current solutions for heart rate monitoring involve
cumbersome equipment, such as heart rate recording belts to be worn
around the chest, electrocardiogram (ECG) electrodes and leads, and
in most cases electrical contact to the skin. However, such methods
remain obtrusive, and are not optimal for long-term and ambulatory
monitoring.
[0019] An alternative method of heart rate measurement uses heart
sounds, conventionally measured with stethoscopes or
phonocardiograph.
[0020] Detection and early warning of risk factors for and any
incident of heart failure is vitally important in medicine, allied
medical fields, residential care-giving, exercise venues and other
settings. Heart failure can be caused by, and is at risk in case
of, coronary artery disease, hypertension, valve disorder, past
myocardial infarction, muscle disorder, congenital heart
conditions, etc.
[0021] Current solutions for not only heart rate monitoring but
also respiration monitoring are believed to involve cumbersome and
expensive equipment e.g., respiration and heart rate monitoring
belts to be worn around the chest, spirometers and canulas to be
worn around the mouth and nose, and electrocardiogram (ECG)
electrodes and leads to be taped on the body. Not only are these
solutions obtrusive and expensive, but may also be too restrictive
to be well-suited for ambulatory monitoring.
[0022] Noise mixed with signals received by the sensors used in
heart monitoring, respiration monitoring, body motion and other
monitoring applications can adversely affect the accuracy of each
type of signal. Accordingly, methods for robust detection and
separation of such signals in noisy conditions are desirable.
Accuracy of heart rate detection is important in many commercial
heart monitoring applications (e.g., heart rate monitors in
exercise equipment, personal heart rate monitors, etc.) and medical
heart monitoring applications (e.g., digital stethoscopes, mobile
cardiac monitoring devices, etc.).
[0023] Simpler, more economical and more efficient methods and
devices are desirable in the art for obtaining, isolating,
determining and monitoring resting data and ambulatory data, such
as robust, accurate detection of heart rate, timings of heart
sounds (S1 and S2) and pathological cardiac conditions, and robust
detection of respiration in connection with respiratory and
pulmonary disorders, as well as data on body motion and ambulatory
data and activity data.
[0024] Conventional approaches to address the bodily motion signal
separation and/or removal problem are believed to involve
multi-signal adaptive algorithms that need an additional motion
signal reference recording typically from a secondary sensor. Also,
the reference signal needs to be reasonably well correlated to the
motion picked up by the primary sensor. Such arrangements are very
difficult to establish in a real setting and can cause poor
rejection of the motion signal and body motion artifacts. Some
conventional single-channel de-noising techniques reinforce all
major signal peaks and fail to distinguish body motions from heart
sounds.
[0025] In addition to medical-related applications, solving the
above problems could also help monitor older adults for unexpected
changes in gait, for falls, for syncope (fainting), for accidents
and trauma incidents. Fitness monitoring at home, in exercise
venues, and in institutional care settings could also benefit.
[0026] Hemodynamic data also challenge the art to find methods and
devices for obtaining, isolating, determining and monitoring more
simply, economically and more efficiently. Hemodynamics as
discussed herein includes the study of blood flow-related data
directly or indirectly related to blood flow, such as: heart stroke
volume, cardiac output, pre-ejection period, contractility (ability
of heart to contract, inotropy), and related causal or caused
bodily dynamics such as exercise and exercise recovery, and the
Valsalva maneuver (such as when pushing or straining while holding
one's breath, or otherwise doing the maneuver in a medical
test).
[0027] Measurement of blood flow, hemodynamics and cardiovascular
performance is integral to a holistic assessment of an individual's
health. Specifically, patients with past conditions of heart
disease like heart failure (potentially arising out of one or more
of many causes like coronary artery disease, heart valve or heart
muscle disorders, past myocardial infarction, hypertension etc.)
may need constant monitoring in order to improve a person's quality
of life via timely and appropriate diagnostic interventions. While
the physiological mechanisms underlying these conditions are fairly
well understood, the technology to monitor these physiological
vitals needs considerable improvement.
[0028] Most current solutions for the measurement of blood flow and
other hemodynamic parameters are believed to involve cumbersome and
expensive equipment e.g., Impedance Cardiography (calls for
electrodes to be connected on the skin), Doppler Echo Cardiography,
Continuous Blood Pressure Monitoring etc. Not only are these
solutions obtrusive and expensive, but may also be too restrictive
to be well-suited for ambulatory monitoring applications.
SUMMARY OF THE INVENTION
[0029] Generally, and in one form of the invention, an electronic
monitoring device includes an electronic processor having at least
one signal input for body monitoring, and a memory holding
instructions for the electronic processor coupled to the electronic
processor so that the electronic processor is operable to isolate a
cardiac signal including cardiac pulses combined with other cardiac
signal variations, and the electronic processor further operable to
execute a filter that separates a varying blood flow signal from
the cardiac pulses and to output information based on at least the
varying blood flow signal.
[0030] Generally, and in another form of the invention, an
accelerometer sensor assembly has a broadside portion and includes
an accelerometer sensor circuit having an axis of acceleration
sensitivity parallel to the broad side and orientable on the chest
to deliver an input signal representing a component of acceleration
approximately parallel to a head-to-feet body axis and including
heart pulses and other variations mixed together, an electronic
circuit responsive to the accelerometer sensor circuit and operable
to execute a filter that delivers a varying blood flow signal from
the input signal at least when that axis of acceleration
sensitivity is approximately parallel to the head-to-feet body
axis, the varying blood flow signal substantially freed of heart
pulses, and an output circuit operable to send information based on
the varying blood flow signal.
[0031] Generally, and in a process form of the invention, an
electronic monitoring process includes electronically bandpassing
digital signals representing living-body monitoring signals in a
range selective for a cardiac signal, and executing a filter that
delivers a varying blood flow signal from the cardiac signal.
[0032] Generally, and in a further form of the invention, a
hemodynamic monitoring device includes an electronic processor
having at least one signal input for body monitoring, and a memory
holding instructions for the electronic processor coupled to the
electronic processor so that the electronic processor is operable
to obtain a cardiac pulse signal having a varying amplitude, and
the electronic processor further operable to post-process the
cardiac pulse signal to provide a time-varying output representing
an estimation for at least one hemodynamic parameter based on the
amplitude and selected from the group consisting of stroke volume
SV and cardiac output CO.
[0033] Other devices, sensor assemblies, electronic circuit units,
and processes are also disclosed and claimed.
BRIEF DESCRIPTION OF THE DRAWINGS
[0034] FIG. 1 is a partially-block, partially-pictorial, partially
graphical depiction of an inventive structure and process for
separating a heart signal from body motion and noise using a single
chest sensor.
[0035] FIG. 2 is a partially-schematic, partially-pictorial,
partially graphical depiction of a measurement setup including both
an accelerometer sensor on the chest with a composite signal having
heart signals and motion and noise, as well as an electrocardiogram
ECG circuit with ECG electrodes affixed to the body and showing an
operational amplifier and an ECG signal.
[0036] FIG. 3 is a block diagram of an inventive structure and
process for separating a heart signal from body motion and noise
using a single accelerometer chest sensor.
[0037] FIG. 4 is a flow diagram of the inventive structure and
process for separating a heart signal from body motion and noise
using a single accelerometer chest sensor and remarkable smoothing
filter and residue circuit, envelope-based noise rejection, folded
correlation and other steps.
[0038] FIG. 5 is a set of four concurrent waveform traces of
voltage versus time in various parts of the inventive structure and
process of FIGS. 2, 3 and 4 with a subject walking around a room. A
time interval portion of the traces is magnified and shown as four
time-magnified waveforms maintaining the same voltage scale for
each. Some of the traces are accelerometer-based and one is
ECG-based.
[0039] FIG. 6 is a pair of concurrent accelerometer-based waveform
traces of voltage versus time in parts of the inventive structure
and process of FIGS. 2, 3 and 4 with a subject walking on a
treadmill. A time interval portion of the traces is magnified and
shown as time-magnified waveforms maintaining about the same
voltage scale for each.
[0040] FIGS. 7A, 7B, and 7C are each a pair of concurrent noisy
handgrip ECG-based waveform traces of voltage versus time in parts
of the inventive structure and process of FIGS. 2 and 4 with a
subject walking on a treadmill. The ECG-based waveforms are one
unfiltered, and one inventively filtered to recover a heart sounds
signal.
[0041] FIG. 8 is a block diagram of another inventive structure and
process for obtaining cardiac information using an accelerometer
chest sensor, such as heart rate and information related to
beat-to-beat changes in stroke volume and cardiac output.
[0042] FIG. 9 is a pair of concurrent accelerometer-based waveform
traces of voltage versus time in parts of the inventive structure
and process of FIGS. 3 and 4 pertaining to envelope-based noise
rejection.
[0043] FIG. 10 is a pair of concurrent accelerometer-based waveform
traces of voltage versus time in parts of the inventive structure
and process of FIGS. 3 and 4 pertaining to folded correlation.
[0044] FIG. 11 is a plot of the difference of two heart-rate
measures (inventively filtered accelerometer-based and ECG-based)
versus their average.
[0045] FIG. 12 is a plot having time interval between adjacent S1
cardiac pulses from inventively filtered accelerometer data on one
graph axis, versus ECG R-R interval on the other graph axis.
[0046] FIG. 13 is a partially-block, partially-pictorial, partially
graphical depiction of an inventive structure and process for
separating a respiration signal from heart and body motion and
other signals using a single chest sensor.
[0047] FIG. 14 is a partially-flow, partially graphical depiction
of an inventive process for FIG. 13 separating a respiration
signal, a heart signal and a body motion signal from each other
using a single chest sensor.
[0048] FIG. 15 is a pair of concurrent accelerometer-based waveform
traces of voltage versus time in parts of the inventive structure
and process of FIGS. 13-14 and shows a raw and inventively filtered
accelerometer-based signal during rest and brisk motion. A time
portion of the signals during rest is magnified in both voltage
scale and time scale. A time portion of the signals during
subsequent motion is magnified in time scale and not voltage
scale.
[0049] FIG. 16 is a set of four concurrent waveform traces of
voltage versus time in various parts of the inventive structure and
process of FIGS. 13-14 during motion and brisk walking A time
interval portion of the traces is magnified and shown as four
time-magnified waveforms maintaining the same voltage scale for
each. Some of the traces are inventively filtered
accelerometer-based and one is ECG-based.
[0050] FIG. 17 is a flow diagram of a process for FIGS. 13-14 to
separate a respiration signal from a heart signal and using
inter-beat intervals of the heart signal, with both the respiration
signal and the heart signal substantially separated from body
motion and noise signals.
[0051] FIG. 18 is a set of three concurrent waveform traces of
voltage versus time in various parts of the inventive structure and
process of FIGS. 13-14, showing raw signal and inventively-obtained
residue from filtered accelerometer-based signal, and further
showing a respiration signal generated from ECG--R-R interval, and
accelerometer heart sounds S1-S1 interval of the residue signal. A
time interval portion of the traces is magnified and shown as three
time-magnified waveforms maintaining the same voltage scale for the
first two, and magnifying the voltage scale for the respiration
signal.
[0052] FIG. 19 is a flow diagram of another process for FIGS. 13-14
to separate a respiration signal from a heart signal according to
baseline wander method herein for respiration monitoring by a
single inventively filtered accelerometer sensor.
[0053] FIG. 20 is a set of four example waveforms of voltage versus
time and shown for comparison of a respiration belt signal with
respiration outputs from each of the processes of FIGS. 17, 19 and
22.
[0054] FIG. 21 is a set of seven example waveforms of voltage
versus time, including a comparison of a reference respiration
signal and ECG-derived respiration signal with respiration outputs
from each of the processes of FIGS. 17, 19 and 22.
[0055] FIG. 22 is a flow diagram of a process for FIG. 13 to
inventively separate a respiration signal from a heart signal by
amplitude modulation detection of peak heights of the heart
signal.
[0056] FIG. 23 is a set of three concurrent waveform traces of
voltage versus time in various parts of the structure and process
of FIGS. 2 and 13 and 22, showing ECG amplitude modulation on the R
peaks, and further showing amplitude modulation on the S1 peaks
from inventively filtered accelerometer based sensing, and also
showing a respiration signal obtained from a respiration belt for
reference.
[0057] FIG. 24 is a block diagram of an inventive wired system
structure and process and including inventive structures and
processes from the other Figures.
[0058] FIG. 25 is a block diagram of an inventive wireless system
structure and process and including inventive structures and
processes from the other Figures.
[0059] FIG. 26 is a partially-block, partially-pictorial, partially
graphical depiction of an inventive structure and process for
separating a blood flow signal from heart and other signals using
sensor signals from one or more axes of a single chest sensor.
[0060] FIG. 27 is a pair of concurrent accelerometer-based waveform
traces of voltage versus time of sensor signals from multiples axes
of a single chest sensor in the inventive structures and processes
of FIG. 26.
[0061] FIG. 28 is a voltage-versus-time graph of a pair of
concurrent accelerometer-based waveforms from Z- and Y-axes of the
single chest sensor, along with inventively filtered Y-axis signal
and residue in the inventive structures and processes of FIG.
26.
[0062] FIG. 29 is a voltage-versus-time graph of three concurrent
waveforms including a pair of inventively filtered
accelerometer-based waveforms from Z- and Y-axes of the single
chest sensor in the inventive structures and processes of FIGS. 26,
30 and 31, compared with a reference ECG waveform.
[0063] FIG. 30 is a partially-block, partially-pictorial, partially
graphical depiction of another inventive structure and process for
separating a blood flow signal and hemodynamic parameters,
respiration signals, heart signals and motion signals from each
other using sensor signals from one or more axes of a single chest
accelerometer sensor.
[0064] FIG. 31 is a combined flow diagram of inventive processes
for separating a heart signal from body motion and noise using
Z-axis sensor input and separating a blood flow signal using Y-axis
sensor input from FIG. 30 and further electronically processing the
heart signal and blood flow signal jointly to generate hemodynamic
parameter signals for a display as in FIGS. 24 and 25.
[0065] FIG. 32 is a voltage-versus-time graph of three concurrent
waveforms including an ECG signal, a filtered heart signal from the
Z-axis accelerometer sensor, and a blood flow signal filtered from
Y-axis of the accelerometer sensor in the inventive structures and
processes of FIGS. 26, 30 and 31, and further showing time
locations P1 and F1 and hemodynamic parameters for isovolumic
contraction interval IVCI, pre-ejection period PEP and flow peak
amplitudes PAmp and Jamp.
[0066] FIG. 33 is a graph of voltage (arbitrary units) versus
time-samples for a multitude (ensemble) of waveforms each of a
respective instance of inventively filtered blood flow signal from
Y-axis of the accelerometer sensor in the inventive structures and
processes of FIGS. 26 and 30. (FIG. 33 is on same sheet as FIG.
37.)
[0067] FIG. 34 is a voltage-versus-time graph of four concurrent
waveforms during exercise recovery, the waveforms including
reference ECG, inventively filtered blood flow signal from Y-axis,
PEP, and PAmp. A time interval portion of the traces is magnified
and shown as four time-magnified waveforms maintaining the same
voltage scale for each except for modest voltage scale
magnification for PEP and PAmp.
[0068] FIG. 35A is a voltage-versus-time graph of four concurrent
waveforms over about a minute for a Valsalva Release phase of a
Valsalva maneuver; the first waveform representing
inventively-produced residue from polynomial filtering the
accelerometer Z-axis as in FIG. 31 (left side), the second waveform
representing peak amplitude PAmp of that residue, the third
waveform representing Stroke Volume, and the fourth waveform
representing Cardiac Output.
[0069] FIG. 35B is a voltage-versus-time graph of another four
concurrent waveforms over about a minute for a Valsalva Release
phase of a Valsalva maneuver; the first waveform representing the
blood flow signal from inventive polynomial filtering of the
accelerometer Y-axis as in FIG. 31 (right side), the second
waveform representing peak amplitude PAmp of that blood flow signal
from accelerometer Y-axis, the third waveform representing Stroke
Volume, and the fourth waveform representing Cardiac Output.
[0070] FIG. 36A is a flow diagram of inventive process for
separating a heart signal from body motion and noise using Z-axis
sensor input such as for use in FIG. 31 or with FIG. 36B.
[0071] FIG. 36B is a flow diagram of inventive process for
separating a heart signal as well as a blood signal from each other
using Y-axis sensor input such as for use in FIG. 31 and for
obtaining further hemodynamic data and other information from the
single Y-axis of the chest sensor.
[0072] FIG. 37 is a model of a standing subject, the model
described by a second-order differential equation to approximate
the blood flow signal of the standing subject as a solution
thereof.
[0073] FIG. 38 is a model of a subject lying prone, the model
described by a second-order differential equation having different
model parameters than in FIG. 37, to approximate the blood flow
signal of the prone subject as a solution thereof.
[0074] FIG. 39 is a block diagram of a system structure for use in
and improved according to inventive structures and processes from
the other Figures.
[0075] FIGS. 40A and 40B are respective broadside and
cross-sectional views of an inventive accelerometer sensor and
transponder chip mounted on a support plate affixed by an adhesive
tape to the chest, and for use with the inventive structures and
processes from the other Figures.
[0076] FIG. 41 is a block diagram of an inventive structure and
process for variably combining accelerometer signals from multiple
axes in various proportions to provide one or more inputs to the
smoothing filtering of FIGS. 31, 36A and 36B and various other
Figures herein.
[0077] FIG. 42 is a voltage-versus-time graph of four concurrent
waveforms including reference ECG, acceleration along a
dorso-ventral axis (Z-axis), acceleration along a superior-inferior
axis (Y-axis) and acceleration along a dextro-sinistral axis
(X-axis), the various acceleration signals for use in the circuit
FIG. 41 and circuits of other Figures.
[0078] Corresponding numerals in different Figures indicate
corresponding parts except where the context indicates otherwise. A
minor variation in capitalization or punctuation for the same thing
does not necessarily indicate a different thing. A suffix .i or .j
refers to any of several numerically suffixed elements having the
same prefix. A first, second, third, etc. waveform is referenced in
top to bottom order for a given Figure.
DETAILED DESCRIPTION OF EMBODIMENTS
[0079] Some structure and process embodiments provide motion
artifact cancellation or motion signal separation to obtain heart
sounds from a single chest-worn accelerometer.
[0080] Miniature, high-sensitivity MEMS accelerometers are
presently available. Here, such an accelerometer is incorporated
into a single, chest-worn sensor for recording of signals including
some related to heart sounds. (The latter signal components are
also themselves sometimes called heart sounds herein. The term
"heart sound" refers in an expansive way to a signal analogous to
cardiac S1, S2, and/or heart murmur or other cardiac waveform
features, obtained from the processing of accelerometer data or
other sensor data, and not necessarily to an audible sound.)
[0081] However, a major challenge of ambulatory monitoring is the
corruption of heart signals by body motion artifact signals and the
confusion of such signals. In some measurements, the chest
acceleration signal as picked up by the accelerometer 10 in FIGS.
1-3 had a rather slow varying, but very strong (20-50 my
peak-to-peak) motion component. Riding on top of this motion
signal, was a higher frequency, but weaker (5-10 my peak-to-peak)
heart sound signal. Significant variability between subjects was
observed in the frequency content of both the motion and the heart
sounds. Also, the two signals--motion and heart sounds--are not
entirely frequency separable. Thus, simple digital band pass
filtering does not consistently work to separate them. Physical
motion impulses from the feet couple very differently and in a
non-stationary and non-correlated manner to sensors placed at
different parts of the body and also to orthogonal axes of the same
sensor. Accordingly, even using multiple sensors to cancel out an
artifact is complicated or unreliable.
[0082] Some of the embodiments remarkably introduce a Data
Acquisition/Signal Processing unit 20 with a special smoothing
filter 130 in FIGS. 3-4 that tracks slow varying body motion signal
wander or variation and then removes the wander from the
sensor-based signal to give a clean (motion-removed) biomedical
signal of interest on a Display unit 30. The smoothing filter 130
involves a polynomial filter or comparably effective smoothing
filter used directly or in a composite signal processing path. Some
embodiments use a subtraction step 140 as in FIG. 4 to remove
non-stationary motion artifacts reliably and robustly. Removing
such artifacts makes the system more fully immune to sensor
placement and contact variations on the chest that might arise when
using sensor 10. This provides a simple, yet effective way to
reduce the impact of motion artifacts and allow the reliable
detection of primary heart sounds and subsequent derivation of
heart rate even when a person is walking while being monitored. In
this way, motion signal removal or separation, and heart-sound
signal detection and heart-rate detection are facilitated. No
secondary reference or noise source is needed, thus reducing
complexity of system design. Embodiments of structure and method
thus extract primary heart sound signals from chest-worn sensor
(e.g., accelerometer) data in the presence of motion artifacts.
[0083] Results from six subjects showed a primary heart signal
detection rate of 99.36% with a false positive rate of 1.3% as
described elsewhere herein (TABLE 2). Such type of embodiment
appears to outperform noise removal techniques such as wavelet
de-noising and adaptive filtering. (In certain motion conditions,
or in combination, alternative approaches like Wavelet
Decomposition, Adaptive Filtering, Blind Source Separation may in
some embodiments also be used instead of, separately from, parallel
to, or in combination with, the polynomial filtering.)
[0084] Advantages include: 1) uses as few as a single sensor or
signal capture component, 2) eliminates use of a secondary
reference sensor, 3) allows unobtrusive and non-invasive monitoring
of vital biomedical signals in ambulatory settings for continuous
monitoring applications, 4) separates heart signals independent of
non-stationary bodily motion wander.
[0085] For biomedical instrumentation and signal processing for
heart sounds specifically, problematic motion artifacts are thus
removed from biomedical signals--such as from chest accelerometer
signals and/or from electrocardiogram (ECG) signals--for use in
ambulatory health monitoring settings. The embodiments can also be
extended by use of a spectrum analyzer (Fourier analysis) to
extract frequency separable components of interest too.
[0086] Ambulatory monitoring of cardiac activity can find
widespread applications in home health monitoring of patients with
a history of cardiovascular conditions, monitoring older adults,
ICU and hospital monitoring, monitoring vital signs in mobile
ambulances, at accident and trauma sites and can be used for
fitness monitoring at exercise centers and elsewhere.
[0087] In some structure and process embodiments for removal of
motion-related artifacts from biomedical signals, beneficial
monitoring is provided for, e.g., either or both of two independent
signal sources--accelerometer 10 and ECG of FIG. 2. Chest
acceleration signals are collected in an ambulatory (walking)
setting from real human subjects using a chest-worn accelerometer
10--providing primary heart sounds signified as S1 and S2. Heart
sound S1 includes audible sounds concurrent with tricuspid and
mitral valve activity and shows on a seismocardiogram as a pulse
bundle. Heart sound S2 is mainly associated with pulmonary valve
and aortic valve activity. Structures and processes of the
embodiments thus remove motion artifacts and facilitate the use of
a single, miniature, chest-worn MEMS accelerometer to pick up heart
activity and heart rate--derived from heart sounds--from ambulatory
subjects as shown in FIGS. 1 and 2. In FIG. 2, electrocardiogram
signals are independently collected from the human subjects walking
briskly or running on a treadmill--providing signal components such
as QRS from the ECG.
[0088] Some background heart anatomy terms are as follows.
De-oxygenated blood enters right atrium of heart via inferior vena
cava and superior vena cava from systemic veins. The right
ventricle of heart receives de-oxygenated blood from right atrium
and pumps it via the pulmonary artery to the lungs where carbon
dioxide is released and oxygen is received into the blood. The
blood moves from the lungs via the pulmonary vein to the left
atrium of the heart. Valves open and close at the entry to,
between, and exit from, the atria and ventricles. The left atrium
passes oxygenated blood to the left ventricle, which pumps the
oxygenated blood out the large artery called the aorta. The aorta
connects by systemic arteries to cerebral, coronary, renal,
visceral (splanchnic), and skin vasculatures and to vasculature of
skeletal muscles. The names of the valves are: tricuspid
valve--right atrium to right ventricle; pulmonary valve--right
ventricle to pulmonary artery; mitral valve--left atrium to left
ventricle; and aortic valve--left ventricle to aorta.
[0089] The primary heart sound components, S1 and S2, are composite
signals generated by valve closures. S1 is caused by the closure of
the mitral and tricuspid values of the heart, and S2 is caused by
the closing of the aortic and pulmonary valves. An analog
electrical heart monitoring signal is captured by two or more ECG
electrodes, and the signal is a varying voltage representing
electrical activity of the heart, i.e., the signal generated in a
person's body to cause the heart to contract or relax. The ECG
signal has three main components, a P-wave, a QRS complex made up
of a Q-wave, an R-wave, and an S-wave, and a T-wave. The pulses
include a small positive P pulse, a larger negative-going QRS
depolarization pulse near in time to the S1 heart sound, and a
large positive-going T pulse near in time to the S2 heart sound.
The P-wave represents the depolarization (electrical activation) of
the atria of the heart. The QRS complex represents the ventricular
activity of the heart. The T-wave represents the re-polarization of
the ventricles.
[0090] Process and structure embodiments can also be extended to
other biomedical signals corrupted by motion wander--e.g., ECG
electrocardiogram, PPG--photoplethysmogram (signal from a Pulse
Oximeter), EEG--electroencephalogram, EMG--electromyogram,
ICG--Impedance Cardiogram signals--or almost any other signal that
might be affected by a separable wander. Thus, motion-related
artifacts are removed from such other biomedical signals in
products that can be produced by a manufacturer in volume.
[0091] Remarkably, with some of the embodiments of structure and
process, polynomial smoothing and differentiating functions and
operations are performed. A secondary reference sensor or signal
source is unnecessary. Gross motion is tracked and canceled out
from the primary accelerometer-based signal. A polynomial smoothing
filter 130 (for example, a Savitzky-Golay filter) is electronically
instantiated herein and digitally smoothes a given
accelerometer-based data signal stream by approximating it within a
specified data window by a polynomial of a specified order that
best matches the data in the window in a least-squares sense. Here,
the electronic smoothing filter 130 fits the slower variations in
body-motion-induced components of the biomedical sensor-based
signal and subtracts them as smoothed content from the biomedical
sensor-based signal to leave behind what is called a residue
signal. The residue signal provides a thus-extracted,
faster-varying signal--primarily the heart sounds and other cardiac
activity, as well as some residual or remaining noise.
[0092] Such polynomial filtering 130 preserves higher order moments
around inflection points, or at extrema like peaks and troughs,
that a digital moving average or low-pass filter does not. In other
words, the polynomial filtering better preserves features--like
local maxima and minima--through a least-squares polynomial fit
around each point. Also, unlike a moving average, in estimating the
value of the fit at a certain point, it does not factor in the
values on the polynomial fit around it, therefore not introducing a
bias at such features while reducing the noise.
[0093] In FIG. 1, a system embodiment has hardware that provides a
measurement set-up and monitoring embodiment. A miniature
(weight--0.08 gram, size--5.times.5.times.1.6 mm) triple axis,
low-power, analog output MEMS accelerometer (LIS3L02AL,
STMicroelectronics, Geneva, Switzerland) is taped onto the chest
(e.g., a few inches to the left of the sternum along the third or
fourth rib). (Taping the accelerometer sensor or using a chest band
presses the accelerometer sensor to or against a bare or shaved
portion of the chest and efficiently couples chest acceleration to
the sensor.) An acceleration signal corresponding to the cardiac
activity is captured along the Z-axis--the dorso-ventral direction
orthogonal to the plane of the chest. The chest acceleration signal
is AC coupled with a 3 Hz cut-off and amplified with a gain of 100
and low pass filtered--for anti-aliasing--through a three-stage,
5-pole Sallen-and-Key Butterworth filter with a 1 kHz corner
frequency. A commercial quad operational amplifier (op amp) package
(LT1014CN, Linear Technology, Milpitas, Calif.) is used for the
analog front-end. The accelerometer signal is then sampled at
10,000 Samples/sec using a data acquisition card (National
Instruments, Austin, Tex.) and captured and stored on a computer
using MATLAB software (Version 2007b, The Mathworks, Natick,
Mass.).
[0094] The AC coupling with approximately 3 Hz cutoff, which is a
non-critical rolloff frequency, is provided, for example, by a
series coupling capacitor C coupled to an input resistance
established for the amplifier.
[0095] In FIG. 2, a reference ECG (lead II) is acquired
simultaneously in a three electrode (single lead) electrocardiogram
ECG amplifier configuration as a standard of reference in order to
compare with the accelerometer-derived cardiac signal for the
evaluation of the performances of the heart rate extraction from
the accelerometer signal.
[0096] In FIGS. 3 and 4, for detection of primary heart sounds and
cardiac activity, the acceleration signal is digitally low pass
filtered in a step 110 at 50 Hz--using a 3326 tap digital FIR
filter with a steep 80 dB roll-off over 20 Hz--and decimated in a
step 120 by a factor of 10. (Rolloff frequency less than 60 Hertz
attenuates 60 cycle USA power line interference with biomedical
signals of interest, and rolloff may be made less than 50 Hertz for
applicable countries using 50 Hertz. While the rolloff frequency
could be made higher, this FIR filter also desirably attenuates
white noise above the frequency range of the signals being
monitored.) Also in a Phase 1, a high order Savitzky-Golay
polynomial smoothing filter 130, using 28th order and 401 point
frame, is used to capture the relatively slow-varying motion wander
and leave out the more rapidly varying heart sound signal
components. (Matlab syntax for such filter is
g=sgolayfilt(X,28,401) where g is the filter output and X is a
latest input column vector of 401 sample values of windowed data.)
In a Phase 2, the smoothing filter 130 output is subtracted in a
step 140 from the decimated LPF output to obtain heart sounds S1
and S2. A folded correlation process in a step 160 then enhances
and strengthens the polynomial filtered S1/S2 peaks in the
motion-removed acceleration signal. Such folded correlation process
160 is described in further detail elsewhere herein and with
background in U.S. patent application Publication "Heart Rate
Detection In High Noise Conditions" 20100094150, dated Apr. 15,
2010 (TI-66732), which is incorporated herein by reference. Then
the location of the peaks is threshold-detected in a step 170 using
an electronic amplitude-based peak picking process, and the
selected peaks are counted in a step 180 to calculate heart rate
HR.
[0097] In FIG. 5, a chest-acceleration signal is derived from the
accelerometer sensor while a subject is walking around a room and
low pass filtered at 50 Hz (step 110) as shown in a first waveform.
LPFing (low pass filtering) sub-50 Hz is used in some of the
examples because most of the desired signal power lies in that
range and in general LPFing with some rolloff frequency below about
one hundred Hertz in many of the embodiments avoids making the
bandwidth so wide as to encompass and integrate a substantial or
undue amount of sensor noise (thermal, white spectrum). In case LPF
with a rolloff frequency above power-line frequency is used, then
some embodiments also include notch-filtering for power-line
frequency rejection. In FIG. 5, a second waveform is an
electronically-derived polynomial smoothing filter 130 output
corresponding primarily to the body motion. A third waveform
concurrently shows the residue signal after subtraction 140 in FIG.
4 and isolates the primary heart sounds. A simultaneous ECG timing
signal is shown as a fourth concurrent waveform for reference.
[0098] In FIG. 6, the same embodiment monitors a chest-acceleration
signal from the accelerometer Z-axis sensor while the subject walks
on a treadmill. A brief rest recording is followed by motion.
Compared to FIG. 5, the plot of FIG. 6 analogously shows an
unfiltered (raw) acceleration signal and the residue from step 140
after the polynomial smoothing of step 130. Note the magnified
scale in some parts of FIG. 6, and that FIG. 6 has a different
scale than in FIG. 5.
[0099] FIGS. 7A-7C show signal plots for an ECG filtering
embodiment 2. The plots have different time scales and walking
conditions. Raw ECG signal from the ECG electrodes in FIG. 2 and a
concurrent filtered ECG signal waveform, by applying steps 110-140
separately to the ECG signal, are depicted for a subject walking on
a treadmill.
[0100] In another embodiment, satisfactory S1-S2 heart signals were
extracted from raw motion-affected accelerometer Z-axis data by LPF
(low pass filtering) with corner at 100 Hz and then Savitzky-Golay
filtering at 20.sup.th order, followed by subtraction of the S-G
signal from the LPF signal, and followed further by signal
enhancement. It appears that polynomial filtering of
motion-affected LPF accelerometer signals, using polynomial
filtering on the order in a range of approximately 20.sup.th order
or higher order to at least over 30.sup.th order, is satisfactory
for obtaining heart signals as a residue by subtraction of the
polynomial filtering output from the LPF signals. Using polynomial
fits at such orders successfully captures both coarser and finer
motion effects. The smoothing filter in some embodiments can be
lower order as well, and may obtain good results even with a
1.sup.st-order polynomial in case of some window sizes and
applications. Also, lower order polynomial filtering is
contemplated and found useful as discussed later hereinbelow. Using
a number of points at least approximately half again (1.5 or more
times) an order of the polynomial and even substantially higher
than that, in some of the embodiments, is believed to help to
reduce noise.
[0101] In FIG. 8, a wireless embodiment has the accelerometer
sensor 210 in a chest-worn miniature unit including Bluetooth or
pico-network wireless or an RF transponder. The miniature unit 210
wirelessly communicates with a Data acquisition/signal processing
unit 215, 220 of FIGS. 8 and 4 such as provided on a belt clip, in
a cell phone or in a gateway elsewhere in a residence (see FIG.
39). In FIG. 8, the signal processing unit 220 is coupled to a
wireless modem 230 or transmitter (or to a wireline modem) for
transmission to a remote location such as a medical clinic. The
medical clinic has a receiver or transceiver such as in a cell
phone or wireless or wireline modem 240, and further has a data
storage and display unit 250. The medical clinic can interrogate
the residential data acquisition/signal processing unit 220 by
transceiver 240 via residence-based modem 230 and re-configure the
residential unit 215, 220 for various performances and for more or
less information and more or less frequent communications.
[0102] In FIGS. 3 and 8, any two or more, or all, of the described
components can combined in a single digital system. The monitoring
signal capture component 210 is configured to capture a heart
monitoring signal from a person and provide it to an analog signal
conditioning and sampling section 215 (A-to-D) that feeds digital
data to the signal processing component 220. In some forms, the
A-to-D happens physically within the accelerometer chip and the
signal flow remains electronically arranged as shown. Either or
both of components 210 and 215, 220 may provide amplification and
noise reduction of the analog and/or digital signal in the process.
In various embodiments, the heart monitoring signal may be provided
to the processing component 220 in real-time, and/or may be
provided periodically as the signal is being captured, and/or may
be recorded and provided to the processing component at a later
time.
[0103] The digital heart monitoring signal may be provided to the
data acquisition 215 and signal processing unit 220 by wired or
wireless forms of communication, e.g., wired using a USB port,
electrode wires, logic circuitry, etc. or wirelessly such as by a
Bluetooth connection, Zigbee, or otherwise. In FIG. 8 a
communications network for remote transmission can be wide area
network (WAN) such as the Internet, a wireless network, a local
area network (LAN), or a combination of networks. Similarly, the
processing component 220 may be connected to an output component
250 by any of the foregoing connections and networks. Any suitable
display device and/or recording apparatus 250 is used such as, for
example, a computer monitor, a display of a handheld computing
device, a display in a personal heart rate monitoring device, a
display in a piece of exercise equipment, etc. The system hardware
of FIG. 39 may be applied with one program at both the premises at
which the accelerometer is used and a replica of that system
hardware applied with that program and/or an additional program at
the remote premises such as a medical clinic.
[0104] The system components including signal processing component
220 may also be implemented by or as part of any suitable digital
system (e.g., a general purpose processor, a digital processor, a
personal heart rate monitoring system, a heart rate monitoring
system in a piece of exercise equipment, a personal computer, a
laptop computer, a server, a mainframe, a personal digital
assistant, a television, a cellular telephone, an iPod, an MP3
player, etc.) configured to receive the digital heart monitoring
signal from the monitoring signal capture component 210. The
processing component 220 is configured to process the digital heart
monitoring samples in the digital heart monitoring signal in
accordance with embodiments of methods described herein. In one or
more embodiments of the invention, the processing component 220
includes functionality, e.g., a computer readable medium such as
memory, a flash memory, an optical storage device, a disk drive,
flash drive, etc., to store executable instructions implementing an
embodiment of a method for processing heart monitoring samples as
described herein and to execute those instructions.
[0105] Embodiments like those of FIGS. 8 and 4 and other Figures
herein have potentially wide-ranging applications from commercial
products that already have in-built accelerometers (e.g., mobile
phones, personal entertainment devices, content players, computer
game controllers etc.) and those that do not (clothing, accessories
etc.) to fitness products (heart straps, belts, wearable adhesive
bandages or sensor tapes, clips, straps, bands or carriers for
temporary affixing to one's chest, arm or elsewhere on the body, or
implantable sensor devices) Embodiments are suitably made as a part
or whole of ambulatory monitoring products for ambulances, at
trauma sites (e.g., for accident or burn victims), for
home-monitoring of older adults and all populations to which the
advantages of the embodiments commend themselves.
[0106] The accelerometer 210 signals from all three axes are
suitably also processed to electronically double-integrate the
acceleration to determine the location of the person wearing it.
Since the person is likely to have been in bed overnight, the
processing determines the location of the person during the day by
double-integrating the acceleration starting from initial
conditions of position initially at the bed location, and zero
initial vector velocity. This information can be helpful as a cue
to the person who is visually impaired, to care-giver, and to a
family member. The accelerometer processing can indicate that the
person is in a given room of the residence, as an assist for one
who is visually impaired, or can indicate that the person is
leaving or has left the residence to inform a care-giver or family
member. In this way, the accelerometer and associated processor
provide numerous services for all concerned, in various ways as
taught herein.
[0107] For background on accelerometer calibration and
double-integration see U.S. patent application "Parameter
Estimation for Accelerometers, Processes, Circuits, Devices and
Systems" Ser. No. 12/398,775 (TI-65353) filed Mar. 5, 2009, which
is incorporated herein by reference in its entirety.
[0108] Due to its low-cost and ease of use, products using the
embodiments have potential for commercial success not only in urban
and developed areas but also widely in the developing world as well
as in rural parts of the developed world or in any place where
low-cost, remote health monitoring facilities may be rare, if
available at all.
[0109] The smoothing filter 130 of FIG. 4 is configured based on a
specified order M and frame size (number of sample points N). For
instance, a Savitzky-Golay polynomial smoothing filter is used in
some embodiments to best approximate the acceleration signal in the
least-squares sense to capture the motion-dependent
baseline-wander. In some embodiments, the smoothing filter is
implemented in flash memory of a local processor of FIG. 39 such as
a belt-worn unit or provided in a home network gateway or clinic
office network gateway, or cell phone or otherwise.
[0110] The matter of selecting and or finding feasible and optimum
values for order M and window length (N.sub.W in points, t.sub.W in
time) for the polynomial smoothing filtering is discussed next. In
general for a fixed window length, N.sub.W, a higher order
polynomial will fit the high frequency components of the streaming
data better. For a given order M, a shorter window of time will
allow fitting the high frequency component better.
[0111] In FIG. 4, the working hypothesis is that the accelerometer
signal has a low-frequency (motion) component and a high frequency
(heart signal) component. The polynomial filter is used to fit to
the motion component.
[0112] A way to approach the optimization problem estimates the
inherent order of the low-frequency component and picks the
smallest window that satisfies the condition that N.sub.W>M+1
and N.sub.W is odd (i.e., N.sub.W=2N+1). The smaller window size
N.sub.W is, the smaller is the number of taps of the
multiply-accumulate filter process implementing the smoothing
filtering. For an accelerometer signal in some applications, order
M=1 and window size N.sub.W=3 (sampling frequency is 1000 Hz). In
some examples herein, higher orders M and window widths N.sub.W are
shown.
[0113] In FIG. 4, motion, heart sounds and heart rate are
electronically separated and ascertained from accelerometer 210
data using the following steps:
a) Low-pass filtering 110 and decimating 120 the accelerometer data
b) Savitzky-Golay filtering 130 to fit the relatively lower
frequency motion data c) Subtracting 140 the output of the
Savitzky-Golay filter from the low-pass filtered accelerometer data
(from step a) to obtain the heart sounds d) Performing 160 folded
correlation to enhance the primary heart sounds (S1 and S2) peak
locations e) Peak picking 170 to count the number of S1 peaks in a
predetermined or configured segment (time interval) and counting
180 the heart rate HR in beats per minute BPM.
[0114] Note that the term `decimation` refers to any process of
regularly removing samples from a sample stream, or passing one
sample in every n.sub.D samples as decimation parameter, and can
but does not necessarily refer to removing all but 1 sample in ten.
Thus, if a sample/ADC delivers f.sub.S samples per second, then a
decimation process delivers a decimation frequency substantially
f.sub.S/n.sub.D samples per second. If a window period is t.sub.W
seconds, then the number of points N.sub.W=2N+1 in the window is
N.sub.W=1+f.sub.S t.sub.W/n.sub.D. The window period t.sub.W may be
selected by considering the time period over which the particular
features and behavior of interest are to be obtained by the
filtering from the signal. The sampling frequency f.sub.S may be
selected with cost, physical size and complexity of anti-aliasing
in mind (low pass filter AAF at 0.5f.sub.S or less situated ahead
of sampling f.sub.S). The sampling frequency f.sub.S may be set
substantially greater than the Nyquist frequency for sampling the
AAF output. The decimation parameter n.sub.D is then selected,
firstly, to yield a decimation frequency f.sub.S/n.sub.D that is
sufficiently high relative to the e.g., 50 Hz low pass filter LPF
following the sampling/ADC circuit to provide effective operation
of that LPF. Secondly, the decimation parameter n.sub.D is also
selected to yield a number N.sub.W of window points that is
sufficiently high relative to the selected order M of the filter to
keep filter noise low while having the N.sub.W window points being
sufficiently low in number as to introduce only so many filter
computations as needed to achieve satisfactory filtering of the
signal stream in the window. The filter computations are related to
the product of the number N.sub.W of points per window multiplied
by a rate number r.sub.W of windows processed per second. If
r.sub.W=N.sub.W/t.sub.W, the computations are proportional to
N.sub.W.sup.2/t.sub.W, which may motivate fewer window points and
longer window times in some energy-saving and lower cost processor
applications. Remarkably, the examples herein satisfy these
considerations for some applications and other examples may readily
be devised for other particular applications as well.
[0115] Mathematically expressed processes are described in further
detail below for preparing various electronic embodiments with
smoothing filters for various ways of motion extraction in step 130
and any other purpose to which their advantages commend their use.
They are appropriately partitioned into offline and real-time
online electronic processes in such embodiments.
[0116] The notation .parallel.(x-Ab).parallel. in Equation (1)
signifies the sum of squared differences between the
[(2N+1).times.1] respective data stream vector sample points or
stream components and the (2N+1) respective estimates of those
stream components provided by multiplying a [(2N+1).times.M]
transform matrix A times a [M.times.1] vector of transform
coefficients b.sub.j. The number of transform coefficients b.sub.j
is M, and they form a [M.times.1] vector b. A gradient V is the
[M.times.1] vector of first partial derivatives with respect to the
transform coefficients b.sub.j. The number M of coefficients
b.sub.j is called the order, and if the number of transform
coefficients b.sub.j is M, then the order of the process is M. The
[M.times.M] matrix of second partial derivatives with respect to
the transform coefficients b.sub.j is signified by
.gradient..gradient.. The filter procedure involves, and in effect
forms, a coefficients change coefficient vector .DELTA.b for
updating an initial transform coefficient estimate b=0 (i.e., all
coefficients initialized to zero). This procedure pre-multiplies
the matrix of second partial derivatives times the negative of the
gradient to obtain that transform coefficients change vector
.DELTA.b.
.DELTA.b=-(.gradient..gradient..parallel.(X-Ab).parallel.).sup.-1.gradie-
nt..parallel.(X-Ab).parallel. (1)
[0117] Since the Equation (1) involves a quadratic expression and
starts from b=0, the process directly finds the values of the
transform coefficients b=.DELTA.b in one pass without iterating
additionally. Equation (2) represents the result of performing the
calculus operations represented by Equation (1). (Some embodiments
transmit the coefficients b from Equation (2) to a remote site for
record storage and further analysis, since they effectively
compress much of the information in the data window. If
coefficients are to be transmitted, the [M.times.(2N+1)] matrix
(A.sup.TA).sup.-1 A.sup.T is pre-computed and then multiplied by
each data window locally on the fly. Other embodiments omit such
compression and/or transmission, or only do it locally on remote
command, and thereby save some power and processing
complexity.)
b=(A.sup.TA).sup.-1A.sup.TX (2)
[0118] This process generally finds transform coefficients b.sub.j
provided the inverse (A.sup.TA).sup.-1 exists. That inverse exists
when the rows of the matrix A are linearly independent (full rank)
and enough data points N.sub.W=(2N+1) are provided so that the
corresponding number of columns of the matrix is sufficient for an
inverse to be delivered.
[0119] In the special case of a polynomial transform process, a
matrix of indices is raised to powers, wherein the j.sup.th column
element A.sub.nj in the nth row of transform matrix A is raised to
a power: n.sup.j. In other words, for the 2N+1 different values of
n from -N to +N in the window of a data stream X(i+n), the
transform finds a set of coefficients b.sub.j for a well-fitting
power series to approximate all the values. Such a power series in
general is represented by Equation (3):
X'(i+n)=b.sub.o+b.sub.1n+b.sub.2n.sup.2+b.sub.3n.sup.3+b.sub.4n.sup.4+
. . . b.sub.Mn.sup.M (3)
[0120] Savitzky-Golay filtering outputs as the filter output g(i)
for the window indexed i the value of b.sub.0 estimated by Equation
(2) for each data window, and successively window-by-window for
successive indices g(i).
[0121] Rows of matrix A are orthogonal when the inner product is
zero for any pair of different ones of them. These rows are
illustrated in TABLE 1. The rows of values A.sub.nj in matrix row n
are non-orthogonal for the example of a polynomial transform. (" A"
signifies raising to a power.)
TABLE-US-00001 TABLE 1 ARRANGEMENT OF MATRIX A.sup.T Power m (Order
M) Points 0 1 2 3 . . . M n = -N: [1 (-N) (-N){circumflex over (
)}2 (-N){circumflex over ( )}3 . . . (-N){circumflex over ( )}M]. .
. . n = -4: [1 -4 (-4){circumflex over ( )}2 (-4){circumflex over (
)}3 . . . (-4){circumflex over ( )}M] n = -3: [1 -3 (-3){circumflex
over ( )}2 (-3){circumflex over ( )}3 . . . (-3){circumflex over (
)}M] n = -2: [1 -2 (-2){circumflex over ( )}2 (-2){circumflex over
( )}3 . . . (-2){circumflex over ( )}M] n = -1: [1 -1 1 -1 . . .
(-1){circumflex over ( )}M] n = 0: [1 0 0 0 . . . 0{circumflex over
( )}M] n = 1: [1 1 1 1 . . . 1{circumflex over ( )}M] n = 2: [1 2
2{circumflex over ( )}2 2{circumflex over ( )}3 . . . 2{circumflex
over ( )}M] n = 3: [1 3 3{circumflex over ( )}2 3{circumflex over (
)}3 . . . 3{circumflex over ( )}M] n = 4: [1 4 4{circumflex over (
)}2 4{circumflex over ( )}3 . . . 4{circumflex over ( )}M] . . . n
= +N: [1 N N{circumflex over ( )}2 N{circumflex over ( )}3 . . .
N{circumflex over ( )}M].
[0122] Next, the process finds an estimated data stream X'=Ab.
X'=A(A.sup.TA).sup.-1A.sup.TX (4)
[0123] An electronic process is set up in a processing circuit as
represented by Equation (2) and electronically executed by the
processing circuit. For Savitzky-Golay filtering, the process is
optimized to only find g(i) as the estimated value of b.sub.0 and
also to perform as much off-line pre-computation as possible.
Accordingly, Equation (4) is revised as in Equation (5) to use only
the n=0 row [1.times.M] of the first pre-multiplying matrix A
instead of the whole matrix A in Equation (4),
g(i)=[1 0 0 . . . 0](A.sup.TA).sup.-1A.sup.TX(i) (5)
[0124] Sometimes a mathematical presentation of Savitzky-Golay
filtering regards the window as multiply-added by a set of (2N+1)
filter coefficients c(n). Here, a [1.times.(2N+1)] filter
coefficient vector C is introduced so that
g ( i ) = C X ( i ) = n = - N + N c ( n ) x ( i + n ) ( 6 )
##EQU00001##
where
C=[1 0 0 . . . 0](A.sup.TA).sup.-1A.sup.T (7)
[0125] In Equation (8), an alternative notation CI equivalent to
Equation (6) post-multiplies Equation (7) by a
[(2N+1).times.(2N+1)] identity matrix I and designates each of the
columns of that identity matrix I as [(2N+1).times.1] unit vectors
E.sub.n. The phrase `unit vector` for .epsilon..sub.n means a
[(2N+1).times.1] vector of all zeroes except for a one (1) at the
nth row position. Furthermore, only the matrix inversion
computations to form the first row of inverse matrix
(A.sup.TA).sup.-1 are relevant and are performed, considering the
pre-multiplication by [1.times.M] row n=0 vector [1 0 0 . . . 0].
Thus, the filter coefficients are also equivalently expressed in
the notation of Equation (8), which is equivalent to Equation
(7).
c(n)={(A.sup.TA).sup.-1(A.sup.T.epsilon..sub.n)}.sub.0 (8)
[0126] The Savitzky-Golay filter does a local polynomial fit in a
least square sense. For a given input variable data window x(i+n)
and window of length 2N+1 and chosen polynomial degree M, the
filter output is given by g(i). Filter coefficients c(n)--2N+1 of
them--are computed, e.g. off-line, by electronic operations
represented by Equation (7) or (8) and loaded into flash memory of
a small signal processing unit either worn on the person or
provided nearby and coupled wirelessly to the accelerometer sensor
210 according to the blocks shown in the FIGS. 1-5. The signal
processing unit suitably has a digital signal processor circuit
such as processor 220 that electronically performs
multiply-accumulates (MACs) represented by Equation (6) according
to a stored program accessing the filter coefficients c(n).
[0127] Some other embodiments use windows that are not centered
around the value at index n used as the output (e.g., n=0). It
should also be apparent from the above process description that a
variety of choices of matrices A are possible and may be used
instead of the particular polynomial transform matrix shown in
TABLE 1. The skilled worker chooses the desired transform, the
window (frame) size (e.g., 2N+1) and the order M. Also, note that
g(i) output of a first filter procedure produces a data stream that
itself can be windowed as represented by column vector
g.sub.1(i.sub.2) in Equation (9B). Accordingly, some embodiments
represented by Equations (9A), (9B) cascade two lower order filters
of Equation (4) and use straightforward technique to minimize the
electronic processing complexity of the computations in
implementation. The transform matrices A1 and A2 can be the same or
different, the window sizes (2N.sub.1+1) and (2N.sub.2+1) can be
the same or different, and the orders M.sub.1 and M.sub.2 can be
the same or different, all these choices being independent of each
other.
g.sub.1(i.sub.1)=[1 0 0 . . .
0](A.sub.1.sup.TA.sub.1).sup.-1A.sub.1.sup.TX(i.sub.1) (9A)
g.sub.2(i.sub.2)=[1 0 0 . . .
0](A.sub.2.sup.TA.sub.2).sup.-1A.sub.2.sup.Tg.sub.1(i.sub.2)
(9B)
[0128] Some embodiments may also apply to the SG process a diagonal
weighting matrix W which is all zeroes in a [(2N+1).times.(2N+1)]
matrix except for weights down the main diagonal. The weights can,
for instance, be one at the middle of the diagonal and diminish
symmetrically in value farther from the middle of the diagonal. The
motivation is that it may not be important for all points in the
window to be well-approximated according to unweighted least
squares, especially in a filter that is providing a determination
of one coefficient as output g(i). In that case, Equation (1) is
replaced by Equation (10), which represents that the squares are
each weighted in the sum of squares .parallel.(X-Ab).parallel.:
.DELTA.b=-(.gradient..gradient..parallel.W(X-Ab).parallel.).sup.-1.gradi-
ent..parallel.W(X-Ab).parallel. (10)
[0129] Then the electronic process represented by Equation (5) for
the output instead is:
g(i)=[1 0 0 . . . 0](A.sup.TWA).sup.-1A.sup.TWX(i) (11)
[0130] The selection of transform type and matrix A is
fixed/predefined by configuration or determined semi-static manner
in some embodiments. Dynamic configuration or selection of the
matrix A or transform type or parameters of a given transform is
contemplated in some other embodiments herein that determine which
is the best transform type, order, window size, amount of
cascading, etc. to use and then dynamically performs processing and
remote communication.
[0131] Some other embodiments store and average a set of values
from the transform output of Equation (4) from different windowed
segments of the data stream X. This approach, roughly speaking,
performs several filters in parallel and averages them in an offset
manner. All the values represent a reconstructed value
corresponding to a same instant of time (i+n)=t, and note that this
approach not only uses the n=0 row to approximate b.sub.0 but also
uses the transform approximations to the other coefficients that
are available from Equation (3). In other words, the results of
approximating the data stream using 2N.sub.1+1 successive windows
are used by selecting only the particular points that represent a
given instant of time t. The number of points 2N.sub.1+1 averaged
(say, some number in a range 3 to 11 points) is enough to average
out some noise without much extra computer burden N.sub.1<=N.
Those points are the successive window data X at indices n=t-i such
that for succeeding windows i, the approximate data values X'
generated by the power series start at high index n=+N.sub.1 and
proceed down to n=-N1. The electronic processor 220 (and/or 240)
executes instructions or otherwise performs the electronic process
as represented by Equation (12), where X'(i+n) is from Equation
(4). Equation (12) reduces to Equation (6) when N.sub.1=0 (i.e.,
2N.sub.1+1=1).
g ( t ) = [ 1 / ( 2 N 1 + 1 ) ] n = - N 1 + N 1 X ' ( i + n ) ( i +
n = t ) ( 12 ) ##EQU00002##
[0132] In view of the analysis herein, it is emphasized that other
types of processes can be alternatively selected according to the
teachings herein, whether they are called Savitzky-Golay or not.
The skilled worker sets up a test bench with library
accelerometer-based waveforms and then makes the transform matrix
choices, choice of number of points (2N+1), and choice of
order-value M, either manually or by an automated process. The
filtering choices are tested either by visual inspection of a
display of output from FIG. 1 process or by automated process
according to metrics of false negatives and false positives, etc.
as described herein. Transform matrix A values, and values of
(2N+1) and M, 2N.sub.1+1, etc. for one or more such filter
processes having favorable metrics are then loaded into the
monitoring device flash memory or hard drive and executed in real
time on processor 220 (or 240).
[0133] A transform for an embodiment approximates an actual data
stream vector x(i+n) and produces an output signal stream that
follows the heart sound peaks well over time in response to a data
stream X herein derived from a body-worn accelerometer. Some
embodiments have reduced processing complexity by using low enough
frame size (2N+1), order M and/or using an efficient transform
matrix A to achieve desired performance for the purposes for which
the monitoring is intended. The same transform is desirably
low-complexity and well-performing over numerous patients,
accelerometers and their positioning on the body, and in different
environments of use, such as clinic, hospital, home, exercise
venue, etc.
[0134] In FIG. 4 and turning to a succeeding electronic process
portion 150 for envelope-based noise rejection, an amplitude
envelope is generated, as shown in FIG. 9. In FIG. 9, the residue
signal stream r(i)=x(i)-g(i) of FIG. 4 has some remaining noise
from subtracting step 140 that subtracts the smoothing filter
output g(i) from the LPF-supplied input x(i). An envelope is fitted
to the residue as indicated by the dotted envelope-line in FIG. 9.
The amplitude-based processed output R(i) is shown in FIG. 9, as
derived from the envelope-fitted residue r(i). The noise n(i) near
the horizontal axis is substantially reduced. Operations for this
process suitably use an envelope-related variable gain function.
Alternatively, the circuit and/or process is arranged to generate
zero signal output when the envelope is below a low threshold that
still passes the peaks.
[0135] Description next turns to the FIG. 4 electronic process
portion 160 called folded correlation. For background, see the
incorporated patent application publication TI-66732. An input data
stream of residue r(i)=x(i)-g(i), or envelope-processed residue
R(i) comes to the folded correlation process. Recall that g(i) is
the output of the smoothing filter such as represented by Equation
(6) or from a buffer memory for it. Processed filtered residue R(i)
is windowed by a further data window (also called a frame) of
length 2N.sub.2+1 (with points accessed by an index n=0, 1, 2 . . .
N.sub.2).
[0136] The output f.sub.c(i) of the folded correlation is given by
Equation (13):
f c ( i ) = n = 0 N 2 R ( i - N 2 + n ) R ( i + N 2 - n ) ( 13 )
##EQU00003##
[0137] The digital data stream for heart monitoring residue signal
samples R(i) from the smoothing filter subtraction is successively
processed in overlapping frames indexed i. In general, the value of
2N.sub.2+1 is selected to be approximately the width t.sub.W of a
desired signal event (e.g., an S1, S2, or R-wave). For example, S1
is typically about 100-150 milliseconds long. If the decimated
sampling frequency f.sub.S is 1000 Samples/sec, the value of
2N.sub.2+1 is established, e.g., as an odd number between 101 and
151, and N2 is some number between 50 and 75 inclusive. Thus,
N2=RND(t.sub.Wf.sub.S/2) (14)
[0138] In some embodiments, the value of N2 is configured in flash
memory, and can be selected or altered by a local or remote
operator of a FIG. 8 remote digital system portion 240, 250 using
the embodiment of structure and process.
[0139] In the electronic folded correlation process 160 represented
by Equation (13), the heart monitoring residue samples R(i+N2-n)
from the later half of each frame are folded around the center
heart monitoring sample R(i) in the frame and multiplied by dot
product (sum of products in Eq. (13)) with heart monitoring residue
samples R(i-N2+n) in the earlier half of each frame. The result of
the dot product is a folded correlation output signal stream
f.sub.c(i) corresponding to instant i of the input residue signal
stream R(i) in the center of the frame.
[0140] In FIG. 10, the residue signal input R(i) (subtraction of SG
fit from LPF input) is shown in a lower waveform and an output
f.sub.c(i) of Folded Correlation 160 is shown in an upper waveform.
Sharp, distinct, positive peaks in output signal f.sub.c(i) are
output by Folded Correlation 160. This is because not only positive
peaks but also negative peaks folded-correlate positively with
themselves due to multiplication (++=+,--=-). Between the peaks,
the noise folded-correlates with itself negligibly. The resulting
output f.sub.c(i) as a whole recovers pulses that follow S1 and S2
heart sounds well.
[0141] Succeeding thresholding passes the S1 peaks and counts them.
Robust detection of primary heart sounds and heart rate from a
chest-worn accelerometer is thus achieved in the presence of
interfering motion artifacts. Such capability is directly relevant
in applications that involve ambulatory monitoring of
cardiovascular and cardio-respiratory health. Applications include:
home health monitoring, fitness applications (exercise monitoring),
hospital and ICU (intensive care unit) patient monitoring, and
patient monitoring at accident sites, in ambulances, gurneys or
rolling patient transfer beds, in mobility aids like scooters and
wheelchairs, and other mobile and/or fixed environments in a
setting that is related to a hospital, clinic, allied medical
testing facility, residence, commercial establishment, airport or
otherwise.
[0142] Heart signal components may have S1, S2, and heart murmur
components. Some embodiments further process heart signal
components by coupling the circuitry and signals described herein
to processing according to the teachings of U.S. Patent Application
Publication 20090192401 "Method and System for Heart Sound
Identification" dated Jul. 30, 2009 (TI-65798), which is
incorporated herein by reference.
[0143] Data used for the evaluation of the methods is collected
from six healthy young volunteers. Ambulatory conditions were
simulated by the subject walking 2-3 minutes at normal speed.
[0144] Table 2 shows the accuracy, number of false positives and
number of false negatives for the subjects collectively. Most of
the false negatives were due to S2 misses.
TABLE-US-00002 TABLE 2 ERROR ANALYSIS OF RESULTS Accuracy 99.36%
False Positives 1.3% No. of S1 misses 2 (0.085%) No. of S2 misses
13 (0.55%)
[0145] FIG. 11 shows a Bland-Altman plot for heart rates
(calculated over every 5 second time segment) for all of the
subjects from ECG data and heart rate estimates obtained from the
processed accelerometer data. The difference of the two heart-rate
measures (accelerometer and ECG) is plotted versus their average. A
few outliers are caused by false positives, but overall, most of
the data is within a 95% confidence interval. For heart rate
calculation both S1 and S2 locations were used. The results are
robust over both lower and higher heart rates.
[0146] In some embodiments, the odd peaks from the output of a
simple form of the peak detector are picked as S1 and the even
peaks as S2. This type of selection may lead to some errors since a
single false peak can cause the error to ripple along. The effect
of this is mitigated to some extent by a choice of performance
measures that look at relative time displacement or distance in
time as opposed to absolute location in time. Nonetheless, the
simple form of the peak detector and process locates most of the S1
and S2 events with very few false positives. In some other
embodiments, to reduce S2 false negatives and reduce false positive
rate even further, the peak detector is augmented with a circuit or
process that incorporates amplitude and S1-S2 interval information
to select the S1 and S2 peaks from the output of the peak
detector.
[0147] In FIG. 12, to further illustrate the ability of a process
embodiment to locate S1 events robustly, the peak detection is made
to pick only S1 events. FIG. 12 shows a plot of Cardiac Interval
from S1 locations from accelerometer data (S1-S1) on the graph
vertical axis, versus ECG R-R interval on the graph horizontal
axis. As seen from FIG. 12, a high correlation (correlation
coefficient of 0.98) between the cardiac periods was obtained from
the different measures. The slope of the least squares fit is
0.99.
[0148] Benefits are obtained by themselves and with other benefits
by structures and processes described elsewhere herein and in the
simultaneously-filed TI-68552 and TI-68553 patent applications,
which are incorporated herein by reference.
[0149] Description turns next to a set of embodiments that separate
and derive motion/activity, heart-rate and respiration from a
single signal from a single chest-worn sensor such as a miniature
Z-axis accelerometer sensor. Ambulatory measurement of respiration
and cardiac activity can find wide application in home health
monitoring of older adults and of patients with a history of
cardiovascular, respiratory, and other conditions for which
respiratory and/or cardiac monitoring are desired. Evaluating
cardiovascular performance of patients in ICU and hospital
settings, in mobile ambulances, and at accident and trauma sites
also calls for ambulatory cardiac and respiratory measurement and
monitoring. Conventional solutions for heart-rate and respiration
monitoring are believed to be expensive, invasive or obtrusive and
too cumbersome for ambulatory and continuous monitoring
applications.
[0150] Remarkably, various embodiments with a single, miniature,
chest-worn MEMS accelerometer and associated monitoring circuitry
measure and monitor respiration, motion and heart
activity--reflected by heart sounds--as shown in FIG. 13.
[0151] In FIG. 13, embodiments are provided for ambulatory
monitoring of heart-rate and heart sounds, activity, body motion
and respiration in a non-invasive and minimally obtrusive way.
Here, a single sensor, such as with a MEMS accelerometer, extracts
not only heart-rate/heart sounds but also respiration in an
ambulatory setting. Any signal is used that includes detectable
heart sound signals from at least one sensor axis of the
accelerometer sensor, or from two or more sensor axes. When a body
motion signal component is included in the sensor signal, that body
motion signal component is in some embodiments separated out or
isolated and delivered as a useful output representing activity or
motion as well.
[0152] Some advantages of various embodiments are extraction of
three vitals (respiration, activity, heart sounds/heart-rate) from
a single sensor and a single signal. A miniature sensor embodiment
taped on the chest provides a non-invasive and minimally obtrusive
way to sense and monitor vital physiological parameters in the
presence of motion. Embodiments can be used with minimal
inconvenience in ambulatory and continuous monitoring applications,
and are very inexpensive and can be made into disposable patches
and tapes, for instance.
[0153] In FIG. 14, a respiration waveform is obtained, for example,
by a monitoring device embodiment of FIG. 13 and its process
embodiment. The process in FIG. 14 receives the raw signal stream
from the FIG. 13 ADC (analog to digital converter) and then first
separates the heart sounds from the composite signal from the
sensor using Savitzky-Golay (S-G) polynomial fitting followed by
Folded Correlation and Peak Detection to deliver the S1 heart
signal peaks. The heart rate is counted in response to the peak
detection to provide a Heart-Rate signal output. Concurrently, the
respiration is monitored by then measuring the successive times,
called the inter-beat intervals or S1-S1 intervals, between heart
beats--beat-by-beat. The variation in the measured inter-beat
interval over time thus represents respiration because it is
respiration-dependent and substantially independent of
non-respiratory gross body motion. The monitoring device thus
delivers as a respiration waveform that substantially represents
the inter-beat interval varying over time. Further respiration
processing counts the breathing rate and delivers a resulting
breathing rate output, and derives and outputs any other useful
information. In the meantime, the motion signal is extracted either
from the S-G polynomial smoothing filter 130 as in FIG. 4 or by a
low pass filter LPF with corner frequency at 2 Hz as shown in FIG.
14.
[0154] In FIG. 14, post-processing of the motion signals is applied
to monitor and deliver waveforms representing average activity
level over time, monitor walking gait and other motions, and detect
a fall if one were to occur. Consequently, deriving motion or
activity from an accelerometer is important, such as by the present
embodiments. For instance, average activity level can be generated
as the root-mean-square (RMS) of the motion waveform measured over
an hour and output hour-by-hour. Walking gait can be derived from
the Z-axis alone or in combination with signal streams from other
accelerometer axes with respiration subtracted out. A fall is
indicated such as by a peak detection of an unusually
high-magnitude acceleration peak which stands out from any recent
or subsequent neighbor peaks in a predetermined window of time such
as +/-15 seconds. Some embodiments thus deploy motion-based
analysis as a fall sensor as described herein or otherwise in any
suitable manner enabled by an embodiment.
[0155] Process embodiments as in FIGS. 3-4 separate motion signal
components from an accelerometer sensor signal to cleanly and
robustly extract heart sounds, which enables the use of the
accelerometer 210 to monitor heart sounds in the presence of motion
and activity. Moreover, process embodiments as in FIGS. 13-14
separate motion signal components from an accelerometer sensor
signal to cleanly and robustly extract heart sounds and use the
accelerometer sensor to monitor not only heart sounds but also
respiration (derived from heart sounds) in the presence of motion
and activity, and further to deliver a motion/activity signal as
well. In some embodiments, motion-based gating is performed to
reject signal frames in the event that the motion/activity level is
unusually high and does not permit reliable detection of heart-rate
or respiration or some other derived signal under a given detection
process. Reliable cancelation of motion artifacts from
accelerometer signals to extract heart sounds is described, among
other things, hereinabove and in the simultaneously-filed TI-68518
patent application, which is incorporated herein by reference.
[0156] In FIGS. 13-14, one example of a monitoring system
embodiment has the same hardware and accelerometer sensor
description as given in connection with FIGS. 1-4. Respiration
monitoring is added as in FIG. 14. A reference ECG may be provided
as discussed for FIG. 2.
[0157] In FIG. 15, detection of a body motion waveform is shown.
The monitoring device measures gross body motion to sense and
monitor activity and can provide a useful index of a person's level
of activity over a period of time, and facilitate inferences about
the person's lifestyle and metabolic index--jointly with
ECG-derived heart-rate. Gait recognition by accelerometer-based
motion monitor aids biometric assessment and can identify signs of
or precursors to a dangerous fall. Also, using the
accelerometer-based motion monitor as an indicator of sudden,
high-magnitude acceleration can additionally identify signs of or
precursors to a dangerous fall, as well as a fall itself.
[0158] In FIGS. 15 and 14, the signal obtained during motion from
the chest-worn accelerometer is digitally low-pass filtered at 2
Hz--using a digital FIR filter--to extract the slowly varying
baseline wander due to motion. For at least some cases of body
motion as well as the body at rest, the respiration signal is well
decoupled in frequency from the low-frequency body motion signal
and is thus digitally low-pass filtered to successfully extract the
respiration signal. FIG. 15 shows the raw and filtered motion
signal extracted from the accelerometer during rest and brisk
motion. Motion/Activity extraction from the accelerometer is
conveniently achieved.
[0159] The implementer pays attention to physical attributes of the
sensors in order to reduce unnecessary or activity-irrelevant
motion artifacts. Coupling noise is reduced through good sensor
location and placement and secure attachment of the sensor. Wire
line noise or cable noise is kept low or eliminated by intelligent
selection and placement and secure electrical and physical
attachments. Wireless transmission is alternatively used to couple
the hardware components of the monitoring system to reduce or
eliminate body motion effects other than those picked up by the
accelerometer itself and included in the acceleration
signal(s).
[0160] In FIG. 16, sensing, detecting, and monitoring heart rate,
heart sounds, and cardiovascular and cardio-respiratory activity
during motion and exercise importantly support continuous patient
motoring and fitness applications, as well as at emergency and
accident sites. The accelerometer-based extraction of primary heart
sounds--S1 and S2 produced by the heart valve pairs closing at the
ends of the diastolic and systolic periods respectively of the
cardiac cycle--is robust in the absence or presence of motion, as
shown in FIG. 16. The primary heart sounds are robustly detected
through the S-G processing of the chest acceleration signal, not
only during resting conditions, but also in the presence of
strongly interfering motion--like walking
[0161] In a respiration monitor example for FIG. 16, the S-G
digital filtering 130 and residue generation 140 by the processor
to obtain the heart sounds are as described in connection with
FIGS. 3 and 4. Timing- and amplitude-based thresholding 150 and
Folded Correlation 160 are applied as process steps of FIG. 4 as
described earlier hereinabove. The Folded Correlation 160 in an
example has a frame size of 7 at with the 1000 samples/sec that
resulted from decimation. The peaks in FIG. 10 corresponding to S1
and S2 in the motion-removed acceleration signal are thus
strengthened and are then peak-detected in step 170 for counting
180.
[0162] FIG. 16 shows as a first waveform the raw acceleration
signal low pass filtered at 50 Hz. A second waveform represents the
electronically-performed numerical polynomial fit g(i)
corresponding primarily to the motion. A third waveform is the
residue r(i) after subtracting the second waveform from the first
waveform. The third waveform exposes or isolates the heart sounds,
with motion-induced amplitude variations of FIGS. 13 and 16 as
amplitude modulation thereon. In FIG. 16, the timing locations of
heart sound components S1 and S2 show plainly and precisely, and
they are largely independent of the amplitude modulation except for
respiration-related variation of inter-beat interval. A fourth
waveform in FIG. 16 is the simultaneously acquired ECG signal that
is used as a timing reference or reference standard. Heart sound
detection in the presence of motion is thus achieved.
[0163] Detection of respiration from the inter-beat interval has a
physiological basis. Respiration modulates the heart rate, and
consequently the inter-beat interval, by a phenomenon called
respiratory sinus arrhythmia RSA, which is possibly responsive to
respiration-related and other intrapleural or intra-thoracic
pressure changes. The respiration-dependent variation in inter-beat
interval is conventionally obtained from the R-R interval in the
ECG recording. R-R interval robustly tracks respiration even during
motion and exercise.
[0164] FIGS. 17, 19, and 22 respectively show three processes or
methods of electronically generating respiration signal outputs
from a sensor input. In various embodiments, these processes are
used either individually, or in pairs, or a combination of all.
[0165] Some embodiments as illustrated in FIGS. 13, 14, 16 and 17
make the ECG recording optional, or obviate and eliminate the ECG
recording, by deriving respiration from an accelerometer
sensed-and-residue-detected variation in the S1-S1 interval during
motion.
[0166] In FIG. 17, a process embodiment obtains the heart sound
signal at a step 310 by removal of motion-dependent wander in Phase
2 of FIG. 4. The heart sound peaks are reinforced through Folded
Correlation 320, and peak detection 330 is performed to detect the
S1-S1 peaks. In a step 340, the S1-to-S1 interval (or S2-to-S2
interval) is repeatedly computed beat-by-beat to electronically
obtain data values of successive inter-beat intervals. These data
points are interpolated in a step 350 to yield a continuous
respiration waveform of FIG. 18.
[0167] In FIG. 18, a first waveform shows the raw acceleration
signal fed from the 50 Hz LPF to the smoothing filter in FIG. 4. A
second waveform shows the residue signal that delivers filtered
heart sounds S1/S2. A third waveform shows a varying inter-beat
interval--the S1-S1 Respiration-related waveform--derived according
to the process of FIG. 17 and superimposed on a concurrent ECG
(R-R) derived respiration waveform. The fit is quite favorable, as
disclosed by inspecting the two different scales of illustration in
FIG. 18.
[0168] In FIG. 19, another process embodiment is called the
baseline wander method herein for respiration monitoring by a
single accelerometer sensor. This process in a step 380 operates on
the raw accelerometer ADC signal input from a person at rest by
low-pass filtering it with a filter cutoff frequency at about 2
Hertz, or otherwise selected, e.g., with LPF cutoff in a range of
about one (1) Hertz to about three (3) Hertz. A waveform called
Baseline Wander thus obtained as an electronic respiration signal
at a step 390, with example waveforms shown in FIGS. 20 and 21 for
comparison with respiration outputs from each of the processes of
FIGS. 17, 19 and 22. In other words, breathing periods of about
half a second or more are passed, so that not only resting
breathing periods of a breath every two or three seconds are
detected, but also breathing periods under stress or after exercise
down to about a third or half a second are detected. Shorter period
signal variations in the accelerometer are suitably obtained from
the S-G filter smoothing or by other types of filtering to
represent body motions other than respiration.
[0169] In FIG. 20, a comparison of concurrent waveforms of
respiration generated by different embodiments is shown along with
a reference respiration waveform obtained from a respiration belt
or spirometer. A second waveform shows a baseline wander signal
using the process embodiment of FIG. 19. A third waveform shows the
varying inter-beat interval method or process of FIG. 17 (RSA:
S1-S1 interval), and a fourth waveform shows output from a heart
sound amplitude modulation process embodiment of FIG. 22.
[0170] In FIG. 21, another comparison of concurrent waveforms of
respiration generated by different embodiments is shown. A first
waveform shows the variation of S1 amplitude output from a heart
sound amplitude modulation process embodiment of FIG. 22. A second
waveform shows a trace of S1 power obtained by further processing
the first waveform. A third waveform shows the varying inter-beat
interval method or process of FIG. 17 (RSA: S1-S1 interval). A
fourth waveform shows a baseline wander signal using the process
embodiment of FIG. 19. A fifth waveform shows a reference
respiration signal. A sixth waveform shows an ECG-derived
respiration signal from inter-beat interval obtained from the R-R
interval of the ECG. A seventh waveform shows a trace of filtered
acceleration.
[0171] In FIG. 22, a further process embodiment, for respiration
monitoring by a single accelerometer sensor 210, obtains the heart
sound signal at a step 410 by removal of motion-dependent wander in
Phase 2 of FIG. 4. The heart sound peaks are reinforced through
Folded Correlation 420, and peak detection 430 is performed to
detect the S1 peaks. These S1 peaks are amplitude modulated as
shown by the second (middle) waveform of FIG. 23. Instead of (or in
some embodiments in addition to) inter-beat interval measurement as
in FIG. 17, the successive S1 peak amplitudes (or S2 peak
amplitudes) in a step 440 are repeatedly measured electronically
beat-by-beat in FIG. 22 to electronically obtain data values of
successive heart sound peak amplitudes. These data values are
interpolated in a step 450, such as by linear or quadratic or other
interpolation, to yield a continuous respiration waveform. Examples
are comparatively shown as the first and second waveforms of FIG.
21 for amplitude and power respectively.
[0172] FIG. 23 illustrates the close correspondence of respiration
measurements obtained in different ways. In a first waveform from
ECG, amplitude modulation rides on the R peaks. Amplitude
modulation rides on the S1 peaks in the second (middle) waveform
from accelerometer based sensing according to the process
embodiment of FIG. 22. The S1 amplitude modulation correlates well,
as seen by comparison with the R amplitude modulation on the first
waveform from ECG. A third waveform shows a respiration signal
obtained from a respiration belt for reference.
[0173] FIG. 24 shows an implementation of a wired system embodiment
600 for a respiration and cardiac monitoring system. An
accelerometer 510 is strapped to the chest of the person being
monitored. An axis sensor signal is coupled to a data acquisition
signal processing unit 520 having a stream data interface and an
associated data storage unit 530 for the signal stream and for
instructions and parameters. The signal processing unit 530
supplies process monitoring data to one or more display units
550.i, each having a respective data storage unit 560.i. A first
form of display 550.1 shows the heart sound signal and/or heart
rate. A second form of display 550.2 shows the body motion signal.
A third form of display 550.3 shows the respiration signal and/or
respiration rate and/or or respiration depth (how deeply the person
is breathing) and/or other respiration parameters. Various
parameters for respiration are obtained from the respiration
waveforms by finding various values on the waveforms and
differences and trends therein. For example, respiration rate is
measured as the number of cycles of inhalation and exhalation in a
given time window (e.g. one minute). Averaging and signal fusion
methods/algorithms are also usable in some embodiments to derive
more robust respiration rates from multiple parameters. For
instance, how deeply the person is breathing is represented by an
average of the difference between successive values of inhalation
peak and exhalation trough in a given time window (e.g. one
minute). Averages and trends in the inhalation peaks are readily
calculated and displayed. Averages and trends in the exhalation
troughs are also readily calculated and displayed.
[0174] The system 500 of FIG. 24 is suitably arranged and
physically protected for mobile and ambulatory monitoring
environments. In other forms the system 500 is set up for use in a
clinic by one or more clinicians concurrently.
[0175] FIG. 25 shows an implementation of a wireless system
embodiment 600 for a respiration and cardiac monitoring system
including various remarkable device or component embodiments. The
description parallels that of FIG. 24, except that the
accelerometer sensor 610 and its electronic circuit also have a
Bluetooth or other short range wireless modem wirelessly coupled to
another short range wireless modem 622 that is coupled via a
streaming data and control interface 624 to a data acquisition
signal processing unit 620. Further, modems 640.2 and 670 for
wireless transmission and reception remotely are provided at each
of two locations so that the data acquisition signal processing
unit 620 communicates via its modem 640.2 to the remote wireless
transceiver unit or modem 670. The latter modem 670 is coupled to
be one or more display units 650.i and their storage unit(s) 660.i.
In this way, tele-medicine applications are supported. The
acquisition signal processing unit 620 and its modem 640.2 are
suitably provided in a residence or ambulance or on the person or
in a wheelchair or gurney. The wireless transceiver 670 and display
unit(s) 650.i are suitably provided in a clinic, hospital, medical
monitoring center or otherwise. Either or both ends of the wireless
system may be mobile, such as one example of a modem 640.3 and
alert/processor/display 680 when a professional in a vehicle is
urgently needed to review data coming in from a residence or
another vehicle in case of emergency and to respond with
instructions.
[0176] In FIG. 25, combinations with further processes, circuits
and devices for automatic cautionary responses, warnings, and/or
automated monitored therapeutic responses are contemplated. Upon
occurrence of undue excursions of one or more measured parameters
or relationships among parameters detected by signal processing
unit 620, the remote processor 670 alerts any one or more of
medical professional, patient, caregiver, and/or family member via
a modem 640.3 and alert/processor/display unit 680 by sending a
cellular telephone call and/or other voice call or message and/or
written alert such as an automatic e-mail. The alert system
suitably provides for acknowledgement by any of the recipients.
Also, another modem unit 640.1 is suitably provided and coupled to
a tele-medicine therapeutic or assistive device 690 for assisting
the patient in some pharmacological, informational, or physically
assistive way by administering a medicine, or adjusting a dosage or
otherwise. In case of excursions that indicate an extreme medical
emergency, the data acquisition signal processing unit 620 may be
permitted to locally control the therapeutic or assistive device
690 temporarily and in a maximally-safe way until remote commands
are received or first responders can arrive. Mere removal or
inadvertent detachment of the accelerometer 610 from the chest is
distinguished by the electronic processing 620 from affirmatively
detected excursions of measured signals and parameters. Regarding
tele-care assistance, such assistance is suitably rendered in some
physical way in response to the real-time accelerometer sensor 620
data by activating motorized apparatus comprehended by device 690
such as to adjust a motorized bed, or move a scooter into proximity
for patient use, or servo-mechanically actuate and flush a toilet
unit, or open a tub drain to empty a tub, or some other
assistance.
[0177] In FIGS. 24 and 25, various parts of the systems 500 and 600
are each variously miniaturized and partitioned into various
modules and provided with various types of wireline interfaces or
wireless modems for different types of products. In this way,
different system embodiments are provided. One type of embodiment
forms a complete medical clinic system. Another type of embodiment
is a patient-worn medical-sensor and/or therapeutic device that is
wired or has a wireless modem. Another type of embodiment is a
patient-worn signal processing and modem module on a belt clip that
connects or wirelessly couples to such a sensor and wirelessly
couples to a premises gateway or directly transmits to a remote
location. Another type of embodiment includes the sensor, signal
processor, memory, and modem together in a micro-miniature device
that is taped to the chest and communicates to a router or gateway
locally, and the latter communicates remotely. Another type of
embodiment is the local router or gateway that includes signal
processor, memory, and multiple types of modem to communicate with
the sensor and separately communicate remotely, such as in a
patient home-based system to telecommunicate to clinic or hospital.
See FIG. 39 and FIGS. 40A/40B for an example of apparatus to
support these various embodiments.
[0178] Respiration detection and monitoring for a person performing
body motion or at rest are thus conveniently achieved along with
cardiac monitoring. Local/remote assistance is suitably initiated
responsively to such detection and monitoring. By contrast,
conventional respiration measurement devices like respiration belts
and spirometers are very susceptible to motion-dependent artifacts
and/or are very unwieldy for continuous ambulatory monitoring.
Various embodiments can therefore significantly facilitate the
measurement of respiration in the presence of motion, or at rest.
Benefits are obtained by themselves and with other benefits by
structures and processes described elsewhere herein and in the
simultaneously-filed TI-68553 and TI-68518 patent applications,
which are incorporated herein by reference.
[0179] Description turns now to further embodiments for estimation
of blood flow and hemodynamic parameters from a single chest-worn
sensor. Embodiments are provided for measurement of blood flow
trends (stroke volume, cardiac output) and other hemodynamic
parameters (contractility, pre-ejection period, iso-volumic
contraction interval) in a non-invasive and minimally obtrusive
way. These measurements are believed to have been problematic,
expensive, and inconvenient in the past. Conventional hemodynamic
monitoring e.g., some forms of Doppler echo or impedance
cardiograms, and some blood pressure monitors, may be expensive,
invasive or obtrusive and too cumbersome for ambulatory and
continuous monitoring applications. Here, by contrast, a single
miniature sensor such as a MEMS accelerometer coupled with a data
acquisition signal processing embodiment extracts hemodynamic
parameters from the in-plane vertical accelerometer axis (Y-axis).
These benefits are obtained by themselves and with the respiration
detection and other features that are described hereinabove and in
the simultaneously-filed TI-68552 and TI-68518 patent applications,
which are incorporated herein by reference.
[0180] Among the advantages of some of the present embodiments,
are:
a. Uses a single sensor and a single signal to extract several
hemodynamic vitals such as any, some or all of changes in stroke
volume, changes in cardiac output, heart-rate, isovolumic
contraction interval, etc. b. Is minimally obtrusive (miniature
sensor taped on the chest) c. Can be used with minimal
inconvenience in continuous monitoring applications d. Disposable
patches/tapes carry the sensor and offer low cost and
convenience.
[0181] Embodiments of system, circuits and process enable the use a
single chest-worn miniature sensor (e.g., a dual-axis or
triple-axis accelerometer) for the extraction of a signal closely
related to the flow of blood from the heart. This enables
extraction and assessment of other hemodynamic and cardiovascular
parameters such as those discussed above and in the next several
paragraphs.
[0182] Isovolumic contraction interval IVCI is the duration of an
event during the early systole when the heart ventricles contract
without any change in volume. During the isovolumic contraction
interval the myocardial muscle fibers have begun to shorten but
have not developed enough pressure in the ventricles to overcome
the aortic and pulmonary end-diastolic pressures and thereby open
the aortic and pulmonary valves. Such contraction interval occurs
after the closure of the mitral and tricuspid valves and before the
opening of the semi-lunar valves. Both pairs of heart valves are
closed during this interval. IVCI can be estimated as the time
difference between the peak of the S1 waveform (from the normal
axis or Z-axis of the accelerometer) and beginning of the first
peak of the vertical Y-axis accelerometer signal. This interval
ICVI is expected to correlate well with the time difference in FIG.
32 between the time P1 of occurrence of the S1 sound and the peak
location in time F1 of the flow signal.
[0183] Stroke volume SV is the difference between the end diastolic
volume and end systolic volume and is a measure of the blood pumped
by the heart per cardiac cycle. A conventional pulse contour method
calculates a blood flow variable (milliliters/sec) from the
pressure signal and computes the stroke volume by integrating the
blood flow signal over a cardiac period. By embodiments of
structure and process herein, a peak amplitude PAmp and Jamp of the
flow signal derived by filtering from the accelerometer sensor is
used to compute relative changes in the stroke volume. Stroke
volume is computed by first applying a blood pressure signal, or a
signal related thereto, to a model of the arterial system. One such
model is called a non-linear Windkessel model, which regards the
blood pressure as analogous to a voltage applied to a
series-parallel network having a series impedance to a output, and
a parallel resistor-capacitor combination across the output. These
model circuit elements are modeled as non-linear to model behavior
of the arteries as they expand under blood pressure. The blood flow
is analogous to the voltage across the output of the circuit. The
integrated output voltage over a period of heart rate S1-to-S1 is
related to stroke volume for that period and is repeatedly
computed. Some other models analogize a reflective electrical
transmission line to the arterial system. Any model appropriate to
the purposes at hand is employed.
[0184] Cardiac output CO is defined as the product of stroke volume
and heart rate. CO is the volume of blood pumped by the heart per
minute. Heart rate is obtained either by counting S1 pulses derived
from an axis sensor of the accelerometer or counting R pulses using
an ECG.
[0185] Pre-ejection period PEP is the time interval between onset
of ECG QRS complex and the cardiac ejection. PEP is calculated from
the beginning of the ECG QRS complex to the beginning of the first
peak in the accelerometer signal, see FIG. 32. The R-F1 interval
approximates the pre-ejection period.
[0186] Ventricular contractility VC measures the intrinsic ability
of the heart to contract. Contractility can be estimated from the
Stroke Volume SV. Increase in Stroke Volume causes an increase in
contractility. Contractility VC may alternatively or additionally
be measured by trending the pre-ejection period PEP.
[0187] Embodiments of structure and process herein are provided to
monitor--by non-invasive and unobtrusive means--some or all of
these vitals and others. Remarkably, a single, miniature,
chest-worn MEMS accelerometer is processed to sense and measure
blood flow and other hemodynamic parameters such as stroke volume
variations--cardiac output variations, iso-volumic contraction
interval; and jointly with a simultaneous ECG--contractility and
pre-ejection period. The signal corresponding to and related to
these parameters is picked up and extracted robustly from the
accelerometer Y-axis, its axis parallel to or in the plane of the
chest and oriented vertically if the patient is standing or seated
vertically, or parallel to a line from head-to-feet
(superior-inferior) if the patient is prone or otherwise not
standing or seated vertically.
[0188] In FIGS. 26 and 30, a system embodiment provides a
convenient monitoring device and measurement set-up as shown. A
miniature (weight--0.08 gram, size--5.times.5.times.1.6 mm) triple
axis, low-power, analog output MEMS accelerometer (LIS3L02AL,
STMicroelectronics, Geneva, Switzerland) is taped onto the chest
and the acceleration signal along the Z-axis--orthogonal to the
plane of the chest--corresponding to the heart sounds is captured.
Simultaneously, the acceleration signal along the Y-axis--in the
plane of the chest and oriented vertically upwards--corresponding
to the blood flow and related hemodynamics is also captured by the
same MEMS accelerometer. In this way, the acceleration signal along
the in-plane vertical axis (Y-axis) is also captured.
[0189] The chest acceleration signals from both axes are, for
instance, concurrently AC coupled (high pass rolloff was dropped
about 10.times. in an example compared to the non-critical three
(3) Hz described earlier hereinabove) and separately and in
parallel are amplified with a gain of 100 and low pass
filtered--for anti-aliasing--through a three-stage, 5-pole
Sallen-and-Key Butterworth filters with a 1 kHz corner frequency.
Two commercial quad operational amplifier packages (LT1014CN,
Linear Technology, Milpitas, Calif.) are used for the analog
front-end. The accelerometer signals are then each sampled at
10,000 (10 K) Samples/sec using a data acquisition card (National
Instruments, Austin, Tex.) and captured and stored on a computer
using MATLAB software (Version 2007b, The Mathworks, Natick,
Mass.).
[0190] A reference ECG as in FIG. 2 is acquired simultaneously in a
three electrode (single lead) electrocardiogram configuration for a
reference in order to compare with the accelerometer-derived
cardiac signal and also to extract information from the fusion
(FIG. 33) of the electrical and mechanical signals (e.g.,
pre-ejection period PEP, contractility VC). An additional parallel
signal path structure is replicated as described in the previous
paragraph and used for the ECG signal.
[0191] The extraction of primary heart sounds (S1 and S2--produced
by the heart valve pairs closing at the ends of the diastolic and
systolic periods respectively of the cardiac cycle) uses a Z-axis
sensor of the accelerometer worn on the chest. In FIG. 31 (and FIG.
4) the primary heart sounds are robustly detected through
post-processing of the Z-axis chest acceleration signal as
described earlier hereinabove, not only during resting conditions,
but also in the presence of strongly interfering motion--like
walking
[0192] The acceleration signal acquired from a subject at rest is
digitally low pass filtered at 50 Hz--using an FIR filter--and
decimated by a factor of 10. The slow varying respiration baseline
wander (e.g., sub-0.5 Hz respiration and body motion) is removed by
smoothing filter and subtraction to yield a residue signal, and the
primary heart sounds (S1 and S2) are detected through amplitude and
timing based peak detection.
[0193] Hemodynamics from Y-axis: As shown in FIG. 27, striking
differences were observed between the signals picked up by the
Y-axis (in the chest plane) and the Z-axis. S1 and S2 align well in
shape and timing in both axes, but the additional features (lower
in frequency with the biggest peak between S1 and S2--marked at top
in black arrows) occur very strongly along the Y-axis, but are
hardly present in the Z-axis direction, as would be expected of a
blood flow-related signal. In the description herein, the phrase
flow signal or flow-related signal or blood flow signal is applied
as a useful identifying label for this chest acceleration
component, recognizing that the accelerometer is sensing a
component of chest acceleration in meters/sec.sup.2. This
time-varying acceleration component may also be thought of as a
body-reaction acceleration or skin-shear acceleration approximately
parallel to the superior-inferior body axis in response to blood
flow and is related in some way to an overall force of cardiac
contraction in newtons and/or to systolic blood pressure in
newtons/square meter and/or to blood flow acceleration in
milliliters/sec.sup.2 and/or blood flow velocity in
milliliters/sec. Also, the dynamics of the blood varies spatially
and with time at different points in the interior and along the
blood vessels of the arterial system emanating from the heart.
Arterial wall friction and elasticity are involved, and these
change if hardening of the arteries occurs. With these
considerations in mind, the accelerometer signal(s) are processed
as described further and post-processed and interpreted in any
appropriate manner by the skilled worker now and in the future to
fully realize the benefits of various embodiments.
[0194] For example, the Y-axis signal is filtered or smoothed using
a low order (4.sup.th order) Savitzky-Golay polynomial filter with
a window size roughly 200 milliseconds. Different window length and
polynomial orders are feasible. Also, specific polynomials and
orders are illustrative and not limiting because they are related
to the signal and the sampling rate. The smoothing filter extracts
the slow varying (lower frequency) blood flow signal separated from
the residue of the heart sounds (S1 and S2). Using Savitzky-Golay
filtering as the smoothing filter is one of various possible ways
of extracting the flow signal from the Y-axis. Slow varying
respiration and body motion baseline wander (e.g., sub-0.5 Hz,
below about one-half Hertz) is also removed or separated from the
blood flow signal in some embodiments by either cascading or
combining a high pass filter to attenuate the respiration wander,
or using a smoothing filter to isolate the respiration wander and
subtracting to yield a residue signal. Further in some embodiments,
the respiration is separated from body motion as taught elsewhere
herein. See filtering process discussion earlier hereinabove.
[0195] FIG. 28 shows a first waveform with the raw Y axis signal
and a second waveform with the raw Z axis signal. A third waveform
traces the extracted flow signal from the S-G filter called a blood
flow component, or flow signal, herein. Notice that the 200 msecs
window size nicely encompasses either straight or curved portions
within a cycle of the oscillating blood flow signal as the raw Y
axis signal stream progresses through the filter window. A fourth
waveform traces the residue along the Y-axis following
Savitzky-Golay smoothing (chiefly the S1 and S2 sounds). Put
another way, FIG. 16 concurrently shows raw Z-axis sensor signal,
raw Y-axis sensor signal, a Savitzky-Golay smoothed signal along
Y-axis (interpreted as blood flow), and a signal residue along the
Y-axis after removal of blood flow component.
[0196] In FIG. 29, a first waveform traces the ECG signal. A second
waveform shows heart sounds derived from the Z axis accelerometer
signal as the residue of polynomial filtering of FIG. 31 (left
side). A third waveform traces the extracted flow signal from S-G
filtering of the accelerometer Y-axis sensor signal of FIG. 31
(right side). In this third waveform, a primary "flow" peak
(between S1 and S2) from the accelerometer-based waveform is
prominent on the third waveform in FIG. 29 and bears some
relationship to a signal from a ballistocardiogram (BCG) while not
being identical to a BCG. This third waveform is called a flow
signal and appears to be related to some extent with the
acceleration of surge blood as it is pushed out of the heart's left
ventricle into the aorta. The flow signal obtained from the
accelerometer Y-axis has noticeable ringy-ness to it, and the
additional features of the flow signal of the S-G filtered Y-axis
accelerometer sensor can convey some useful information about heart
and valve mechanics and possibly other subjects, as described
further elsewhere herein. Such information is suitably generated
and communicated electronically in the wired or wireless apparatus
of FIGS. 24, 25 and 26 operating according to processes described
in FIG. 31 and FIG. 36A, and/or in FIG. 36B.
[0197] Detection of Isovolumic Contraction Interval IVCI (FIG. 32)
is provided in some embodiments by electronically measuring the
time interval between the closure of the mitral and tricuspid valve
(S1 waveform on the Z-axis signal) and the start of blood flow
(measured by the first major inflection point on the Y-axis flow
signal after S1) to output a measure signal representing the IVCI
of the ventricle.
[0198] Detection of Pre-ejection Period PEP and Contractility VC
involves the QRS waveform of the reference ECG (FIGS. 2, 29, 32)
that signifies the instant of ventricular depolarization. PEP is
provided in some embodiments by electronically measuring the time
interval between the peak in the QRS wave and the start of blood
flow to output a measure signal signifying PEP. The electronic
measurement jointly processes 1) the flow signal from filtering the
accelerometer-derived Y-axis (FIGS. 26, 28-31), and 2) the
simultaneous ECG as shown in FIG. 2.
[0199] In FIG. 31, a process embodiment is represented physically
in system storage unit or memory of FIG. 25 and executed on the
signal processing unit of FIG. 25 or digital signal processor DSP
of FIG. 26. In FIG. 31 (left side), Z-axis signal processing
110-170 generally is analogous to the Z-axis processing of FIG. 4,
and additionally the peak detection 170 of the S1 pulse is followed
in FIG. 31 by a time determination 780 of a first peak location in
time P1 of the first heart sound peak S1 in each heart beat.
Counter operation in response to the S1 pulses (and S2 pulses or
both) electronically generates a heart rate signal as in FIG.
36.
[0200] In FIG. 31 (right), Y-axis signal processing at step 710 low
pass filters (LPFs) the Y-axis input (filter cutoff 50 Hz) and then
a step 730 S-G polynomial-filters (or otherwise filters
effectively) the LPF signal from step 710 to produce a flow signal
output from step 730. A step 770 performs electronic peak (and
trough) detection on the flow signal to identify a flow peak
amplitude PAmp. A succeeding step 785 identifies the location F1 of
that peak in time, as illustrated graphically in FIG. 32. Then, in
FIG. 31, both the heart rate signal output HR and first peak
location in time P1 from step 780 are combined with and/or compared
with first flow signal peak time location F1 from step 785 for each
heartbeat. The time difference of F1-P1 represents or is
proportional to the isovolumic contraction interval IVCI. IVCI in
FIGS. 31 and 32 is estimated, for example, as the time difference
between time P1 of the peak of the S1 waveform (derived from the
residue signal based on the Z-axis sensor) and the time F1 of the
first peak of the flow signal derived from the Y-axis sensor. (Some
embodiments determine heart sound time P1 based on Y-axis
processing instead of Z-axis processing.)
[0201] In FIG. 31, Stroke volume SV and relative changes therein
are computed at a step 790 proportional to the peak amplitude PAmp
of the flow signal derived by filtering from the Y-axis
accelerometer sensor. SV in some embodiments is computed
proportional to the peak amplitude PAmp of the flow signal
multiplied by the ICVI estimate. Cardiac output CO in FIG. 31 is
electronically generated by multiplying stroke volume SV by heart
rate HR derived from Z-axis processing from the step 140 residue
from accelerometer sensor filtering and the result is supplied to
display 550.i or 650.i and other devices as discussed in connection
with FIG. 25 and other system Figures. In FIG. 31, hemodynamic
parameters including all of IVCI, SV, CO, PEP, and VC and others
are obtained.
[0202] FIG. 32 depicts an ECG, heart sound signal, and flow signal
from the accelerometer 610. The time P1 of occurrence of the S1
sound and the peak location in time F1 of the flow signal in FIG.
32 are used to provide a time difference for electronically
estimating isovolumic contraction interval ICVI. Pre-ejection
period PEP is electronically derived using the time difference
between the peak of ECG signal and peak F1 of the flow signal. A
peak amplitude of the flow signal is signified by PAmp in FIG. 32.
Peak amplitude PAmp is electronically derived as the difference
between the highest peak of the flow signal during a given
heartbeat and a baseline average of the flow signal as indicated by
a medial dotted line for that heartbeat. A succeeding peak Jamp is
also depicted. An electronic process embodiment generates signals
representing a value of PEP for every heartbeat and a value of PAmp
for every heartbeat.
[0203] FIG. 33 shows superimposed flow signal ensembles for
individual heart beats aligned to the ECG R-waves. The superimposed
ensembles show relative changes (up to 2.times.) in signal
amplitude and timing (jitter/dilation) during recovery of subject
from mild exercise. The multiple ensembles or heart-beat intervals
from the same signal acquired during exercise recovery are
superimposed after aligning to ECG R-wave. They show the amplitude
and timing jitter in this Y-axis signal that is interpreted as
blood flow. Amplitude jitter shows that the initial flow is almost
twice stronger shortly after exercise and the intensity of blood
flow decreased as the subject recovered over time. Also, the timing
jitter shows that the initial flow peak is closer to the ECG R-wave
(shorter pre-ejection period) corresponding to a more contractile
state of the heart. Some embodiments also generate a signal
representing the variance of the jitter in either or both of ICVI
and PEP during a period of exercise and/or during a period of rest,
as well as changes in such variance value, as an indication of
heart function and changes therein.
[0204] In some embodiments that measure Pre-ejection period PEP and
contractility VC, one of the ECG electrodes of FIG. 2 is physically
combined or associated with the accelerometer sensor of FIG. 2,
FIG. 26, and/or FIGS. 40A, 40B. Affixing both the
electrically-separate ECG sensor and accelerometer as one physical
unit simultaneously to the chest affords additional convenience.
Another ECG electrode of FIG. 2 in some embodiments has a flexible
lead across the chest or along the body that physically joins to an
operational amplifier chip of FIG. 2 in the same physical unit as
the accelerometer to detect and amplify a potential difference
between the two ECG electrodes.
[0205] Because of the micro-miniaturization of integrated circuits,
the physical sensor unit is very light in weight and readily taped
to the chest. Some further embodiments also include a miniature
microphone along with the accelerometer in the same chest-worn
physical unit for obtaining heart sound audio for parallel
processing. Various embodiments recognize a multitude of concurrent
signals that can be obtained by a single chest accelerometer and
provide rich processing to separate them while managing to get
physiologically relevant information from the multitude of signals.
Some of these signals are: Heart-rate, Activity/Motion, Respiration
and intrapleural or intra-thoracic pressure changes, Hemodynamics
(timings and amplitudes and changes), Cough, sneeze, snore, speech,
breathing sounds, and all other physiological processes,
conditions, and parameters to which the teachings herein lend
themselves. Coughing, sneezing, snoring, speech-related processes,
and breathing sounds are detected in some embodiments by
post-processing the accelerometer for acceleration patterns over
both single instances and multiple instances to distinguish body
motions due to coughing, sneezing, snoring, speech-related
processes, and breathing sounds from those of gait and respiration
and other activities.
[0206] Detecting and separating coughing, sneezing, snoring,
speech-related processes, and breathing sounds are suitably also or
alternatively provided by filtering of a microphone input and
processing to detect a pattern and/or also processing the
accelerometer in parallel to other processes described herein and
at higher frequencies. In this way, nuanced analysis of
cardiovascular, pulmonary, respiratory and other conditions is
conveniently facilitated by the data representing the perspectives
that ECG potential difference(s), chest-derived audio, and
accelerometer sensor respectively support.
[0207] Some embodiments may be called upon to estimate Pre-ejection
period PEP and contractility VC or trends therein, but lack the ECG
electrodes of FIG. 2 and have only the accelerometer sensor. This
may be the case in remote monitoring when a person is at a
residence and away from the clinic. IVCI can be measured using the
accelerometer sensor 610 as the only data source in some
embodiments based on S1-to-F1 interval in FIG. 32. Then,
recognizing a possibly temporary and not necessarily certain
relationship or correlation of PEP and IVCI, the IVCI is guardedly
either used as a proxy directly or for trends for Pre-ejection
period PEP (and for use in obtaining contractility VC) where
measurements indicate that it is sufficiently-correlated to PEP or
trend therein, or at least sufficient for monitoring variations in
PEP and VC to detect excursion conditions (departures from expected
parameter ranges) indicative of advisability of a subsequent clinic
visit. Also, previous clinical measurements on the patient with
both accelerometer and ECG may be used to calibrate a time interval
adjustment value .alpha. and scale value .beta. to estimate PEP
from IVCI according to Equation (15), where measurements indicate
that adjustment .alpha. and scale value .beta. are statistically
significant and sufficiently accurate. That adjustment .alpha. and
scale value .beta. are suitably additionally clinically measured
for the patient as a function of heart rate and any other relevant
parameter and, if similarly satisfactory, then further downloaded
as a table of values .alpha.(HR, etc.) and .beta.(HR, etc.)
representing a calibration adjustment to estimate PEP from IVCI
adjusted by such tabulated function .alpha.(HR, etc.) and
.beta.(HR, etc.) of heart rate HR, etc. That mechanism of
estimating PEP from IVCI involves training a mapping function for
each subject, recognizing that such approach is likely to be
acceptable only when the subject thereafter is to be remote from
the clinic and no other alternative will be immediately available
at the remote location.
PEP=.beta.*IVCI+.alpha. (15)
[0208] The table of values or parameter table for such functions
.alpha.(HR, etc.) and .beta.(HR, etc.) is downloaded into flash
memory for use by the signal processor that processes the
accelerometer sensor signal. In that way, PEP estimates and VC
estimates are obtained without an ECG and associated ECG electrodes
when the patient is remote from the clinic subsequently.
[0209] FIG. 34 shows the modulation of blood flow
parameters--Pre-ejection period PEP (and therefore contractility
VC) and flow amplitude PAmp--with exercise recovery. The data is
collected for 350 seconds while a subject recovers from exercise. A
first waveform traces the ECG signal. A second waveform traces the
extracted flow signal from S-G filtering of the accelerometer
Y-axis sensor signal. A third waveform shows a PEP signal derived
from the first and second waveforms. A fourth waveform shows peak
flow signal amplitude PAmp of the flow signal of the second
waveform. As can be seen from the third waveform, the PEP increases
(the heart becomes less contractile as subject recovers from
exercise). Concurrently, the peak flow signal amplitude PAmp,
interpreted as peak blood flow, decreases over time as shown in the
fourth waveform. Smaller variations are clarified in the zoomed-in
view and likely correspond to respiration modulation of the peak
blood flow and contractility. In this way, the embodiment produces
signals as in FIG. 34 for exercise recovery measurement of
Pre-ejection Period PEP and amplitude of peak blood flow PAmp.
Moreover, in some embodiments, respiration signals can be separated
from either PEP or flow peak amplitude PAmp of FIG. 34.
[0210] Some medical diagnostic device embodiments have processing
embodiments to detect blood flow using an accelerometer sensor, and
hence calculate changes in various parameters such as Stroke
Volume, contractility etc, as described herein. Deriving BCG-like
flow data from an accelerometer sensor according to embodiments is
suitably made part of post-operative recovery monitoring system
embodiments, as well as device embodiments for use with an
accelerometer for long term, continuous monitoring of a patient's
heart. Various embodiments remarkably process input from a single
accelerometer sensor and operate display and therapeutic devices on
the basis of generated signals from the processing that
electronically represent any, some or all of heart rate, body
motion, respiration, blood flow and hemodynamic parameter
signals.
[0211] In FIGS. 35A and 35B, waveforms are depicted during the
Valsalva maneuver--wherein the subject sits quietly and blows into
a mouthpiece under predetermined back-pressure of the apparatus. By
way of background, typical physiological responses expected during
the Valsalva maneuver are that aortic pressure rises from a resting
value and then falls back to it after a while. Concurrently, heart
rate falls below resting rate and then rises above resting rate.
Then breath is released after a predetermined time interval. Aortic
pressure again rises from approximately the resting value and then
falls back to it after a while. Heart rate concurrently falls below
the resting rate and then slowly increases to the resting rate.
[0212] In FIGS. 35A and 35B, actual waveforms during two different
instances of Valsalva Release phase are shown. In FIG. 35A, a first
waveform traces the filtered heart signal residue wherein the heart
rate is generally increasing, as indicated by decreasing separation
between the numerous S1 residue spikes from filtered accelerometer
Z-axis as in FIG. 31 (left side). In FIG. 35B, the first waveform
traces the blood flow signal (FIG. 31 right side), and the heart
rate is generally increasing also, as indicated by decreasing
separation between the numerous flow peaks from the flow signal in
FIG. 35B. The second waveform of each Figure represents peak
amplitude PAmp of the first waveform in each same Figure, which is
declining in both instances. The third waveform represents
declining Stroke Volume, and the fourth waveform represents
declining Cardiac Output. The waveforms appear to be consistent
with the physiology of the Valsalva maneuver.
[0213] In FIGS. 35A/35B, the SV and CO waveforms (3.sup.th,
4.sup.th) were obtained indirectly, using ModelFlow software,
responsive to a separate finger-mounted sensor using a continuous
blood pressure measurement system manufactured by Finapres Medical
Systems. The system is understood to use a non-linear Windkessel
model (described elsewhere herein) to model arterial resistance so
as to determine blood flow from continuous blood pressure
measurements. Notice that the accelerometer-derived peak amplitude
(2.sup.nd waveform) in both FIGS. 35A and 35B tracks the SV and
CO.
[0214] Accordingly, some embodiments post-process the peak
amplitude PAmp (2.sup.nd waverform in FIGS. 35A/35B) on Z-axis or
other-axis signal amplitude (which correlates well with SV and CO)
to provide or derive time-varying output signals and displays. Such
signals and displays an estimation for hemodynamic parameters such
as SV and CO and others derivable directly at FIG. 31 step 790 from
the amplitude/power of the cardiac S1 pulse either independently
of, or in combination with, information from the blood flow signal.
The estimation may differ from SV and CO themselves by an additive
constant and a scale factor, and this is likely to be acceptable
for monitoring applications such as those that begin with a
pre-existing physiological state of a subject person and are
interested in subsequent variations and/or unusual departures.
Notice that the SV and CO hemodynamic parameters vary much more
slowly with time t (e.g., less than 0.2 Hz or less than 0.1 Hz or
so) than respiratory variation in the peak amplitude signal
PAmp(t), so that SV and CO are derived from or filtered out of the
peak amplitude signal PAmp(t) in some embodiments and provided for
display and recording. Respiration is separated from the peak
amplitude signal PAmp(t) as described in FIG. 22 for instance.
Respiration, gait and other body motions are detected and separated
from each other based on an accelerometer signal as also taught
elsewhere herein and also provided for display and recording.
[0215] In FIGS. 36A and 36B, a process embodiment is represented
physically in system storage unit 630 or memory of FIG. 25 and
executed on the signal processing unit 620 of FIG. 25 or digital
signal processor DSP of FIG. 26. In FIG. 36A, Z-axis signal
processing 110-180 generally is analogous to the Z-axis processing
of FIG. 4 and Z-axis processing of FIG. 31 (left side), and outputs
heart rate at step 180 and need not be further detailed. In FIG.
36B, Y-axis signal processing 910-940, 970, 980 independently also
derives heart rate by obtaining a residue signal from the Y-axis
input with the S-G filtered signal subtracted out. Electronic peak
detection of the residue signal at step 940 is followed by peak
detection 970 and counting 980. The heart rate signal output from
step 980 is either combined with and/or compared with the heart
rate output of FIG. 36A or used instead of and without the heart
rate output of FIG. 36A, depending on embodiment.
[0216] Further in FIG. 36B, the S-G filtered signal from step 930
itself is a ringy flow signal of FIGS. 29 and 32 interpreted as
blood flow and provided as an electronic output 950 for display
650.i and optional storage 660.i. Also, as described in connection
with FIGS. 37 and 38, that flow signal 950 is further processed at
a step 960 to recover a Forcing Function F(t) as a further
electronic output indicative of cardiac function. In addition, that
flow signal is processed at a step 965 to estimate one, two, or all
of a triplet of 2.sup.nd order model parameters for mass m, dashpot
.rho. (rho), spring .gamma. (gamma) that are delivered as still
further electronic outputs. FIG. 31 steps 770, 785 and 790 are
suitably also included in FIG. 36B using the flow signal from step
930. Any or all of the outputs can be still further post-processed
into electronically-represented interpretations and displays in
FIG. 25 of the internals of the chest and heart and states of
function.
[0217] In FIGS. 37 and 38, post-processing is applied to the damped
oscillatory flow signal at 950 of FIG. 36B (derived from the Y-axis
accelerometer sensor) as from a 2nd order system model. That
2.sup.nd order system model has a forcing function F(t) (newtons)
and constant coefficient parameters for mass m, dashpot .rho.
(rho), spring .gamma. (gamma) in its 2nd order linear differential
equation of Equation (16). The variable y represents physical
displacement of the chest sensor from an average y position
(conceptually measured from some stationary point of reference on
the body, such as the hips, relative to which the chest is
displaced).
m.differential..sup.2y/.differential.t.sup.2+.rho..differential.y/.diffe-
rential.t+.gamma.y(t)=F(t) (16)
[0218] FIG. 37 models a standing individual with a first triplet of
those parameters subscripted "1." FIG. 38 models the individual
lying prone, with a second triplet of values for those parameters
subscripted "2." In both cases the accelerometer Y-axis sensor is
used, where in FIG. 37 that sensor is vertical, and in FIG. 38 that
sensor is horizontal. In both the standing and prone positions that
sensor is positioned the same on the chest, parallel to a
superior-inferior axis of symmetry of the body from head to
feet.
[0219] Note that the flow signal 950, g(i) derived by step 930 from
the Y-axis accelerometer sensor (e.g. by S-G filtering), can be
regarded as a series of samples g(t) each substantially
proportional to the second derivative
.differential..sup.2x/.differential.t.sup.2 itself in Equation
(16). The dashpot parameter .rho.introduces energy dissipation, and
the time constant .tau. of decay of the damped oscillatory signal
is related to the ratio m/.rho., meaning the mass parameter m
(kilograms) divided by the dashpot parameter .rho.
(newtons/(meters/sec)).
.tau.=m/.rho. (17)
[0220] The frequency f.sub.s of the damped oscillatory signal is
related to (1/2.pi.) {square root over ( )}(.gamma./m), i.e., the
square root of the ratio of the spring parameter .gamma.
(newtons/meter) divided by the mass parameter m (kilograms), and
that square-root result divided by 2n.
f.sub.s=(1/2.pi.) {square root over ( )}(.gamma./m) (18)
[0221] The post-processing suitably estimates F(t)/m, such as by
numerical integrations directly from the damped oscillatory flow
signal waveform from the y-axis of the accelerometer,
S(t)=.differential..sup.2y/.differential.t.sup.2 using Equation
(16) written in the form of Equation (19). The numerical
integration begins as each spindle-shaped accelerometer Y-axis
waveform commences in FIG. 29 (3rd waveform) for a given heartbeat,
and assumes that any constants of integration are zero (i.e., zero
position, zero velocity. For applications based on the shape or
morphology of the forcing function F(t), the mass m is merely a
constant of proportionality that does not affect the shape. If mass
is important to the application, the mass is taken as that of the
head and torso such as some fraction (e.g., 0.6) of the body mass
in kilograms. The time constant .tau. is numerically estimated as
the length of time from the peak of the spindle-shaped acceleration
waveform to the time when the waveform is about 2/3 dissipated
(i.e., reduced on later end of the spindle to 1/e of its earlier
peak amplitude, where e is base of natural logarithms 2.71828 . . .
). In FIG. 29, the time constant is about a quarter of a second.
The frequency f.sub.s is numerically estimated as the number of
cycles in some portion of the spindle-shaped acceleration waveform
divided by the time in seconds occupied by that portion. In FIG.
29, the frequency f.sub.s is about 8 Hertz.
F(t)/m=S(t)+(1/.tau.).intg..sub.0.sup.tS(t)dt+(2.pi.f.sub.s).sup.2.intg.-
.sub.0.sup.t.intg..sub.0.sup.tS(t)dt (19)
[0222] Alternatively, the post-processing uses any applicable
statistical time-series analysis package or procedure to recover
best statistical estimates for the forcing function and the
2.sup.nd order constant coefficient parameters.
[0223] The forcing function F.sub.Y (t) component parallel to the
Y-axis sensor may arise from a mixture of 1) physical acceleration
of the heart itself upon ventricular contraction and 2) the
acceleration of blood surging into the aorta when the blood is
expelled from the left ventricle. The parameter .gamma. for
spring-constant and parameter .rho. for dashpot seem to relate to
some gross average of mechanical properties of the interiors of
chest and abdomen. The mass parameter m probably is related or
proportional to the mass of the torso and perhaps the head, but
probably not to the mass of the legs because the legs are probably
not accelerated in the Y-axis direction. The observed S1-S1
waveform also has a rising amplitude of oscillation immediately
preceding the damped oscillation, see FIG. 32. The latter behavior
can be due to entry of blood from the venae cavae into the right
atrium, and the right ventricular contraction into the pulmonary
artery. Accordingly, embodiments for extraction and analysis of
forcing function F.sub.Y(t) can provide useful and more nearly
comprehensive information on cardiac and pulmonary function as well
as hemodynamic information.
[0224] Some embodiments are contemplated that monitor accelerometer
X-axis sensor information as well as the Y-axis and Z-axis. By
X-axis sensor is meant a sensor oriented to sense acceleration
laterally across the chest. A transverse displacement variable x
for purposes of Equation (20-X) represents side-to-side physical
displacement of the chest sensor from an average x position (or
conceptually also from a point of reference such as the center of
mass of the heart relative to which the chest is displaced.) In
such an embodiment, signal from the X-axis sensor is filtered in
parallel with the filtering of the Y-axis signal, and in a manner
for the X-axis analogous to the filtering described hereinabove for
the Y-axis signal. Because of the assymetrical location and
slantwise inclination and of the heart in the chest, the filtered
signal from the X-axis sensor provides further information about a
lateral (side-to-side) component F.sub.X(t) of the forcing function
F(t) considered as a vector. Taken together, these two forcing
function components F.sub.Y(t) and F.sub.X(t) can provide further
useful information on cardiac function, pulmonary function,
properties of the pleura, pleural cavity, and pericardium, as well
as hemodynamics information relating to the aorta, venae cavae, and
pulmonary arteries and pulmonary veins by any suitable process now
known or hereafter devised. The parameter triplets are respectively
subscripted "1Y" and "1X" to designate a standing position ("1")
and the Y-axis or X-axis sensor involved. If the prone position is
involved then the subscript "1" is changed to "2."
m.sub.1Y.differential..sup.2y/.differential.t.sup.2+.rho..sub.1Y.differe-
ntial.y/.differential.t+.gamma..sub.1Yy(t)=F.sub.1Y(t) (20-Y)
m.sub.1X.differential..sup.2x/.differential.t.sup.2+.rho..sub.1X.differe-
ntial.x/.differential.t+.gamma..sub.1Xx(t)=F.sub.1X(t). (20-X)
[0225] In FIG. 39, an embodiment is improved as in the other
Figures herein and used as one or more replicas as discussed in
connection with FIG. 25. FIG. 39 illustrates inventive integrated
circuit chips including chips 1100, 1200, 1300, 1400, 1500, and GPS
1190 (1495) for use in any one, some or all of the blocks of the
communications system 600 of FIG. 25. The skilled worker uses and
adapts the integrated circuits to the particular parts of the
communications system 600 as appropriate to the functions intended.
It is contemplated that the skilled worker uses each of the
integrated circuits shown in FIG. 39, or such selection from the
complement of blocks therein provided into appropriate other
integrated circuit chips, or provided into one single integrated
circuit chip, in a manner optimally combined or partitioned between
the chips, to the extent needed by any of the applications
supported such as voice WLAN gateway, cellular telephones,
televisions, internet audio/video devices, routers, pagers,
personal digital assistants (PDA), microcontrollers coupled to
controlled mechanisms for fixed, mobile, personal, robotic and/or
automotive use, combinations thereof, and other application
products now known or hereafter devised for increased, partitioned
or selectively determinable advantages.
[0226] In FIG. 39, an integrated circuit 1100 includes a digital
baseband (DBB) block that has a RISC processor 1105 (such as MIPS
core(s), ARM core(s), or other suitable processor) and a digital
signal processor 1110 such as from the TMS320C55x.TM. DSP
generation from Texas Instruments Incorporated or other digital
signal processor (or DSP core) 1110, communications software and
security software for any such processor or core, security
accelerators 1140, and a memory controller. Security accelerators
1140 provide additional computing power such as for hashing and
encryption that are accessible, for instance, when the integrated
circuit 1100 is operated in a security level enabling the security
accelerators block 1140 and affording types of access to the
security accelerators depending on the security level and/or
security mode. The memory controller interfaces the RISC core 1105
and the DSP core 1110 to Flash memory 1025 and SDRAM 1024
(synchronous dynamic random access memory). On chip RAM 1120 and
on-chip ROM 1130 also are accessible to the processors 1105 and
1110 for providing sequences of software instructions and data
thereto. A security logic circuit 1038 of FIGS. 16 and 17 has a
secure state machine (SSM) to provide hardware monitoring of any
tampering with security features. A Secure Demand Paging (SDP)
circuit 1040 is provided for effectively-extended secure
memory.
[0227] Digital circuitry 1150 on integrated circuit 1100 supports
and provides wireless modem interfaces for any one or more of GSM,
GPRS, EDGE, UMTS, and OFDMA/MIMO (Global System for Mobile
communications, General Packet Radio Service, Enhanced Data Rates
for Global Evolution, Universal Mobile Telecommunications System,
Orthogonal Frequency Division Multiple Access and Multiple Input
Multiple Output Antennas) wireless, with or without high speed
digital data service, via an analog baseband chip 1200 and GSM/CDMA
transmit/receive chip 1300. Digital circuitry 1150 includes a
ciphering processor CRYPT for GSM ciphering and/or other
encryption/decryption purposes. Blocks TPU (Time Processing Unit
real-time sequencer), TSP (Time Serial Port), GEA (GPRS Encryption
Algorithm block for ciphering at LLC logical link layer), RIF
(Radio Interface), and SPI (Serial Port Interface) are included in
digital circuitry 1150.
[0228] Digital circuitry 1160 provides codec for CDMA (Code
Division Multiple Access), CDMA2000, and/or WCDMA (wideband CDMA or
UMTS) wireless suitably with HSDPA/HSUPA (High Speed Downlink
Packet Access, High Speed Uplink Packet Access) (or 1xEV-DV,
1xEV-DO or 3xEV-DV) data feature via the analog baseband chip 1200
and RF GSM/CDMA chip 1300. Digital circuitry 1160 includes blocks
MRC (maximal ratio combiner for multipath symbol combining), ENC
(encryption/decryption), RX (downlink receive channel decoding,
de-interleaving, viterbi decoding and turbo decoding) and TX
(uplink transmit convolutional encoding, turbo encoding,
interleaving and channelizing.). Blocks for uplink and downlink
processes of WCDMA are provided.
[0229] Audio/voice block 1170 supports audio and voice functions
and interfacing. Speech/voice codec(s) and speech recognition are
suitably provided in memory space in audio/voice block 1170 for
processing by processor(s) 1110. An applications interface block
1180 couples the digital baseband chip 1100 to an applications
processor 1400. Also, a serial interface in block 1180 interfaces
from parallel digital buses on chip 1100 to USB (Universal Serial
Bus) of PC (personal computer) 2070. The serial interface includes
UARTs (universal asynchronous receiver/transmitter circuit) for
performing the conversion of data between parallel and serial
lines. A power resets and control module PRCM 1185 provides power
management circuitry for chip 1100. Chip 1100 is coupled to
location-determining circuitry 1190 satellite positioning such as
GPS (Global Positioning System) and/or to a network-based
positioning (triangulation) system, to an accelerometer, to a tilt
sensor, and/or other peripherals to support positioning,
position-based applications, user real-time kinematics-based
applications, and other such applications. Chip 1100 is also
coupled to a USIM (UMTS Subscriber Identity Module) 1195 or other
SIM for user insertion of an identifying plastic card, or other
storage element, or for sensing biometric information to identify
the user and activate features.
[0230] In FIG. 39, a mixed-signal integrated circuit 1200 includes
an analog baseband (ABB) block 1210 for
GSM/GPRS/EDGE/UMTS/HSDPA/HSUPA which includes SPI (Serial Port
Interface), digital-to-analog/analog-to-digital conversion DAC/ADC
block, and RF (radio frequency) Control pertaining to
GSM/GPRS/EDGE/UMTS/HSDPA/HSUPA and coupled to RF (GSM etc.) chip
1300. Block 1210 suitably provides an analogous ABB for CDMA
wireless and any associated 1xEV-DV, 1xEV-DO or 3xEV-DV data and/or
voice with its respective SPI (Serial Port Interface),
digital-to-analog conversion DAC/ADC block, and RF Control
pertaining to CDMA and coupled to RF (CDMA) chip 1300.
[0231] An audio block 1220 has audio I/O (input/output) circuits to
a speaker 1222, a microphone 1224, and headphones (not shown).
Audio block 1220 has an analog-to-digital converter (ADC) coupled
to an audio/voice codec 1170 and a stereo DAC (digital to analog
converter) for a signal path to the baseband block 1210 and with
suitable encryption/decryption. A control interface 1230 has a
primary host interface (I/F) and a secondary host interface to
DBB-related integrated circuit 1100 of FIG. 39 for the respective
GSM and CDMA paths. The integrated circuit 1200 is also interfaced
to an I2C port of applications processor chip 1400 of FIG. 39.
Control interface 1230 is also coupled via circuitry to interfaces
in circuits 1250 and the baseband 1210. A power conversion block
1240 includes buck voltage conversion circuitry for DC-to-DC
conversion, and low-dropout (LDO) voltage regulators for power
management/sleep mode of respective parts of the chip regulated by
the LDOs. Power conversion block 1240 provides information to and
is responsive to a power control state machine between the power
conversion block 1240 and circuits 1250. Power management circuitry
PRCM 1185 (1470) is coupled with and controls power conversion
block 1240 and interfaces to GPS 1190 (1495) and to power save mode
controller 2130 (2290) in systems of FIGS. 1-39 and as described
elsewhere herein. Circuits 1250 provide oscillator circuitry for
clocking chip 1200. The oscillators have frequencies determined by
one or more crystals 1290. Circuits 1250 include a RTC real time
clock (time/date functions), general purpose I/O, a vibrator drive
(supplement to cell phone ringing features), and a USB On-The-Go
(OTG) transceiver. A touch screen interface 1260 is coupled to a
touch screen XY 1266 off-chip. Batteries such as a lithium-ion
battery 1280 and backup battery and recharger provide power to the
system and battery data to circuit 1250 on suitably provided
separate lines from the battery pack. When needed, the battery 1280
also receives charging current from a Charge Controller in analog
circuit 1250 which includes MADC (Monitoring ADC and analog input
multiplexer such as for on-chip charging voltage and current, and
battery voltage lines, and off-chip battery voltage, current,
temperature) under control of the power control state machine.
Battery monitoring is provided by either or both of 1-Wire and/or
an interface called HDQ.
[0232] In FIG. 39 an RF integrated circuit 1300 includes a
GSM/GPRS/EDGE/UMTS/CDMA RF transmitter block 1310 supported by
oscillator circuitry with crystal(s) 1290. Transmitter block 1310
is fed by basebands block 1210 of chip 1200. Transmitter block 1310
drives a dual band RF power amplifier (PA) 1330. On-chip voltage
regulators maintain appropriate voltage under conditions of varying
power usage. Off-chip switchplexer 1350 couples wireless antenna
and switch circuitry to both the transmit portion 1310, 1330 and
the receive portion next described. Switchplexer 1350 is coupled
via band-pass filters 1360 to receiving LNAs (low noise amplifiers)
for 850/900 MHz, 1800 MHz, 1900 MHz and other frequency bands as
appropriate. Depending on the band in use, the output of LNAs
couples to GSM/GPRS/EDGE/UMTS/CDMA demodulator 1370 to produce the
I/Q or other outputs thereof (in-phase, quadrature) to the
GSM/GPRS/EDGE/UMTS/CDMA basebands block 1210.
[0233] Further in FIG. 39, an integrated circuit chip or core 1400
is provided for applications processing and more off-chip
peripherals. Chip (or core) 1400 has interface circuit 1410
including a high-speed WLAN 802.11a/b/g interface coupled to a WLAN
chip 1500. Further provided on chip 1400 is an applications
processing section 1420 which includes a RISC processor 1422 (such
as MIPS core(s), ARM core(s), or other suitable processor), a
digital signal processor (DSP) 1424 such as from the TMS320C55x.TM.
DSP generation and/or the TMS320C6x.TM. DSP generation from Texas
Instruments Incorporated or other digital signal processor(s), and
a shared memory controller MEM CTRL 1426 with DMA (direct memory
access), and a 2D (two-dimensional display) graphic accelerator.
Speech/voice codec/speech recognition functionality is suitably
processed in chip 1400, in chip 1100, or both chips 1400 and
1100.
[0234] The RISC processor 1422 and the DSP 1424 in section 1420
have access via an on-chip extended memory interface (EMIF/CF) to
off-chip memory resources 1435 including as appropriate, mobile DDR
(double data rate) DRAM, and flash memory of any of NAND Flash, NOR
Flash, and Compact Flash. On chip 1400, a shared memory controller
1426 in circuitry 1420 interfaces the RISC processor 1420 and the
DSP 1424 via an on-chip bus to on-chip memory 1440 with RAM and
ROM. A 2D graphic accelerator is coupled to frame buffer internal
SRAM (static random access memory) in block 1440. A security block
1450 includes an SSM analogous to SSM 1038 of FIG. 1, and includes
secure hardware accelerators having security features and provided
for secure demand paging 1040 and for accelerating encryption and
decryption. A random number generator RNG is provided in security
block 1450.
[0235] On-chip peripherals and additional interfaces 1410 include
UART data interface and MCSI (Multi-Channel Serial Interface) voice
and data wireless interface for an off-chip IEEE 802.15 (Bluetooth
and low and high rate piconet, Zigbee, and personal network
communications) wireless circuit 1430. The Bluetooth or Zigbee
wireless interface is useful for receiving from and controlling the
accelerometer sensor and its associated analog circuitry and
digital to analog-to-digital converter ADC in FIGS. 1 and 26, among
other Figures. In arrangements including ECG electrodes and/or a
chest microphone, the analog circuitry at the taped-on sensor unit
also includes couplings from such pickup elements to the Bluetooth
or Zigbee short distance transceiver from the chest sensor (e.g.
FIGS. 40A/40B communicating with a counterpart short distance
transceiver at the interface 1410.
[0236] Debug messaging and serial interfacing are also available
through the UART. A JTAG emulation interface couples to an off-chip
emulator Debugger for test and debug. GPS 1190 (1495) is scannable
by the debugger, see FIG. 2. Further in peripherals 1410 are an I2C
interface to analog baseband ABB chip 1200, and an interface to
applications interface 1180 of integrated circuit chip 1100 having
digital baseband DBB.
[0237] Interface 1410 includes a MCSI voice interface, a UART
interface for controls and data to position unit GPS 1495 and
otherwise, and a multi-channel buffered serial port (McBSP) for
data. Timers, interrupt controller, and RTC (real time clock)
circuitry are provided in chip 1400. Further in peripherals 1410
are a MicroWire (u-wire 4 channel serial port) and multi-channel
buffered serial port (McBSP) to Audio codec, a touch-screen
controller (or coupling to 1260), and audio amplifier 1480 to
stereo speakers.
[0238] External audio content and touch screen (in/out) 1260, 1266
and LCD (liquid crystal display), organic semiconductor display,
and DLP.TM. digital light processor display from Texas Instruments
Incorporated, are suitably provided in various embodiments and
coupled to interface 1410. In vehicular use, such as at unit 690 of
FIG. 25, the display is suitably any of these types provided in the
vehicle, and sound is provided through loudspeakers, headphones or
other audio transducers provided in the vehicle. In some vehicles a
transparent organic semiconductor display 2095 of FIG. 16 is
provided on one or more windows of a vehicle and wirelessly or
wireline-coupled to the video feed. Maps and visual position-based
interactive imaging and user kinematics applications are provided
using double-integrated accelerometer output as discussed elsewhere
herein. Also GPS 1190 (1495) and processor 1105, 1110 (1422, 1424)
support fixed, portable, mobile, vehicular and other platforms.
[0239] Interface 1410 additionally has an on-chip USB OTG interface
that couples to off-chip Host and Client devices. These USB
communications are suitably directed outside handset 2010 such as
to PC 2070 (personal computer) and/or from PC 2070 to update the
handset 2010 or to a camera 1490.
[0240] An on-chip UART/IrDA (infrared data) interface in interfaces
1410 couples to off-chip GPS (global positioning system of block
1495 cooperating with or instead of GPS 1190) and Fast IrDA
infrared wireless communications device. An interface provides EMT9
and Camera interfacing to one or more off-chip still cameras or
video cameras 1490, and/or to a CMOS sensor of radiant energy. Such
cameras and other apparatus all have additional processing
performed with greater speed and efficiency in the cameras and
apparatus and in mobile devices coupled to them with improvements
as described herein. Further in FIG. 39, an on-chip LCD controller
or DLP.TM. controller and associated PWL (Pulse-Width Light) block
in interfaces 1410 are coupled to a color LCD display or DLP.TM.
display and its LCD light controller off-chip and/or DLP.TM.
digital light processor display.
[0241] Further, on-chip interfaces 1410 are respectively provided
for off-chip keypad and GPIO (general purpose input/output).
On-chip LPG (LED Pulse Generator) and PWT (Pulse-Width Tone)
interfaces are respectively provided for off-chip LED and buzzer
peripherals. On-chip MMC/SD multimedia and flash interfaces are
provided for off-chip MMC Flash card, SD flash card and SDIO
peripherals. On chip 1400, a power, resets, and control module PRCM
1470 supervises and controls power consuming blocks and sequences
them, and coordinates with PRCM 1185 on chip 1100 and with Power
Save Mode Controller 2130 (2290) in GPS 1495 as described elsewhere
herein.
[0242] In FIG. 39, a WLAN integrated circuit 1500 includes MAC
(media access controller) 1510, PHY (physical layer) 1520 and AFE
(analog front end) 1530 for use in various WLAN and UMA (Unlicensed
Mobile Access) modem applications. In some embodiments, GPS 1495
operates in close coordination with any one, some, or all of WLAN,
WiMax, DVB, or other network, to provide positioning,
position-based, and user real-time kinematics applications. Still
other additional wireless interfaces such as for wideband wireless
such as IEEE 802.16 WiMAX mesh networking and other standards are
suitably provided and coupled to the applications processor
integrated circuit 1400 and other processors in the system. WiMax
has MAC and PHY processes and the illustration of blocks 1510 and
1520 for WLAN indicates the relative positions of the MAC and PHY
blocks for WiMax.
[0243] In FIG. 39, a further digital video integrated circuit 1610
is coupled with a television antenna 1615 (and/or coupling
circuitry to share antenna 1015 and/or 1545 and/or 2105) to provide
television antenna tuning, antenna selection, filtering, RF input
stage for recovering video/audio/controls from television
transmitter (e.g., DVB station 2020 of FIG. 16). Digital video
integrated circuit 1610 in some embodiments has an integrated
analog-to-digital converter ADC on-chip, and in some other
embodiments feeds analog to ABB chip 1200 for conversion by an ADC
on ABB chip 1200. The ADC supplies a digital output 1619 to
interfaces 1410 of applications processor chip 1400 either directly
from chip 1610 or indirectly from chip 1610 via the ADC on ABB chip
1200. Controls for chip 1610 are provided on lines 1625 from
interfaces 1410. Applications processor chip 1400 includes a
digital video block 1620 coupled to interface 1410 and having a
configurable adjustable shared-memory telecommunications signal
processing chain such as Doppler/MPE-FEC. A processor on chip 1400
such as RISC processor 1422 and/or DSP 1424 configures, supervises
and controls the operations of the digital video block 1620.
[0244] In combination with the GPS circuit 1190 and/or 1495, and
video display 1266 or LCD, the RISC processor 1105/1422 and/or DSP
1110 (1424) support location-based embodiments and services of
various types, such as roadmaps and directions thereon to a
destination, pictorials of nearby commercial establishments,
offices, and residences of friends, various family supervision
applications, position sending to friends or to emergency E911
service, and other location based services now known or yet to be
devised.
[0245] Digital signal processor cores suitable for some embodiments
in the IVA block and video codec block may include a Texas
Instruments TMS32055x.TM. series digital signal processor with low
power dissipation, and/or TMS320C6000 series and/or TMS320C64x.TM.
series VLIW digital signal processor, and have the circuitry and
processes of the FIGS. 1-39 coupled with them as taught herein. A
camera CAM provides video pickup for a cell phone or other device
to send over the internet to another cell phone, personal digital
assistant/personal entertainment unit, gateway and/or set top box
STB with television TV.
[0246] FIGS. 40A and 40B are respective broadside and
cross-sectional views of an accelerometer sensor 210 and
transmitter, transceiver, or transponder chip 212 firmly mounted on
a thin, resilient plastic support plate 214 that can be firmly
affixed by an adhesive tape 216 to the chest. The electronics is
conveniently light-weight and small and may be quarter-sized,
dime-sized or even smaller in size. In FIG. 40A, a dotted outline
shows a round smoothed or flanged periphery of plastic support 214
shaped for comfort on the chest.
[0247] In FIG. 40B, the Z-axis of accelerometer sensor 210 is
perpendicular to the plane defined by plastic support 214 (and to
the plane defined by chip 212). The Y-axis and X-axis of
sensitivity to acceleration of the accelerometer sensor 210 are
perpendicular to each other, with each parallel to the plane
defined by a broadside of a package enclosing the accelerometer and
likewise parallel to a plane defined by plastic support 214.
Adhesive tape 216 adheres to the outward broad side of plastic
support 214, thereby holding plastic support 214 firmly against the
chest when applied thereto. Adhesive tape 216 has an inner edge 217
defining an approximately square aperture in FIG. 40A that admits
the outward-placed transponder chip 212 and accelerometer sensor
210.
[0248] An ECG sensor of FIG. 2 and/or a small microphone may also
be mounted on plastic support 214 to monitor chest potential and/or
chest sounds. The chest-adjacent side of plastic support 214 may
also be provided with ECG electrode paste for ECG connectivity with
the chest.
[0249] In some embodiments, chip 212 harvests power from an
interrogation signal from the circuitry of FIG. 39, and in other
embodiments a small battery is also provided on plastic support 214
and electrically connected to supply a low power to chip 212.
Accelerometer sensor 210 is electrically coupled to transponder
chip 212 along with any ECG electrode and microphone elements for
wireless communication to the system of FIG. 39. In various
embodiments, none, one, some or all of the blocks of FIG. 1 are
provided as part of transponder chip 212. Chip 212 in some
embodiments includes a very low power processor such as an
MSP430.TM. processor from Texas Instruments Incorporated or other
such processor along with the short distance wireless transmitter.
Chip 212 can have an antenna such as a spiral antenna fabricated as
part of the chip 212, or in some other embodiments an antenna is
suitably provided as part of plastic support 214 and electrically
connected to chip 212. Optionally, a plastic cap or header
physically encloses and protects the chips over the support 214.
Also, in some embodiments, a wireline interface is also provided in
chip 212, and the support 214 physically has a miniature wireline
female connector attached thereto and electrically connected to the
wireline interface in chip 212, such as for USB (Universal Serial
Bus). In that way, a clinician may connect a lightweight male
connector from a monitoring processor and display unit to the
miniature wireline female connector and bypass the short distance
wireless function of chip 212 at will. In still other embodiments,
the accelerometer 210 and transponder 212 are mounted in a
pacemaker that is either implanted in the patient or affixed to the
chest.
[0250] In FIGS. 40A and 40B, the orientation of the X, Y, and Z
axes of the accelerometer sensor on the chest may vary depending on
actual placement and actual physical manufacture. Actual
orientation of the accelerometer sensor on the chest may vary
because of convenience for categories of patients or particular
patients or simply due to inadvertent mis-orientation of the
sensor. However, physical orientation of the multiple axis
accelerometer merely distributes the overall physical acceleration
vector a to be sensed to the different sensor axes of the
accelerometer according to their vector components in the various
axis directions.
[0251] Accordingly, some embodiments as in FIG. 41 include an
electronic processing module 990.i for virtual re-orientation or
optimization of the accelerometer sensor signals by applying a
rotation of axes that introduces a multiplication by a rotation
matrix to the signals. Such rotation of axes 990.i combines the
signals for X, Y, Z accelerometer axes, such as shown for those
axes in FIG. 42, according to linear combinations of the signals as
if the accelerometer axes were rotated, as shown by Equation
(21).
[0252] Let an angle .theta. represent an angle by which the
accelerometer Y-axis sensor is to be virtually rotated from its
affixed position on the chest to align with the foot-to-head
direction on the body or for whatever purpose. Let an angle .phi.
represent an angle by which the accelerometer Z-axis sensor is to
be virtually rotated from its affixed position approximately
perpendicular to the chest toward that foot-to-head direction on
the body. Let a vector V represent the Z-axis signal, the Y-axis
signal and the X-axis signal. Vector V of these signals is matrix
multiplied electronically in FIG. 41 according to the rotation
product R*V, using rotation matrix R expressed by Equation
(21).
R = [ cos .PHI. ( sin .PHI. sin .theta. ) ( sin .PHI. cos .theta. )
0 cos .theta. - sin .theta. - sin .PHI. ( cos .PHI. sin .theta. ) (
cos .PHI.cos .theta. ) ] ( 21 ) ##EQU00004##
[0253] In various embodiments, the axis rotations are suitably
customized by the processing for the type of signal output (e.g.,
blood flow, heart sounds) which is to be maximized for a given
purpose. The angles .theta. and .phi. are each varied by a given
feedback control circuit 995.i to maximize the desired type of
signal output to which that feedback control circuit is
applied.
[0254] In FIG. 41, for instance, some embodiments execute a
feedback control 995.1 to thus maximize the heart sound signal for
the heart monitoring path, and the axes-rotation parameters for the
rotation process 990.1 for a feedback loop 990.1, 130, 140, . . . ,
995.1 established either as a configuration routine before
run-time, or dynamically at run-time. The feedback loop rotates the
axes to deliver a linear combination of Z-axis and Y-axis (and
X-axis can also be useful) as an input in place of the raw Z-axis
signal in FIGS. 31 and 36A to the Savitzky-Golay polynomial filter
130 for the Z-axis to maximize the amplitude of the S1 peaks at the
output of the Folded Correlation, for heart sound and heart rate
monitoring purposes.
[0255] Analogously, in FIG. 41, some embodiments additionally or
alternatively execute a feedback loop 990.2, 930, . . . , 995.2 by
independently rotating axes to deliver a linear combination of
X-axis and Y-axis (and Z-axis can also be useful) as an input in
place of the raw Y-axis signal in FIGS. 31 and 36B to the
Savitzky-Golay polynomial filter 930 for the Y-axis, to maximize
the blood flow signal peak amplitude PAmp of FIGS. 29 and 32. To
save some processing, some embodiments can perform the blood flow
axes-rotation on the X and Y axes only (.phi.=0 in Equation (21))
for the blood flow signal, see Equation (22).
R ( .PHI. = 0 ) = [ 1 0 0 0 cos .theta. - sin .theta. 0 sin .theta.
cos .theta. ] ( 22 ) ##EQU00005##
[0256] Note that the rotation matrix R of Equation (21) is the
product of a tilt matrix M of Equation (23) with the XY rotation
matrix of Equation (22):
M = [ cos .PHI. 0 sin .PHI. 0 1 0 - sin .PHI. 0 cos .PHI. ] ( 23 )
##EQU00006##
[0257] In another way to save some processing, some embodiments can
use one rotation 990 and one feedback control 995 operating in
response to signals jointly, like heart sounds amplitude and/or
blood flow signal amplitude. Various modes of operation and
configuration can be activated or disabled by means of one or more
control registers with bits or bit fields for the various
operations and configurations. A manual mode, if activated, can
override the feedback controls and let a clinician manually
optimize the virtual rotations while examining signals like those
of FIG. 29 on the computer display of FIG. 25.
[0258] Some embodiments also include an electronic compass
physically included into the assembly of FIGS. 40A, 40B for
supporting location-based services by the sensor assembly. An
e-compass and signals therefrom are provided, calibrated and
processed using the teachings of US patent application "Processes
for More Accurately Calibrating E-Compass for Tilt Error, Circuits,
and Systems" Ser. No. 12/398,696 (TI-65997) filed Mar. 5, 2009, and
which is incorporated herein by reference in its entirety.
[0259] FIG. 42 shows four concurrent waveforms including reference
ECG, acceleration along the dorso-ventral axis (Z-axis),
acceleration along the superior-inferior axis (Y-axis) and
acceleration along the dextro-sinistral axis (X-axis). Notice the
relatively prominent heart peaks in the Z-axis and X-axis waveforms
and the relatively prominent spindle-shaped oscillatory blood flow
component in the Y-axis waveform. The latter three acceleration
signals for X, Y, Z accelerometer axes are suitably applied in the
circuit FIG. 41 and circuits and processes of any other Figures
that can benefit from use of signals from two or three of the
accelerometer axes. Some further embodiments provide circuitry
and/or firmware that fuses hemodynamic and acoustic signatures from
multiple-axis signals and/or inter-axis cross-talk using approaches
like blind source separation, principal component analysis PCA
and/or independent component analysis ICA.
[0260] Various embodiments as described herein are manufactured in
a process that prepares a particular design and printed wiring
board (PWB) of the system unit and has an applications processor
coupled to a modem, together with one or more peripherals coupled
to the processor and a user interface coupled to the processor or
not, as the case may be. A storage, such as SDRAM and Flash memory
is coupled to the system (e.g., FIG. 39) and has tables,
configuration and parameters and an operating system OS, protected
applications (PPAs and PAs), and other supervisory software. System
testing tests operations of the integrated circuit(s) and system in
actual application for efficiency and satisfactory operation of
fixed or mobile video display for continuity of data transfer and
content, display and other user interface operation and other such
operation that is apparent to the human user and can be evaluated
by system use. If further increased efficiency is called for,
parameter(s) are reconfigured for further testing. Adjusted
parameter(s) are loaded into the Flash memory or otherwise,
components are assembled on PWB to produce resulting system
units.
[0261] The electronic monitoring devices and processing described
herein is suitably supported by any one or more of RISC (reduced
instruction set computing), CISC (complex instruction set
computing), DSP (digital signal processors), microcontrollers, PC
(personal computer) main microprocessors, math coprocessors, VLIW
(very long instruction word), SIMD (single instruction multiple
data) and MIMD (multiple instruction multiple data) processors and
coprocessors as cores or standalone integrated circuits, and in
other integrated circuits and arrays. Other types of integrated
circuits are applied, such as ASICs (application specific
integrated circuits) and gate arrays and all circuits to which the
advantages of the improvements described herein commend their
use.
[0262] In addition to inventive structures, devices, apparatus and
systems, processes are represented and described using any and all
of the block diagrams, logic diagrams, and flow diagrams herein.
Block diagram blocks are used to represent both structures as
understood by those of ordinary skill in the art as well as process
steps and portions of process flows. Similarly, logic elements in
the diagrams represent both electronic structures and process steps
and portions of process flows. Flow diagram symbols herein
represent process steps and portions of process flows in software
and hardware embodiments as well as portions of structure in
various embodiments of the invention.
ASPECTS
See Notes Paragraph at End of this Aspects Section
[0263] Notes about Aspects above: Aspects are paragraphs which
might be offered as claims in patent prosecution. The above
dependently-written Aspects have leading digits and internal
dependency designations to indicate the claims or aspects to which
they pertain. Aspects having no internal dependency designations
have leading digits and alphanumerics to indicate the position in
the ordering of claims at which they might be situated if offered
as claims in prosecution.
[0264] Processing circuitry comprehends digital, analog and mixed
signal (digital/analog) integrated circuits, ASIC circuits, PALs,
PLAs, decoders, memories, and programmable and nonprogrammable
processors, microcontrollers and other circuitry. Internal and
external couplings and connections can be ohmic, capacitive,
inductive, photonic, and direct or indirect via intervening
circuits or otherwise as desirable. Process diagrams herein are
representative of flow diagrams for operations of any embodiments
whether of hardware, software, or firmware, and processes of
manufacture thereof. Flow diagrams and block diagrams are each
interpretable as representing structure and/or process. While this
invention has been described with reference to illustrative
embodiments, this description is not to be construed in a limiting
sense. Various modifications and combinations of the illustrative
embodiments, as well as other embodiments of the invention may be
made. The terms including, includes, having, has, with, or variants
thereof are used in the detailed description and/or the claims to
denote non-exhaustive inclusion in a manner similar to the term
comprising. The appended claims and their equivalents should be
interpreted to cover any such embodiments, modifications, and
embodiments as fall within the scope of the invention.
* * * * *