U.S. patent application number 10/786359 was filed with the patent office on 2005-05-12 for using chest velocity to process physiological signals to remove chest compression artifacts.
Invention is credited to Freeman, Gary A., Geheb, Frederick, Tan, Qing.
Application Number | 20050101889 10/786359 |
Document ID | / |
Family ID | 38156737 |
Filed Date | 2005-05-12 |
United States Patent
Application |
20050101889 |
Kind Code |
A1 |
Freeman, Gary A. ; et
al. |
May 12, 2005 |
Using chest velocity to process physiological signals to remove
chest compression artifacts
Abstract
A method of analyzing a physiological (e.g., an ECG) signal
during application of chest compressions. The method includes
acquiring a physiological signal during application of chest
compressions; acquiring the output of a sensor from which
information on the velocity of chest compressions can be
determined; and using the information on the velocity to reduce at
least one signal artifact in the physiological signal resulting
from the chest compressions.
Inventors: |
Freeman, Gary A.; (Newton
Center, MA) ; Tan, Qing; (Somerville, MA) ;
Geheb, Frederick; (Danvers, MA) |
Correspondence
Address: |
FISH & RICHARDSON PC
225 FRANKLIN ST
BOSTON
MA
02110
US
|
Family ID: |
38156737 |
Appl. No.: |
10/786359 |
Filed: |
February 24, 2004 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
10786359 |
Feb 24, 2004 |
|
|
|
10704366 |
Nov 6, 2003 |
|
|
|
Current U.S.
Class: |
601/41 |
Current CPC
Class: |
A61N 1/0492 20130101;
A61H 31/005 20130101; A61H 2201/5048 20130101; A61H 2201/5084
20130101; A61B 5/316 20210101; A61N 1/3925 20130101; A61H 2201/5043
20130101; A61B 5/11 20130101; A61B 5/4836 20130101; A61B 5/14551
20130101; A61B 5/721 20130101; A61H 2201/5058 20130101; A61H
2201/5079 20130101; A61B 5/361 20210101; A61N 1/3987 20130101; A61H
31/007 20130101; A61B 5/0205 20130101; A61B 5/4848 20130101; A61H
31/006 20130101; A61N 1/046 20130101; A61B 5/7207 20130101; A61B
5/318 20210101; A61H 2230/04 20130101; A61N 1/3993 20130101 |
Class at
Publication: |
601/041 |
International
Class: |
A61H 031/00 |
Claims
1. A method of analyzing a physiological signal during application
of chest compressions, the method comprising: acquiring a
physiological signal during application of chest compressions;
acquiring the output of a sensor from which information on the
velocity of chest compressions can be determined; and using the
information on the velocity to reduce at least one signal artifact
in the physiological signal resulting from the chest
compressions.
2. The method of claim 1 wherein the physiological signal is an ECG
signal.
3. The method of claim 1 wherein the physiological signal is an IPG
signal.
4. The method of claim 1 wherein the physiological signal is an ICG
signal.
5. The method of claim 1 wherein the physiological signal is a
pulse oximetry signal.
7. The method of claim 1 or 2 wherein the sensor is a velocity
sensor, and the information on the velocity is determined from the
velocity sensor.
8. The method of claim 1 or 2 wherein the sensor is an
accelerometer, and the information on the velocity is determined
from integration of the output of the accelerometer.
9. The method of claim 1 or 2 wherein using the information on the
velocity to reduce at least one signal artifact in the
physiological signal comprises time aligning the physiological
signal with the velocity.
10. The method of claim 1 or 2 wherein using the information on the
velocity to reduce at least one signal artifact in the
physiological signal comprises using an adaptive filter that is
adjusted to remove chest compression artifacts.
11. The method of claim 1 or 2 further comprising a ventricular
fibrillation detection algorithm for processing the physiological
signal with reduced artifact to estimate whether a ventricular
fibrillation is present.
12. The method of claim 10 further comprising a preprocessing step
that detects when chest compressions are applied and automatically
initiates the adaptive filter.
13. The method of claim 11 further comprising enabling delivery of
a defibrillation shock if the algorithm estimates that ventricular
fibrillation is present.
14. The method of claim 10 wherein a difference signal is produced,
the difference signal being representative of the difference
between the physiological signal fed into the adaptive filter and
the physiological signal after artifact reduction by the adaptive
filter.
15. The method of claim 14 wherein the difference signal provides a
measure of the amount of artifact in the physiological signal.
16. The method of claim 15 further comprising the step of using the
difference signal to modify the subsequent processing of the
physiological signal.
17. The method of claim 16 wherein, if the difference signal
indicates that the amount of artifact exceeds a first threshold,
the ventricular fibrillation detection algorithm is modified to
make it more resistant to being influenced by the artifact.
18. The method of claim 17 wherein, if the difference signal
indicates that the amount of artifact exceeds a second threshold
higher than the first threshold, use of the ventricular
defibrillation detection algorithm is suspended.
19. The method of claim 16 wherein spectral analysis is performed
on the difference signal, and adjustments are made to filtering of
the physiological signal based on the outcome of the spectral
analysis.
20. The method of claim 10 wherein the velocity signal undergoes a
normalization pre-processing prior to being fed to an adaptive
filter
21. The method of claim 10 wherein the adaptive filter comprises an
FIR filter.
22. The method of claim 21 wherein the adaptive filter comprises a
zero-th order filter.
23. The method of claim 10 wherein the adaptive filter comprises
coefficients that are dynamically controlled by an estimate of the
physiological signal.
24. The method of claim 10 wherein the adaptive filter comprises
the capability of being automatically reset when the difference
between the filter output and the measured physiological signal is
beyond a threshold.
25. The method of claim 24 wherein the automatic reset comprises
the capability of dynamically changing the step size and thus
improving the relationship of convergence and stability of the
filter.
26. The method of claim 1 or 2 further comprising a time-aligning
process performed on the physiological and velocity signals,
wherein the time aligning process aligns the two signals relative
to the compressions.
27. The method of claim 26 further comprising adaptive filtering of
the output of the time aligning process, wherein the adaptive
filtering reduces the error between the physiological and velocity
signals.
28. The method of claim 10 wherein the adaptive filter comprises a
Kalman filter.
29. The method of claim 10 wherein the adaptive filter employs
adaptive equalization.
Description
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation-in-part of and claims
priority from U.S. application Ser. No. 10/704,366, filed on Nov.
6, 2003, and hereby incorporated by reference.
TECHNICAL FIELD
[0002] This invention relates to devices for assisting cardiac
resuscitation.
BACKGROUND
[0003] Resuscitation treatments for patients suffering from cardiac
arrest generally include clearing and opening the patient's airway,
providing rescue breathing for the patient, and applying chest
compressions to provide blood flow to the victim's heart, brain and
other vital organs. If the patient has a shockable heart rhythm,
resuscitation also may include defibrillation therapy. The term
basic life support (BLS) involves all the following elements:
initial assessment; airway maintenance; expired air ventilation
(rescue breathing); and chest compression. When all three (airway
breathing, and circulation, including chest compressions) are
combined, the term cardiopulmonary resuscitation (CPR) is used.
[0004] Current automated ECG rhythm analysis methods interrupt
cardiopulmonary resuscitation (CPR) to avoid artifacts in the ECG
resulting from chest compressions. Long interruptions of CPR have
been shown to result in higher failure rate of resuscitation.
Studies have reported that the discontinuation of precordial
compression can significantly reduce the recovery rate of
spontaneous circulation and the 24-hour survival rate. Y. Sato, M
H. Weil, S. Sun, W. Tang, J. xie, M. Noc, and J. Bisera, Adverse
effects of interrupting precordial compression during
cardiopulmonary resuscitation, Critical Care Medicine, Vol. 25(5),
733-736 (1997). Yu et al., 2002. Circulation, 106, 368-372 (2002),
T. Eftestol, K. Sunde, and P A. Steen, Effects of Interrupting
Precordial Compressions on the Calculated Probability of
Defibrillation Success During Out-of-Hospital Cardiac Arrest,
Circulation, 105, 2270-2273, (2002). Management of breathing is
another important aspect of the CPR process. Typical methods of
monitoring breathing employ some form of impedance pneumography
which measure and track changes in the transthoracic impedance of
the patient. Currently, however, chest compressions result in
significant artifact on the impedance signals, resulting in
impedance-based pneumographic techniques as unreliable indicators
of lung volume during chest compressions.
[0005] Adaptive filters have been attempted as a way of removing
chest-compression artifacts in the ECG signal. S O. Aase, T.
Eftestol, J H. Husoy, K. Sunde, and P A. Steen, CPR Artifact
Removal from Human ECG Using Optimal Multichannel Filtering, IEEE
Transactions on Biomedical Engineering, Vol. 47, 1440-1449, (2000).
A. Langhelle, T. Eftestol, H. Myklebust, M. Eriksen, B T. Holten, P
A. Steen, Reducing CPR Artifacts in Ventricular Fibrillation in
Vitro. Resuscitation. Mar; 48(3):279-91 (2001). J H. Husoy, J.
Eilevstjonn, T. Eftestol, S O. Aase, H Myklebust, and P A. Steen,
Removal of Cardiopulmonary Resuscitation Artifacts from Human ECG
Using an Efficient Matching Pursuit-Like Algorithm, IEEE
Transactions on Biomedical Engineering, Vol 49, 1287-1298, (2002).
H R. Halperin, and R D. Berger, CPR Chest Compression Monitor, U.S.
Pat. No. 6,390,996 (2002). Aase et al. (2000) and Langhelle et al.
(2001) used the compression depth and thorax impedance as reference
signals for their adaptive filter. Husoy et al. (2002) extended
this study by using a matching pursuit iteration to reduce the
computational complexity; however, their results are usually
computationally intensive, such as involving the calculation of a
high order inverse filter. Halperin et al. (2002) proposed a
frequency-domain approach using the auto- and the cross-spectrum of
the signals and a time-domain approach using a recursive least
square method for adaptive filtering the ECG signal. In both
approaches, intensive computations are required.
[0006] There are numerous references available on adaptive filters.
E.g., S. Haykin, Adaptive Filter Theory, Third Edition, Upper
Saddle River, N.J., USA. Prentice-Hall, 1996
SUMMARY
[0007] In general the invention features a method of analyzing a
physiological (e.g., an ECG) signal during application of chest
compressions. The method includes acquiring a physiological signal
during application of chest compressions; acquiring the output of a
sensor from which information on the velocity of chest compressions
can be determined; and using the information on the velocity to
reduce at least one signal artifact in the physiological signal
resulting from the chest compressions.
[0008] Preferred implementations of the invention may incorporate
one or more of the following: The physiological signal may be any
of a variety of physiological signals, including an ECG signal, an
IPG signal, an ICG signal, or a pulse oximetry signal. The sensor
may be a velocity sensor, and the information on the velocity may
be determined from the velocity sensor. The sensor may be an
accelerometer, and the information on the velocity may be
determined from integration of the output of the accelerometer.
Using the information on the velocity to reduce at least one signal
artifact in the physiological signal may comprise time aligning the
physiological signal with the velocity. Using the information on
the velocity to reduce at least one signal artifact in the
physiological signal may comprise using an adaptive filter that may
be adjusted to remove chest compression artifacts. The method may
include a ventricular fibrillation detection algorithm for
processing the physiological signal with reduced artifact to
estimate whether a ventricular fibrillation may be present. The
method may include a preprocessing step that detects when chest
compressions are applied and automatically initiates the adaptive
filter. The method may include enabling delivery of a
defibrillation shock if the algorithm estimates that ventricular
fibrillation is present. A difference signal may be produced, the
difference signal being representative of the difference between
the physiological signal fed into the adaptive filter and the
physiological signal after artifact reduction by the adaptive
filter. The difference signal may provide a measure of the amount
of artifact in the physiological signal. The difference signal may
be used to modify the subsequent processing of the physiological
signal. If the difference signal indicates that the amount of
artifact exceeds a first threshold, the ventricular fibrillation
detection algorithm may be modified to make it more resistant to
being influenced by the artifact. If the difference signal
indicates that the amount of artifact exceeds a second threshold
higher than the first threshold, use of the ventricular
defibrillation detection algorithm may be suspended. Spectral
analysis may be performed on the difference signal, and adjustments
may be made to filtering of the physiological signal based on the
outcome of the spectral analysis. The velocity signal may undergo a
normalization pre-processing prior to being fed to an adaptive
filter. The adaptive filter may include an FIR filter. The adaptive
filter may include a zero-th order filter. The adaptive filter may
have coefficients that are dynamically controlled by an estimate of
the physiological signal. The adaptive filter may have the
capability of being automatically reset when the difference between
the filter output and the measured physiological signal is beyond a
threshold. The automatic reset may be capable of dynamically
changing the step size and thus improving the relationship of
convergence and stability of the filter. A time-aligning process
may be performed on the physiological and velocity signals, wherein
the time aligning process aligns the two signals relative to the
compressions. The method may include adaptive filtering of the
output of the time aligning process, wherein the adaptive filtering
reduces the error between the physiological and velocity signals.
The adaptive filter may include a Kalman filter. The adaptive
filter may employ adaptive equalization.
[0009] Among the many advantages of the invention (some of which
may be achieved only in some of its various implementations) are
the following:
[0010] This invention provides excellent techniques for (a)
adaptively removing the artifacts induced by CPR in an ECG signal,
(b) enhancing an ECG signal for monitoring, and (c) increasing the
reliability of ECG rhythm advisory algorithms.
[0011] As part of a rhythm advisory algorithm, various
implementations of the invention could be incorporated in an ECG
monitor, an external defibrillator, an ECG rhythm classifier, or a
ventricular arrhythmia detector.
[0012] The invention makes it possible to continue performing CPR
while ECG data is collected for an ECG rhythm advisory algorithm.
This can enhance the result of CPR, leading, for example, to an
increase in the success rate of resuscitation.
[0013] The invention can also provide a "cleansed" ECG signal
output for display to the user of a defibrillator.
[0014] The invention also provides for the first time a means of
measuring lung volume during chest compressions by impedance-based
methods. The method may also be used to filter other physiological
signals corrupted by compression-induced artifact, such as
impedance cardiography and pulse oximetry.
[0015] This invention demonstrates excellent performance at
removing the CPR artifact with a zero-th order FIR filter, thus
making some implementations of the invention much simpler and
faster than the adaptive-filter structures proposed in the prior
art.
[0016] Pre-processing of the reference signal and an
automatic-reset feature make it possible for some implementations
of the invention to use a relatively large step size for
adaptation, thus making convergence faster and more stable.
[0017] Some implementations of the invention achieve excellent
performance in CPR-artifact removal at reduced computational
cost.
[0018] Other features and advantages of the invention are described
in the detailed description, drawings, and claims.
BRIEF DESCRIPTION OF THE DRAWINGS
[0019] FIG. 1 is a block diagram of one implementation of the
invention.
[0020] FIG. 2 shows plots of the ECG signal, CPR reference signal,
and output of adaptive filter for a normal sinus rhythm.
[0021] FIG. 3 shows plots of the ECG signal, CPR reference signal,
and output of adaptive filter for ventricular fibrillation.
[0022] FIG. 4 is a block diagram of a filtered-X least mean squares
(FXLMS ANC) algorithm.
[0023] FIG. 5 is a block diagram of an implementation using the
algorithm of FIG. 4.
[0024] FIG. 6 shows two spectral power distributions related to the
implementation of FIG. 5.
DETAILED DESCRIPTION
[0025] There are a great many possible implementations of the
invention, too many to describe herein. Some possible
implementations that are presently preferred are described below.
It cannot be emphasized too strongly, however, that these are
descriptions of implementations of the invention, and not
descriptions of the invention, which is not limited to the detailed
implementations described in this section but is described in
broader terms in the claims.
[0026] One possible implementation is illustrated by a flow chart
in FIG. 1. The front end of an AED acquires both the ECG signal and
the CPR signal, which is the velocity of compression of the chest.
If chest displacement or acceleration are measured instead of
velocity, velocity can be mathematically acquired via one or more
integration or differentiation operations from the measurement
signal.
[0027] The velocity signal undergoes pre-processing, and is then
fed to an adaptive filter. In a preferred implementation, the
pre-processing is a normalization of the velocity signal so that
the signal supplied to the adaptive filter is limited to be within
0 and 1. But normalization is not required. In another
implementation, a time-aligning process is performed on the ECG and
the reference signal by such methods as cross-correlation. This
provide alignment of the two signals relative to the compressions
so that the input signals of the adaptive filter are better
aligned. But this aligning process is not required. Other
preprocessing can be applied to the velocity signal to improve the
performance of the adaptive filter.
[0028] In FIG. 1, x(n) and y(n) are the input and the output of the
adaptive filter H, which can be an FIR filter, an IIR filter, or
another type of filter. In a preferred implementation, the
coefficients of the filter are dynamically controlled by the
estimated ECG signal:
h(n)=h(n-1)+m.times.e(n).times.X(n)
[0029] where h(n) is a vector containing the filter coefficients, m
is a vector containing the step sizes for each filter coefficients,
e(n) is the estimated ECG signal, and X(n) is a vector containing
the input data. The estimated ECG signal is computed by subtracting
the filter output y(n) from the measured ECG signal (containing
artifact).
[0030] In some implementations, there is an automated resetting
mechanism. When the difference between the filter output y(n) and
the measured ECG s(n) is beyond a threshold, the adaptive filter
will reset its coefficients so that the system will not become
unstable.
[0031] Other filter structures than the one shown in FIG. 1, as
well as other mathematical representations of the filtering, are
possible.
[0032] FIG. 2 shows samples of the performance of the adaptive
filter of FIG. 1 in response to a normal sinus rhythm. The signal
in (a) is the ECG signal with CPR artifact. The signal in (b) is
the compression velocity used as the reference signal. The signal
in (c) is the output of the adaptive filter.
[0033] FIG. 3 shows samples of the performance of the adaptive
filter of FIG. 1 during ventricular fibrillation. The signal in (a)
is the ECG signal with CPR artifact. The signal in (b) is the
compression velocity used as the reference signal. The signal in
(c) is the output of the adaptive filter.
[0034] As shown in both FIG. 2 and FIG. 3, the implementation of
FIG. 1 is able to suppress the CPR artifacts embedded in the
measured ECG signals (a). The CPR artifact is nearly, if not
completely, removed in the estimated ECG signal (c). The velocity
signal (b) used as a reference signal is clearly correlated with
the CPR artifacts in the measured ECG signals (a).
[0035] The adaptive filter assumes that the artifact in the signal
is correlated with the reference signal and uncorrelated with the
desired signal (estimated ECG). It thus adaptively estimates the
artifact using the reference signal and subtracts the estimated
artifact from the measured ECG signal.
[0036] The results shown in FIG. 2 are based on a 0th-order FIR
filter, which simply scales the current sample of the ECG signal
adaptively. The CPR artifact was significantly reduced, if not
completely removed. This implementation thus combines simplicity
and efficiency in its performance.
[0037] In the applications of adaptive filters, the speed of
adaptation convergence is usually controlled by a step-size
variable. A faster convergence requires a larger step size, which
usually tends to make the filter less stable. The automatic
resetting mechanism of some implementations can dynamically change
the step size and thus improve the relation of convergence and
stability.
[0038] The coefficients of the filter are updated in a
sample-by-sample manner. The changes of the coefficients, i.e.,
h(n)-h(n-1) is proportional to the product of the step size and the
reference signal. The amplitude of the reference signal can thus
affect the stability and convergence of the filter. The
pre-processing of the reference signal can therefore enhance the
performance of the filter by adjusting the reference signal.
[0039] In another implementation, a time-aligning process is
performed on the ECG and velocity signals by such methods as
cross-correlation. This provide alignment of the two signals
relative to the compressions. Then, preferably, adaptive filtering
methods are used such as those involved in the minimization of the
mean-squared error between the ECG and the velocity.
[0040] A processing unit could be provided for detecting when
compressions are being applied and automatically turning on the
adaptive filter. The output of the adaptive filter (i.e., the ECG
signal with artifact reduced) could be supplied to a ventricular
fibrillation (VF) detection algorithm (e.g., a shock advisory
algorithm) of an automatic external defibrillator (AED).
[0041] An error signal could be produced that is representative of
the difference between the ECG input and ECG output of the adaptive
filter. This error signal would give a measure of the amount of CPR
artifact in the signal, and it would be useful as a means of
modifying the subsequent processing of the ECG. For instance, if
the artifact level gets high enough (e.g., higher than a first
threshold), the VF detection algorithm thresholds could be
increased to make it more resistant to any CPR artifact that still
remained in the ECG signal. If the level got even higher (e.g.,
higher than a second threshold higher than the first threshold),
the VF detection could be shut off entirely.
[0042] In preferred implementation, the filter output is presented
graphically on the display of a defibrillator or other medical
device incorporating an electro-cardiographic function. The filter
output may also be printed on a strip-chart recorder in the medical
device. Alternatively, the filter output may provide the input
signal for subsequent signal processing performed by the processing
means. The purpose of such signal processing may take the form of
QRS detection, paced beat detection during pacing, arrhythmia
analysis, and detection of ventricular fibrillation or other
shockable rhythms.
[0043] Spectral analysis could be performed on the error signal,
and based on the major bands of frequency content of the error
signal, the pre-filtering of the ECG signal prior to the VF
detection can be adjusted. For instance, if the error signal is
found to reside primarily in the 3-5 Hz band, additional filtering
can be provided in that band prior to input into the VF detection
(or other ECG processing) algorithm.
[0044] Many other implementations of the invention other than those
described above are within the invention, which is defined by the
following claims.
[0045] For example, methods of adaptive channel equalization may be
employed to ameliorate both synchronization and phase errors in the
velocity waveform. Kalman filtering techniques may also be employed
to improve performance of the filter when rescuer performance of
chest compressions changes over time and is better modeled as a
non-stationary process.
[0046] Time alignment of the ECG and velocity signal may also be
accomplished by such methods as cross-correlation techniques known
to those skilled in the art. This will provide alignment of the two
signals relative to the compressions. Then, preferably, adaptive
filtering methods are used such as those involved in the
minimization of the mean-squared error between the ECG and the
velocity.
[0047] In a further implementation, more sophisticated signal
processing methods may be used to minimize ECG artifacts induced by
CPR chest compressions. For example, methods known as feed forward
active noise cancellation (FANC) may be used. FIG. 4 shows a block
diagram of the filtered-X least mean squares (FXLMS ANC) algorithm,
as developed by Widrow and Burgess. P(z) represents the unknown
plant through which the signal x(n) is filtered. Digital filter
W(z) is adaptively adjusted to minimize the error signal e(n). In
one implementation, as depicted in FIG. 5, x(n) is the unfiltered
ECG signal, P(z) is eliminated from the diagram, and d(n) is
approximated with the chest compression velocity signal v(n). In
the LMS algorithm, assuming a mean square cost function
.xi.(n)=E[e2(n)], the adaptive filter minimizes the instantaneous
squared error, .xi.(n)=e2(n), using the steepest descent algorithm,
which updates the coefficient vector in the negative gradient
direction with step size .mu.:
w(n+1)=w(n)-.mu.2*.xi.(n),
[0048] where .xi.(n) is an instantaneous estimate of the mean
square error (MSE) gradient at time n equal to -2v(n) e(n).
Stability and accuracy of the FXLMS ANC algorithm can be improved
by adding a variable cutoff low pass filter H(z) to eliminate
frequency components in the ECG not related to the chest
compression artifact. In general, the spectral energy of the chest
compression artifact is predominately lower than those of the ECG.
A cutoff frequency of approximately 3 Hz is adequate in many cases,
but this may vary from patient to patient and among different
rescuers performing chest compressions. To overcome this
difficulty, an FE" is performed on v(n) and input into a cutoff
frequency estimation (CFE) procedure that determines the optimal
cutoff frequency, fC, for the lowpass filter. In a preferred
implementation, the decision is based on calculating the frequency,
not to exceed 5 Hz, below which 80% of the waveform energy is
present, but this percentage may vary and additional decision logic
may be employed. For instance, an FEI may also be calculated for
x(n), also input to the CFE procedure. By first normalizing
amplitude of the frequency spectra X(z) amplitude peak of the
compression artifact and then subtracting the velocity spectra V(z)
from the normalized input X'(z), the difference spectra is
calculated .DELTA.X'(z)=X'(z)-V'(z). Frequencies are then
determined for V(z) and .DELTA.X'(z) at which most of the spectral
energy is within, set in this embodiment to 97%, and labeled fCV
and fCX, respectively, and shown in FIG. 6. FC is then set to the
lesser of fCV and fCX. Alternatively, fC can be set to some
intermediate frequency between fCV and fCX.
[0049] The quality of other physiological signals, such as
impedance cardiographic (ICG), impedance pneumographic (IPG), or
pulse oximetry, known to those skilled in the art, may also be also
be enhanced by the filter, particularly if the sensor is located on
the thoracic cage in nearby proximity to the motion sensor from
which the velocity signal is derived. Minimization of compression
artifact with impedance pneumography signals can be accomplished
with any of the previously described methods.
[0050] The adaptive filter can be used to minimize the
cross-correlation of the adaptive-filter output with the reference
signal or the cross-correlation of the adaptive-filter output with
the measured ECG signal.
* * * * *