U.S. patent application number 14/064853 was filed with the patent office on 2014-02-20 for system, method and computer program product for detection of changes in health status and risk of imminent illness.
This patent application is currently assigned to UNIVERSITY OF VIRGINIA PATENT FOUNDATION. The applicant listed for this patent is UNIVERSITY OF VIRGINIA PATENT FOUNDATION. Invention is credited to Douglas E. Lake, J. Randall Moorman.
Application Number | 20140052011 14/064853 |
Document ID | / |
Family ID | 40952444 |
Filed Date | 2014-02-20 |
United States Patent
Application |
20140052011 |
Kind Code |
A1 |
Moorman; J. Randall ; et
al. |
February 20, 2014 |
System, Method and Computer Program Product for Detection of
Changes in Health Status and Risk of Imminent Illness
Abstract
A method for analysis of cardiac rhythms and the clinical status
of a patient, based on calculations of entropy and moments of time
series intervals. An optimal determination is made of segments of
data that demonstrate statistical homogeneity, specifically with
regard to moments and entropy. The invention also involves
calculating moments and entropy on each interval segments with the
goal of diagnosis of cardiac rhythm. More specifically, an absolute
entropy measurement is calculated, providing dynamic information of
fundamental importance in diagnosis and analysis.
Inventors: |
Moorman; J. Randall;
(Charlottesville, VA) ; Lake; Douglas E.;
(Charlottesville, VA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
UNIVERSITY OF VIRGINIA PATENT FOUNDATION |
Charlottesville |
VA |
US |
|
|
Assignee: |
UNIVERSITY OF VIRGINIA PATENT
FOUNDATION
Charlottesville
VA
|
Family ID: |
40952444 |
Appl. No.: |
14/064853 |
Filed: |
October 28, 2013 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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12866056 |
Aug 4, 2010 |
8588908 |
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PCT/US2009/033082 |
Feb 4, 2009 |
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14064853 |
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61043598 |
Apr 9, 2008 |
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61025989 |
Feb 4, 2008 |
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Current U.S.
Class: |
600/515 ;
600/508 |
Current CPC
Class: |
A61B 5/412 20130101;
A61B 5/0468 20130101; A61B 5/0245 20130101; A61B 5/02405 20130101;
A61B 5/0006 20130101; A61B 5/044 20130101; A61B 5/04012 20130101;
A61B 5/0452 20130101 |
Class at
Publication: |
600/515 ;
600/508 |
International
Class: |
A61B 5/024 20060101
A61B005/024; A61B 5/00 20060101 A61B005/00; A61B 5/04 20060101
A61B005/04; A61B 5/044 20060101 A61B005/044; A61B 5/0468 20060101
A61B005/0468; A61B 5/0245 20060101 A61B005/0245 |
Claims
1. A method of detecting abnormal cardiac rhythms and clinical
status of a patient comprising: obtaining physiological data from a
subject comprising one or more series of intervals; calculating
entropy data based the series of intervals; and generating a
diagnostic output based on the entropy data.
2. The method of claim 1, wherein the entropy data based on the
series of intervals comprises numbers of matching intervals.
3. The method of claim 2, wherein generating the diagnostic output
comprises using a regression model to combine the numbers of
matching intervals.
4. The method of claim 1, wherein the physiological data comprises
two or more series of intervals obtained simultaneously from
different physiological signals.
5. The method of claim 1 further comprising separating the series
of intervals into a plurality of subsets of the series of
intervals.
6. The method of claim 5, wherein calculating the entropy data
further comprises averaging entropy data from each of the plurality
of the subsets of the series of intervals.
7. The method of claim 5, wherein the subsets of the series of
intervals comprise less than 13 intervals.
8. The method of claim 1, wherein generating the diagnostic result
further comprises using the coefficient of sample entropy
(COSEn).
9. The method of claim 1 further comprising producing an output to
a medical care provider to diagnose abnormal cardiac rhythms and
clinical status of the subject.
10. The method of claim 1, wherein the interval data comprises a
number of samples during one period of a cardiac rhythm.
11. The method of claim 1, wherein calculating entropy data based
on the numbers of matching intervals further comprises a tolerance
value (r) for determining whether two or more intervals match.
12. The method of claim 1, wherein the data from the cardiac rhythm
of a patient comprises samples from an EKG waveform.
13. A method of detecting abnormal cardiac rhythms and clinical
status of a patient comprising: obtaining physiological data from a
subject; separating the physiological data into intervals; grouping
the intervals into one or more sets of segments of intervals;
determining numbers of matching intervals within each segment; and
generating a diagnostic output based on the numbers of matching
intervals.
14. The method of claim 13, wherein the diagnostic output
determines whether the subject has an abnormal cardiac rhythm and
the subject's clinical status.
15. The method of claim 13, wherein generating the diagnostic
output further comprises running a regression model on the numbers
of matching intervals.
16. The method of claim 13 further comprising calculating an
absolute entropy measurement.
17. The method of claim 16, wherein the absolute entropy
measurement is a coefficient of sample entropy (COSEn).
18. An apparatus for detecting abnormal cardiac rhythms and
clinical status of a patient comprising: a sampling device for
obtaining physiological data from a subject comprising a one or
more series of intervals; a computer processing device configured
for processing the physiological data from the subject into entropy
data and producing a diagnostic result; and an output device for
rendering the diagnostic output.
19. The apparatus of claim 18, wherein the sampling device
comprises an EKG machine.
20. The apparatus of claim 18, wherein producing the diagnostic
output comprises running a regression model on the entropy
data.
21. The apparatus according to claim 18, wherein the physiological
data is representative of a cardiac rhythm and clinical status of
the subject.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application claims benefit under 35 U.S.C. .sctn.119(e)
to U.S. Provisional Patent Application Ser. No. 61/025,989 filed on
Feb. 4, 2008, and Ser. No. 61/043,598 filed on Apr. 9, 2008, which
are hereby incorporated by reference in their entireties.
[0002] This application is also related to PCT application No.
PCT/US2008/060021, which is hereby incorporated by reference in its
entirety.
STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT
[0003] This invention was not made in the course of federally
sponsored research or development.
THE NAMES OF THE PARTIES TO A JOINT RESEARCH AGREEMENT
[0004] This invention was not made in the course of joint research
agreement.
BACKGROUND OF THE INVENTION
[0005] The present invention generally relates to the field of
cardiology and in particular to detection and analysis of cardiac
function. There is a serious need for detection of normal and
abnormal cardiac rhythms as well as evaluation of the clinical
status of the patient, e.g., detection of severe or worsening
congestive heart failure, using heart rate (HR) or interbeat
interval series.
[0006] A problem of enormous and growing concern in health care in
America is hospitalization for worsening congestive heart failure
(CHF). New medical therapies have prolonged the life of many with
CHF, and implantable cardiac devices--implantable
cardioverter-defibrillators (ICDs) and biventricular
pacemakers--have been especially effective in prolonging life and
reducing symptoms. ICDs are small battery-powered electrical
impulse generators that are implanted in at-risk patients and are
programmed to detect cardiac arrhythmia and correct it by
delivering a jolt of electricity to the heart muscle. Most patients
with single lead ICDs have reduced Left Ventricle (LV) function,
and thus either have or are at risk for CHF syndromes. Other than
heart rate and heart rate variability, and in some cases
trans-thoracic impedance, no measures are currently available to
gauge the degree of CHF over time. There is, however, potentially a
great deal of clinical utility in doing so.
[0007] A new role for ICDs is as diagnostic monitors that might
allow early detection of incipient volume overload. For example,
modern pacemakers and defibrillators store several dimensions of
physiological data representative of the functional status or
physiological signals of the patient, including: [0008] heart rate
(HR) [0009] heart rate variability (HRV) [0010] amount of pacing in
the atrium and the ventricle [0011] patient activity, in hours per
day [0012] atrial fibrillation burden (only in devices with atrial
leads) [0013] arrhythmia log [0014] respiration [0015]
trans-thoracic impedance, a measure of pulmonary vascular
congestion [0016] and any other relevant physiological signals
[0017] The hope is that all of these parameters will yield
clinically useful information about the status of the
cardiovascular system and in particular the possibility of imminent
decompensation. The presumption is that very early detection of
volume overload can be treated at home with increased doses of
medications, averting severe symptoms and the need for
hospitalization.
[0018] These parameters, however, are currently presented to the
physician for review without presenting any interpretation, and
there are few studies of how these data can be of clinical use. It
has been demonstrated that hospitalizations for heart disease is
associated with a reduction in heart rate variability (HRV, a
well-established measure of risk of cardiac events) measured by the
standard deviation of 5-minute median A-A intervals (SDAAM) (the
time between sensed, that is, non-paced, atrial depolarizations),
reduction in patient activity, and increased heart rate (HR) at
night. Although a patient with CHF may exhibit low HRV, there are
usually a few beats that are distinct from the rest and will occur
prematurely, followed by an extended pause so that the heart can
catch-up to where it should have been absent the premature beat.
These are termed premature ventricular beats or contractions
(PVCs).
[0019] Although atrial fibrillation (AF) can be discerned using
coefficient of sample entropy (COSEn), attempts at developing a
diagnostic tool that distinguishes normal sinus rhythm (NSR) in
patients with CHF from other patients with NSR using only very
short heart rate time series have so far not succeeded. Such a
method would be very useful in patients with ICDs, where the risk
of CHF is high but the ability to do extended calculations is low.
The long-felt need for a new method that addresses the limitations,
disadvantages, and problems discussed above is evidenced by the
many databases available for development and testing of new
arrhythmia detection algorithms. Several of these databases, such
as the MIT-BIH database, have been used during the development and
testing of embodiments of the present disclosure.
[0020] Detection of AF can be accomplished with very high degrees
of accuracy if an intra-atrial cardiac electrogram from an
implanted pacing lead or a conventional EKG signal from skin
electrodes is available. Neither is as non-obtrusive as a device
that records the time from one arterial pulse waveform to the next,
but such a non-invasive device can provide only the heart rate time
series with no information about cardiac electrical activity. Thus,
an algorithm and computer method for detecting arrhythmia or the
clinical status of a patient using only a heart rate or pulse rate
series is a desirable goal.
[0021] There is currently exists no single parameter to inform
clinicians and patients of imminent problems such as CHF. Yet such
an approach is sensible--combinations of data values may define
specific profiles of clinical status. For example, a measure that
combines an HRV measure, patient activity and nocturnal heart rate
is very likely to be more useful than any of the measures alone. An
aspect of an embodiment of the present invention comprises among
other things, combining multiple data streams using optimized
mathematical techniques.
BRIEF SUMMARY OF INVENTION
[0022] We have developed a new measure of heart rate entropy that
changes in proportion to the degree of CHF. It is related to, but
distinct from, sample entropy (SampEn) or the coefficient of sample
entropy (COSEn) that we have previously developed. We find that we
can distinguish CHF patients from normals using only an analysis of
12 beats every 30 minutes.
[0023] Aspects of various embodiments of the present disclosure
comprise, but are not limited to, the following: systems and
methods for analyses of physiological time series recorded by
implanted cardiac devices, and for analyses of multiple
simultaneously recorded series. While the embodiments are used
herein for detection of incipient congestive heart failure episodes
and atrial fibrillation using information from implanted pacemakers
and cardioverter-defibrillators, other embodiments are contemplate
to extend to other kinds of signals from internal and external
measurement devices and monitors, and to other states of health and
disease.
BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS
[0024] FIG. 1 is an illustrative diagram of a computer system.
[0025] FIG. 2 depicts two charts showing the efficacy of parameters
used in the SampEn algorithm.
[0026] FIG. 3 depicts histograms of expected interval matches for
different matching probabilities.
[0027] FIG. 4 illustrates histogram from an analysis of patients in
the MIT-BIH database determining a number of matching intervals on
average in 500 interval time series.
[0028] FIG. 5 depicts a histogram of average results from an
analysis of patients from the MIT-BIH database, the histogram is
very similar to that of FIG. 4, but only 12 instead of 500 interval
time series were examined.
[0029] FIG. 6 is a scatter plot distinguishing between patients
with AF, NSF and CHF.
[0030] FIG. 7 illustrates results from a regression model using
data from an embodiment of the present disclosure and COSEn in a
scatter plot showing differentiation of patients with AF, NSF and
CHF.
[0031] FIG. 8 illustrates a box plot showing a trend for higher
diagnostic values in more severe class III and IV CHF patients.
DETAILED DESCRIPTION OF THE INVENTION
[0032] Aspects of various embodiments of the present disclosure
comprise, but are not limited to, the following: comparing time
series representing periods of a cardiac rhythm with other series
to generate numbers of matches to provide an entropy estimate. The
numbers of matches may further be processed to generate a
diagnostic output representative of a patient's abnormal cardiac
rhythm and clinical status. No existing approaches employ optimized
estimates of time series entropy or combine measurements across
different kinds of time series data from implantable cardiac
devices.
[0033] One embodiment of the present disclosure focuses on entropy
estimation of time series time domain HRV data types. Generally
speaking, this is a measure of the predictability or regularity of
a time series. Entropy estimation of heart rate has been
well-described for a number of years, and can distinguish normal
long heart rate records from those of patients with CHF. For
example, sample Entropy (SampEn) is a robust estimator of entropy
in short biological and physiological time series. Multiscale
entropy can be described based on sample entropy measure to
accomplish this task. Entropy estimation of other measures listed
above has not been described, but may hold important clinical
information. For example, a current concept is that illness leads
to reduced coupling among physiological systems, altering the
entropy content of physiological signals.
[0034] An aspect of the present disclosure is to use optimized
entropy calculations of time series information present in
implantable devices to make early diagnosis of sub-acute,
potentially catastrophic illness such as CHF. Each of the
physiological signals listed above (e.g., HR, HRV, ect.) can be
assessed in some way for its regularity or predictability using an
entropy calculation. It has been found that illness leads to a
reduction in complexity of physiological processes. Thus, time
series of heart rate, heart rate variability parameters themselves
(such as the standard deviation of short thoracic impedance, and
intracardiac pressures) should show changes in entropy as clinical
status changes. Of these, heart rate has been well-studied. An
interesting finding is that patients with CHF have been reported to
have increased entropy of heart rate time series, a finding that
does not fit well with current knowledge. This apparent discrepancy
has been resolved by showing that a multiscale entropy calculation
demonstrated lower entropy in CHF patients, suggesting that too
short a time scale fails to capture the predictability of normal
heart rate control.
[0035] Embodiments discussed throughout the disclosure improve the
detection of CHF. Various embodiments of the present disclosure may
be based on several fundamental differences between the RR interval
time series in CHF and in other clinical settings as well as
important supplemental information provided by physiologic signals
measuring activity, blood pressure, and respiration. Measurements
of the RR interval series used to classify cardiac rhythms and
clinical status fall into two basic categories of estimates of the
moments and estimates of entropy rate to characterize heart rate
dynamics. For analysis of multiple simultaneous signals,
mathematical approaches to detect CHF can be placed into 3
categories: 1) moments; 2) entropy and entropy rate; and 3)
cross-correlation and cross-entropy measures.
[0036] The first category includes measurements that are associated
with established statistical methods, such as the mean, standard
deviation, and coefficient of variation. The second category
includes the family of Renyi entropy (or q-entropy) rates. The
third category consists of measures of the association and
interaction between the various physiologic signals. These include
results from standard cross-spectral analysis including pair-wise
correlations between signals at varying time-lags.
[0037] Embodiments of the present disclosure detect cardiac rhythms
and clinical status of a patient based on a series of RR intervals
or other physiological signals, which arise from a complex
combination of both deterministic and stochastic physiological
processes.
[0038] Sample entropy (SampEn) is a measure derived from chaos
theory that reports on deterministic properties of time series.
SampEn has better statistical properties than approximate entropy
and has been utilized successfully on neonatal HR data to aid in
the prediction of sepsis. SampEn has also been used as part of a
promising new multiscale entropy (MSE) analysis technique to better
discriminate adult HR data among normal, atrial fibrillation, and
congestive heart failure patients.
[0039] For purposes of comparison, sample entropy is considered a
deterministic approach to measuring complexity and order in heart
rate variability. A complementary approach included in some
embodiments is to consider HR and other physiological data
sufficiently stochastic to model it as a random process. We have
developed stochastic Renyi entropy rate measures that can be
reliably estimated with a known family of statistical properties.
An appropriate member of the family to emphasize is differential or
quadratic entropy rates (q=2) which is denoted by Q and calculated
using the SampEn algorithm with optimized values for the parameters
m and r.
[0040] These measures can be interpreted in ways that are analogous
to the deterministic concepts of complexity and order. While
developed under a stochastic framework, the algorithms are easily
modified to compute deterministic approach measures that include
both Approximate Entropy (ApEn) and SampEn. There are several basic
differences between the stochastic approach and the deterministic
approaches, and each has potential application to detection of
congestive heart failure. The deterministic approach, for example,
involves calculating probabilities while the stochastic approach
calculates probability densities. The probabilities involve
matching intervals of length m within a tolerance r and converting
them to densities by dividing by the volume of the matching region,
which is (2r).sup.m. This simply reduces to adding a factor of
log(2r) to ApEn or SampEn. The stochastic approach becomes viable
when the values converge as r tends to 0 and the deterministic
approach is diverging.
[0041] With deterministic approaches, the values of m and r are
fixed for all the analysis (sometimes signal length is also
constant). This is done to enable comparison of a wider variety of
processes, but has several disadvantages. The choices of m and r
vary from study to study and comparison of results is not always
possible. Optimal parameters can be chosen for other clinical
settings. In one embodiment, we use both fixed value of r=50 msec
as well as r=f(S.D.). With fixed values, there is always the
possibility of encountering data that results in highly inaccurate
entropy estimates, so included in this embodiment is the continued
development of absolute entropy measures independent of m and r
that are statistically reliable and allow for comparison between a
wide range of HR data sets.
[0042] With the stochastic approach, the goal is to estimate a
theoretical limiting value as r goes to zero. The value of r for
estimation does not need to be fixed and can be optimized for each
signal. In addition, for longer records we include in one
embodiment the option of not fixing m and instead estimating the
theoretical limiting value as m tends to infinity. One advantage of
this general philosophy is that tolerances and interval lengths can
be selected individually for each signal to ensure accurate
estimates. Even if it is advantageous or necessary to compare
signals at the same value of r, this embodiment flexibility allows
using different tolerances for estimating and applying a correction
factor.
[0043] This idea is particularly important in the current setting
of estimating entropies of quantized RR intervals obtained from
coarsely sampled EKG waveforms, as quantization of the signal can
occur when the sampling rate is low. These scenarios mean that all
tolerances r within the resolution will result in the exact same
matches and the issue becomes what value r should be used to
calculate the entropy rate. The proper choice is to pick the value
midway between the quantized values of r. For example, the EKG
signal was sampled at 250 Hz for some signals in the CHF database,
and thus the RR intervals are at a resolution of 4 ms. In this
case, all tolerances between, say, 12 and 16 milliseconds would be
considered 14 for the log(2r) term. Different values are needed for
signals from the NSR database and other signals in the CHF database
that were sampled at 128 Hz and at a resolution of 7.8 msec. This
continuity correction can be nontrivial when tolerances are close
to the resolution of the data. Some embodiments optimize the
accuracy and discriminating capability of the entropy measures.
Undersampling can occur with other physiologic signals as well, and
the embodiments robustly and accurately estimate entropy in spite
of this problem.
[0044] As an example, we describe the use of entropy measures to
discern abnormal cardiac rhythm and clinical status in RR interval
time series from the canonical MIT-BIH CHF and NSR databases
employing multivariable logistic regression and its variations.
These databases are available at www.physionet.org and are
described in Table 1. Most, if not all, of the rhythms are sinus
with varying degrees of premature ventricular beats, or ventricular
ectopy.
TABLE-US-00001 TABLE 1 MIT-BIH databases CHF class CHF class
Database NSR 1-2 3-4 Patients 72 29 15 Age 20 to 76 34 to 79 22 to
63 Male 49% not known 73% Duration (72) (24) = 1 (29) (24) = 6 (15)
(24) = 3 (hours) 728 96 60 Sampling (Hz) 128 128 250 NSR = normal
sinus rhythm, CHF = congestive heart failure
[0045] We determined optimal values of m and r by calculating the
error of the SampEn estimate for a wide range of both parameters.
The results for distinguishing normal subjects with NSR from CHF
patients with NSR using the MIT data bases are shown in the FIG. 2.
Here, we analyzed non-overlapping 500 point segments of RR
intervals.
[0046] In the maps of FIG. 2, the gray areas 10 represent favorable
characteristics of the m, r pair--accurate entropy estimates in the
left and middle panels, and good discrimination (measured as the
receiver-operating characteristic, or ROC, area) in the right hand
panel. Black areas 11 show m, r pairs where no matches were found
and thus entropy could not be calculated. The area to the left of
and below zigzag line 12 shows an m, r space where SampEn cannot be
accurately determined in atrial fibrillation. This is a sensible
result--that area of the plot requires long intervals that match
closely, which is not the case for AF. These results demonstrate
both the general method for optimal selection of m and r and
specific findings for detecting CHF--m should be 1 or 2, and r
should be about 20 msec. With these settings, SampEn alone provides
distinction with ROC areas around 0.8.
[0047] In implanted devices, computing power and memory are
currently limited, and analysis of 500 point segments is currently
prohibitive. Therefore, a reduced sampling of parameters from a
patient is beneficial. Thus, for the next analysis, we studied
non-overlapping 16-point segments. Moment and entropy rate
parameters described above were estimated for each record, and CHF
detectors were developed using multivariable logistic regression
analysis and an optimal subset of variables. With multiple
physiologic signals, this approach would be expanded to include
moment and entropy rate variables from each individual.
[0048] In this embodiment, an optimal subset of variables for
detecting CHF were the quadratic or differential entropy rate (Q),
the natural logarithm (ln) of the mean (.mu.), and the log of the
standard deviation (.sigma.) of the RR intervals. This model has an
ROC area of 0.750, which is highly significant as are each of the
coefficients. The entropy rate is calculated using the SampEn
algorithm with parameters m=1 and a tolerance r selected to ensure
a number of matches in the numerator of at least the record length
(in this case 16). This result aided in the development COSEn,
which is described in more detail in PCT application No.
PCT/US2008/060021. We also compare these results with the
coefficient of variation CV=.sigma./.mu..
[0049] The results for other models are summarized below in Table
2. Subsets of parameters are evaluated using the significances of
individual coefficients and of the overall model using the Wald
statistic adjusted for repeated measures. The overall significance
of the model can be converted to a Wald Z-statistic, which can be
used to make a fair comparison among models with varying number of
parameters. The results clearly demonstrate that the proposed
approach that includes entropy measures as part of a multivariate
model enhances the detection performance of CHF. Additional
improvements are anticipated with the inclusion of entropy and
cross-entropy measures from other available physiological
signals.
TABLE-US-00002 TABLE 2 Model Performances on MIT NSR and CHF data
bases CHF Parameters ROC Wald Wald Z log (.mu.) .708 21.4 14.5 log
(.sigma.) .659 9.2 5.8 log (CV) .638 5.1 2.9 log (.mu.), log
(.sigma.) .712 33.6 15.8 Q .741 17.8 11.9 Q, ln (.mu.), log
(.sigma.) .750 62.6 24.3
[0050] As discussed above, one way of quantifying one period of a
cardiac rhythm is by its length (m). The length can be determined
by, for example, sampling points on an EKG during one interval of
the cardiac rhythm. A longer interval will have a greater number of
samples than a shorter interval. An interval can be one period of
the rhythm, for example an RR or AA interval, but it could also be
any arbitrary size.
[0051] Once the intervals of a cardiac rhythm are quantified, they
may be compared to determine whether the numbers of samples for
each interval match. The intervals may be divided up into, for
example, 12 interval series. Next, each interval may be compared
with each other interval to determine the number of times that each
interval matches another interval of the same series. Each interval
therefore has a corresponding number of matches associated with it.
For example, 7 intervals may have matched 0 times, 4 may match 1
time, and 1 may have matched 2 times. A resulting histogram of
these values would appear similar to the left-most histogram of
FIG. 3.
[0052] Histograms created in the method just described will have a
predictable appearance depending on the entropy of the cardiac
rhythm. To illustrate, as the tolerance r goes to zero, the
distribution of interval match counts approximates the distribution
of a random variable f(X) where X is a random RR interval of length
m and f is the probability density function of X, e.g., left-most
histogram of FIG. 3. For Gaussian random numbers, we expect -2
ln(interval match counts) to have a shifted chi-square distribution
with m degrees of freedom, e.g., middle histogram of FIG. 3. If the
RR intervals are independent, then the distribution of interval
match counts (not including self-matches) is approximately binomial
with n-m trials and success probability p equal to the probability
of any interval matching within the tolerance r. FIG. 3 shows the
expected results of interval match counts for 12-beat segments and
p(match)=0.05, 0.50 and 0.95.
[0053] In the case of a rhythm with high entropy, as would be found
in a patient with AF, it would be expected that there would be a
low number of matches. This result corresponds to the left-most
histogram of FIG. 3. As is well known in the art, a patient with
CHF has low entropy, i.e., low HRV. Therefore, it would be expected
that a high number of intervals would match. A resulting histogram
would appear similar to the right-most histogram illustrated in
FIG. 3.
[0054] FIG. 4 shows histograms representing entropy measures for
normal (top), CHF (middle) and AF (bottom) patients in the MIT-BIH
databases. In each n-beat segment, the general method is to count
the number of matches that were found for each interval. For m=1,
for example, there are n intervals, and each can find as many as
n-1 matches. To examine the results, we made histograms of the
frequency of intervals having specified numbers of matches. Each
24-hour record was divided into 500 intervals, and the histograms
were averaged. m=2 and r=20 msec. There are large phenotypic
differences. FIG. 7 illustrates how AF, NSR and CHF patients in the
MIT-BIH database are distinguished using the two measures COSEn and
the matching algorithm disclosed herein, which is based on the
histograms on the left. These results are from an analysis of
12-beat samples every 30 minutes from 24-hour Holter monitor
recordings, and improve greatly over existing measures such as
heart rate and heart rate variability, results of which are shown
in FIG. 6.
[0055] Thus, the disclosed matching algorithm should provide an
accurate estimate of the degree of CHF burden in patients with
single lead ICDs, and should be useful in early detection of
deterioration and impending CHF hospitalization. In addition, it
can be used to calculate an AF burden in conjunction with or
separate from COSEn analysis. Both COSEn and the disclosed matching
algorithm are computationally efficient (only a few extra steps are
required to employ the disclosed matching algorithm), and together
they offer new opportunities for informing clinicians about AF and
CHF burdens in at-risk patients.
[0056] Entropy estimation is limited by the fact that it is
fundamentally a ratio of 2 counts (expressed as the negative log),
the numbers of matches of intervals of length m and m+1. While we
have emphasized the importance of adequate numbers of counts of
both types, we have not until now investigated the counts
themselves. When given an entropy result of 0.693, we can tell that
the ratio of the counts is 0.5, but we cannot tell whether the
counts themselves were 5/10 or 50,000/100,000. It is conceivable
that two time series with the same entropy result might have very
different properties. This is especially possible in the case of RR
interval time series where clinicians seek to detect CHF among
patients with NSR. This is because patients with CHF can have much
lower heart rate variability (HRV) than normals even though both
groups have NSR. Indeed, many measures of HRV that can distinguish
CHF from normals are based on aspects of reduced variability as
measured in the time- and frequency-domains. Thus, low HRV should
increase the number of matching intervals, and a strategy that
employs a count of matching intervals as well as other measures of
entropy should have increased diagnostic performance.
[0057] FIGS. 4 and 5 use the same records; however, FIG. 5 was
analyzed much more sparsely--12 intervals at a time every 2400
beats, or about every 30 minutes (i.e., m=1 and r=20 msec). The
differences between the histograms remain.
[0058] More specifically, FIGS. 4 and 5 show interval match count
histograms for three groups of representative 24-Holter recordings
from the MIT-BIH databases. From top to bottom of FIG. 4, the
datasets were 500-beat segments from the NSR, CHF and AF databases.
In each, the y-axis is the frequency of intervals of length m=2
that have the match count given on the x-axis. The left-most bin,
for example, is the frequency of intervals in the 24 hour recording
that each found 0 to 10 matches. The right-most bin is the
frequency of intervals that matched 490 to 499 of the other
intervals. The total of all the frequencies is the number of points
in the 24-hour records, as each point is an interval.
[0059] The histograms look very different:
[0060] Histograms from NSR records are characterized by very few
intervals that match the majority of points.
[0061] CHF records have relatively larger numbers of intervals in
the left-most bins, representing intervals with a small numbers of
matches. We ascribe these to premature ventricular or atrial beats.
CHF records, on the other hand, have much larger numbers of
intervals in the right-most bins. We ascribe these to intervals in
a low variability baseline, where p(match) is high.
[0062] AF records are strikingly different. Most intervals have
very few matches, as expected from the high variability of the RR
interval time series and corresponding low p(match).
[0063] Thus, the observed interval match count histograms generally
follow the theoretical results of FIG. 1, additional information
important to discriminating CHF from NSR is available in the
details of specific interval match count bins.
[0064] The large phenotypic differences between the histograms
indicate that good distinction is possible. Since one of our goals
is to implement the matching algorithms in implanted devices, where
computing power is precious, we also examined these histograms for
much shorter segments. The number of operations scales with
n.sup.2, so an effective diagnostic strategy using shorter segment
lengths has appeal. In FIG. 5, we show results for 12-beat
segments. In order to prepare for real-world implementation in
implanted devices, where stored energy is at a premium, we only
analyzed every 200.sup.th segment, or every 2400 beats. This is
about every 30 to 35 minutes, depending on the heart rate. For
COSEn, we used m=1 and r=20 msec. (The justification for r=20 msec
is that this led to the smallest proportion of intervals that were
degenerate, that is, that found either no matches or matched every
other interval.) Even at this sparse sampling, large phenotypic
differences remain. It may be that even sparser sampling schemes
have equally good results.
[0065] We used these phenotypic changes in interval match count
histograms to develop a detection scheme based on multivariable
regression. Table 3 shows diagnostic performance of detection
algorithms based on conventional HR and HRV measures, and on the
new measures COSEn and the interval match counts. Results are based
on logistic regression models trained to distinguish the MIT-BIH
NSR records from the MIT-BIH CHF records, and the ROC
(receiver-operating characteristic) curve area is given. "Match
counts" refers to the output of a regression model utilizing
parameters extracted from the match count histogram. In this
example, we used the average number of intervals having 0, 1, or 11
matches, and we used the average number of matches per interval.
Other kinds of schemes also give good results. In particular,
adding the mean RR interval to the match counts gave the highest
diagnostic performance.
TABLE-US-00003 TABLE 3 Performance of multivariable predictive
models predictor 1 predictor 2 ROC p1 p2 S.D. 0.60 * mean RR 0.78 *
S.D. mean RR 0.79 * COSEn 0.78 * match counts model 0.92 * COSEn
match counts model 0.89 * average counts model match counts model
0.91 * * match counts model mean RR 0.93 * *
[0066] Predictors 1 and 2 are predictor variables in the
multivariable regression models; ROC is receiver-operating
characteristic curve area; p1 and 2 are the p-values on the
coefficients of predictors 1 and 2, respectively, in the regression
models, and * denotes p<0.05 for addition of independent
information.
[0067] It is important to note that COSEn, which we developed to
distinguish AF from NSR, does not add information to the new
measures in detecting CHF. We implemented these predictive models
in the MIT-BIH databases, and represented each Holter monitor
record as a single measure based on analysis of 12 beats every 2400
beats. For the MIT-BIH AF records, we found that several had
paroxysmal AF and fewer than 40 measures were available from the
record. Accordingly, we added 20 24-hour Holter monitors from the
University of Virginia (UVA) Heart Station that showed only AF.
[0068] FIGS. 6 and 7 show scatter plots of old and new measures. AF
records are shown in black squares and combine records from the
MIT-BIH AF database and from our own UVA Holter database. The other
symbols are all from the MIT-BIH databases--NSR (red dots) and the
CHF databases, containing 32 patients with severe CHF (classes III
and IV, right-side up green triangles) and 12 patients with
somewhat less severe CHF (classes I and II, upside down blue
triangles).
[0069] FIG. 6 illustrates a scatter plot of conventional HRV
measures for 12-beat segments sampled every 2400 beats for records
in MIT-BIH AF, NSR and CHF databases, and 20 AF records from UVA.
The scatter plot includes the results of standard measures of heart
rate (x-axis, mean RR interval) and heart rate variability (mean of
the standard deviation). Though HRV has been known for years to
distinguish among these clinical conditions, we find little useful
information in these settings.
[0070] FIG. 7 shows improved distinction using COSEn (y-axis) and a
new measure, the average of a predictive model based on interval
match count histograms.
[0071] As we have previously disclosed, COSEn separates AF well
from the other records, which all are NSR but with varying degrees
of CHF by clinical criteria. COSEn is not as effective, however, in
detecting CHF.
[0072] In FIG. 7, there is good distinction among the NSR and CHF
groups, consistent with the idea that the matching algorithm sorts
records into a hierarchy along a gradient from no CHF to severe
symptoms. AF is also distinguished on the y-axis due to the
incorporation of COSEn data into the regression model.
[0073] To further test the idea that the new measure changed
smoothly over a range of CHF severity, we compared records of
patients from the MIT-BIH database with NYHA class I/II or class
III/IV CHF. The box plot of FIG. 8 shows that there was a trend for
higher values for the new algorithm in the more severe class III
and IV CHF patients.
[0074] New entropy-based measures of RR interval time series give
information on the presence and degree of CHF. Remarkably good
diagnostic performance is available from only sparsely sampled data
sets--12 beats every 30 minutes. This new set of measures should be
useful in implanted devices with even a single ventricular lead to
help monitor CHF in patients at risk.
[0075] Turning to FIG. 1, it is contemplated that embodiments of
the invention may be practiced using a computer system. FIG. 1 is
an illustrative block diagram for a computer system 100 for
implementation of an exemplary embodiment or portion of an
embodiment of present invention. For example, a method or system of
an embodiment of the present invention may be implemented using
hardware, software or a combination thereof and may be implemented
in one or more computer systems or other processing systems, such
as personal digit assistants (PDAs). In an example embodiment, the
invention was implemented in software running on a general purpose
computer 100 as illustrated in FIG. 1. The computer system 100 may
includes one or more processors, such as processor 104. The
Processor 104 is connected to a communication infrastructure 106
(e.g., a communications bus, cross-over bar, or network). The
computer system 100 may include a display interface 102 that
forwards graphics, text, and other data from the communication
infrastructure 106 (or from a frame buffer not shown) for display
on the display unit 830.
[0076] The computer system 10 may also include a main memory 108,
preferably random access memory (RAM), and may include a secondary
memory 110. The secondary memory 110 may include, for example, a
hard disk drive 112 and/or a removable storage drive 114,
representing a floppy disk drive, a magnetic tape drive, an optical
disk drive, a flash memory, etc. The removable storage drive 114
reads from and/or writes to a removable storage unit 118 in a well
known manner. Removable storage unit 118, represents a floppy disk,
magnetic tape, optical disk, etc. which is read by and written to
by removable storage drive 114. As will be appreciated, the
removable storage unit 118 includes a computer usable storage
medium having stored therein computer software and/or data.
[0077] In alternative embodiments, secondary memory 110 may include
other means for allowing computer programs or other instructions to
be loaded into computer system 100. Such means may include, for
example, a removable storage unit 122 and an interface 120.
Examples of such removable storage units/interfaces include a
program cartridge and cartridge interface (such as that found in
video game devices), a removable memory chip (such as a ROM, PROM,
EPROM or EEPROM) and associated socket, and other removable storage
units 122 and interfaces 120 which allow software and data to be
transferred from the removable storage unit 122 to computer system
100.
[0078] The computer system 100 may also include a communications
interface 124. Communications interface 124 allows software and
data to be transferred between computer system 100 and external
devices. Examples of communications interface 824 may include a
modem, a network interface (such as an Ethernet card), a
communications port (e.g., serial or parallel, etc.), a PCMCIA slot
and card, a modem, etc. Software and data transferred via
communications interface 124 are in the form of signals 828 which
may be electronic, electromagnetic, optical or other signals
capable of being received by communications interface 124. Signals
128 are provided to communications interface 124 via a
communications path (i.e., channel) 126. Channel 126 (or any other
communication means or channel disclosed herein) carries signals
128 and may be implemented using wire or cable, fiber optics, blue
tooth, a phone line, a cellular phone link, an RF link, an infrared
link, wireless link or connection and other communications
channels.
[0079] In this document, the terms "computer program medium" and
"computer usable medium" are used to generally refer to media or
medium such as removable storage drive 114, a hard disk installed
in hard disk drive 112, and signals 128. These computer program
products are means for providing software to computer system 100.
The computer program product may comprise a computer usable medium
having computer program logic thereon. The invention includes such
computer program products. The "computer program product" and
"computer usable medium" may be any computer readable medium having
computer logic thereon.
[0080] Computer programs (also called computer control logic or
computer program logic) may be stored in main memory 108 and/or
secondary memory 110. Computer programs may also be received via
communications interface 124. Such computer programs, when
executed, enable computer system 100 to perform the features of the
present invention as discussed herein. In particular, the computer
programs, when executed, enable processor 104 to perform the
functions of the present invention. Accordingly, such computer
programs represent controllers of computer system 100.
[0081] In an embodiment where the invention is implemented using
software, the software may be stored in a computer program product
and loaded into computer system 100 using removable storage drive
114, hard drive 112 or communications interface 124. The control
logic (software), when executed by the processor 104, causes the
processor 104 to perform the functions of the invention as
described herein.
[0082] In another embodiment, the invention is implemented
primarily in hardware using, for example, hardware components such
as application specific integrated circuits (ASICs). Implementation
of the hardware state machine to perform the functions described
herein will be apparent to persons skilled in the relevant
art(s).
[0083] In yet another embodiment, the invention is implemented
using a combination of both hardware and software.
[0084] In an example software embodiment of the invention, the
methods described above may be implemented in SPSS control language
or C++ programming language, but could be implemented in other
various programs or other programs known to those skilled in the
art.
[0085] Embodiments of the present disclosure may extend the entropy
estimation to two or more simultaneous time series of parameters
measured by the device. The present disclosure relates
cross-entropy measures optimized for simultaneous time series for
data using a different scale and of a different character. These
cross-entropy approaches may be extended to multiple simultaneous
time series recorded by implantable devices, and require
optimizations because of the differences in sampling rates, scales,
and dynamics inherent in the recordings of HR, HR parameters
(especially the entropy measures developed elsewhere in this
specification), patient activity, body temperature, AF and other
arrhythmia burden, trans-thoracic impedance, and intra-cardiac
pressures.
[0086] Thus, a part of some embodiments is to not only include
entropy rate estimates of RR intervals and other physiologic
signals, but to incorporate global measures of entropy rate for the
entire collection of series. We developed multidimensional entropy
estimates for this task.
[0087] For the case of two signals, measures of entropy rate have
already been developed that extend SampEn to Cross-SampEn. This
method requires that the two signals have similar scale and
location, which can be achieved by first standardizing each signal
by subtracting the mean and dividing by the standard deviation.
This step then allows candidate match intervals from the first
signal to be sought in the second signal. By accumulating the total
number of cross-matches of various interval lengths m, conditional
probabilities and densities can be calculated and natural
logarithms taken in the same manner as SampEn and differential or
quadratic entropy rate. One approach to analyzing more than two
signals for detecting CHF is to calculate all pair-wise
combinations of the cross-entropy rate.
[0088] Some embodiments include several enhancements to estimating
entropy rate for p signals, where p>1. First, the idea of an
interval vector of length m can be extended to a interval matrix of
size m times p. In this case, each column represents the
corresponding standardized signal value at a particular time. In an
analogous way to SampEn, interval matrix matches will require that
all elements be within a specified tolerance r. Proceeding in this
way leads to a global measure of entropy rate.
[0089] A second approach is to use ways of determining matches
using distance measures other than a simple tolerance requirement
component by component, for example Euclidean distance (the square
root of the sum of the component distances squared). This approach
recognizes that simply standardizing the signals has limitations
and does not incorporate the correlations among the signals. An
improved distance measure (used previously in nearest-neighbor
analysis) between the p signals at two particular times can be
achieved using the Mahalanobis distance which specifically accounts
for the correlation. This distance first multiplies the signal
vector by a matrix that makes the components uncorrelated and then
taking the Euclidean distance.
[0090] In implanted cardiac devices and in many other kinds of
monitors, signals are sampled at different rates. For the
multidimensional entropy measures reported here, the signal
information is summarized at specified time increments (hourly, for
example) for inclusion on the mathematical calculations described.
The summarization may be a robust marker of the central value such
as the median, or detect abnormalities of interest. Since, for
example, reduced HRV is associated with illness but may visit many
levels of variability in the course of an hour, a suitable summary
measure is the 10.sup.th percentile lowest value observed. Thus,
the summarization strategies play key roles in the multidimensional
detection schemes.
[0091] Some of the measures may vary by time of day, so clock time
is another important dimension to include in the final predictive
schemes. Patients known to have episodes of arrhythmia such as
atrial fibrillation may generate misleading monitoring results if
AF is not detected and quantified, as naive measures of HRV will
return spuriously normal results. Thus, families of predictive
algorithms tailored for past findings are required.
[0092] Those of ordinary skill may vary the methods and apparatus
for detecting an abnormal cardiac rhythm and patient clinical
status without varying from the scope of the invention as defined
in the appended claims.
* * * * *
References