U.S. patent application number 11/500871 was filed with the patent office on 2008-03-06 for categorizing automatically generated physiological data based on industry guidelines.
This patent application is currently assigned to Zargis Medical Corp. Invention is credited to Raymond L. Watrous.
Application Number | 20080058607 11/500871 |
Document ID | / |
Family ID | 39152731 |
Filed Date | 2008-03-06 |
United States Patent
Application |
20080058607 |
Kind Code |
A1 |
Watrous; Raymond L. |
March 6, 2008 |
Categorizing automatically generated physiological data based on
industry guidelines
Abstract
Methods and systems for mapping a physiological signal into
clinical guideline parameters are disclosed. A physiological signal
having a characteristic that may represent an anomaly is received
and mapped to a clinical guideline condition space. Probabilities
are determined that the mapped signal with which the anomaly may be
associated represents a first clinical guideline condition
corresponding to a referral indication or a second clinical
guideline condition corresponding to an absence of the referral
indication. The determined probability is presented and a referral
decision is made responsive to the determined probability that the
anomaly is associated with the first clinical guideline
condition.
Inventors: |
Watrous; Raymond L.; (Belle
Mead, NJ) |
Correspondence
Address: |
RATNERPRESTIA
P O BOX 980
VALLEY FORGE
PA
19482-0980
US
|
Assignee: |
Zargis Medical Corp
|
Family ID: |
39152731 |
Appl. No.: |
11/500871 |
Filed: |
August 8, 2006 |
Current U.S.
Class: |
600/300 |
Current CPC
Class: |
G16H 70/20 20180101;
A61B 7/04 20130101; G16H 50/20 20180101 |
Class at
Publication: |
600/300 |
International
Class: |
A61B 5/00 20060101
A61B005/00 |
Claims
1. A method for mapping a physiological signal into clinical
guideline parameters, the method comprising: receiving the
physiological signal, the physiological signal having a
characteristic that may represent an anomaly; mapping the received
physiological signal to a clinical guideline condition space;
determining a probability that the mapped signal with which the
anomaly is associated represents one of a first clinical guideline
condition corresponding to a referral indication or a second
clinical guideline condition corresponding to an absence of the
referral indication; and presenting the determined probability to a
user, whereby a referral decision is made responsive to the
determined probability that the anomaly is associated with the
first clinical guideline condition.
2. The method according to claim 1, wherein the determined
probability is presented as a measure of a sensitivity of the
determined probability to variations in the physiological
signal.
3. The method according to claim 2, wherein the measure of the
sensitivity includes a range variance indicative of a confidence
measure.
4. The method according to claim 1, wherein the step of presenting
the determined probability presents derived features relative to a
probability surface including a first region representing the first
clinical guideline condition and a second region representing the
second guideline condition.
5. The method according to claim 4, wherein the step of presenting
the determined probability further presents the derived features
relative to at least one predetermined probability threshold
between the first region and second region.
6. A method for mapping a physiological signal into clinical
guideline parameters for distinguishing between a first condition
corresponding to a referral indication and a second condition
corresponding to an absence of the referral indication, the method
comprising the steps of: receiving the physiological signal, the
physiological signal including a characteristic that may contain an
anomaly representing a referral indication, the anomaly being
within at least one predetermined physiological event of the
physiological signal; computing a normalized energy profile for the
at least one predetermined physiological event representative of
the anomaly; mapping the normalized energy profile to a clinical
guideline condition space; determining probabilities that the
mapped normalized energy profile is associated with each of the
first condition and the second condition; and presenting the
determined probabilities.
7. The method according to claim 6, wherein the physiological
signal includes a phonocardiogram signal and the predetermined
physiological event includes at least one of a systolic interval
and a diastolic interval.
8. A method for assisting in a referral indication of heart
murmurs, the method comprising the steps of: receiving an acoustic
signal representing heart sounds; parsing the received acoustic
signal into predetermined physiological events including a
plurality of systolic intervals and a plurality of diastolic
intervals; computing a first normalized mid-range energy
representative of a median systolic interval and a second
normalized mid-range energy representative of a median diastolic
interval, respectively, the first and second normalized mid-range
energies computed from the parsed acoustic signal; characterizing
the median systolic interval based on the first normalized
mid-range energy; determining probabilities relative to a first
condition associated with the referral indication and a second
condition associated with an absence of the referral indication
based on the characterized median systolic interval; and presenting
the first normalized mid-range energy, the second normalized
mid-range energy and the determined probabilities, whereby the
presented first normalized mid-range energy, the second normalized
mid-range energy and the determined probabilities may be used to
determine whether a referral is indicated.
9. The method according to claim 8, the method including the steps
of: characterizing the median diastolic interval based on the
second normalized mid-range energy; determining further
probabilities relative to a diastolic murmur condition and a
non-diastolic murmur condition; and the step of presenting further
includes presenting the further determined probabilities.
10. A computer readable carrier including computer program
instructions that cause a computer to perform the method according
to claim 8.
11. The method according to claim 8, wherein the first and second
normalized mid-range energies are each between 150 and 600 Hz.
12. The method according to claim 8, wherein the first and second
normalized mid-range energies are converted to respective murmur
grades and the step of presenting the first and second normalized
mid-range energies presents the respective murmur grades.
13. The method according to claim 8, wherein the first condition
includes systolic murmurs selected from the group consisting of a
holo-systolic murmur, a late-systolic murmur and a mid-systolic
murmur equal or greater than a murmur grade 3.
14. The method according to claim 8, wherein the step of
characterizing the median systolic interval computes a focal point
of energy using a maximum first normalized mid-range energy and
systolic interval temporal center of a normalized mid-range energy
concentration to generate a model of the median systolic
interval.
15. The method according to claim 8, wherein the step of presenting
the determined probabilities includes presenting a range variance
representing an uncertainty measure.
16. The method according to claim 8, wherein the step of presenting
the determined probabilities presents derived features relative to
a probability surface, the probability surface including a first
region representing the first condition and a second region
representing the second condition.
17. The method according to claim 16, wherein the probability
surface includes a predetermined threshold between the first region
and the second region, the predetermined threshold selected
according to a predetermined sensitivity and a predetermined
specificity.
18. The method according to claim 17, wherein the probability
surface is presented as a function of a duration of the median
systolic interval and the first normalized mid-range energy
associated with the median systolic interval.
19. The method according to claim 8, the step of computing the
first normalized mid-range energy and second normalized mid-range
energy includes the steps of: dividing each of the median systolic
interval and median diastolic intervals into a plurality of
subintervals; and calculating the first normalized mid-range energy
and second normalized mid-range energy for each of the subintervals
of the respective median systolic interval and median diastolic
interval.
20. The method according to claim 19 wherein: the step of dividing
each of the median systolic interval and median diastolic intervals
into a plurality of subintervals includes the steps of: subdividing
the median systolic interval into at least thirty intervals;
subdividing the median diastolic interval into at least thirty
intervals, and the step of presenting the first and second
normalized mid-range energies includes the step of graphically
displaying the calculated normalized mid-range energy for each of
the plurality of sub-intervals.
21. The method according to claim 8, including the step of
detecting heart murmurs using the parsed audio signal and the first
normalized mid-range energy and second normalized mid-range
energy.
22. The method according to claim 21, the step of detecting heart
murmurs calculates Bayesian statistics for the parsed audio signal
and the first and second normalized mid-range energies.
23. A system for mapping a heart sound signal to clinical guideline
referral indicators, the system comprising: an input terminal for
receiving the heart sound signal including systolic intervals and
diastolic intervals; a murmur detector for computing a statistic
that a murmur is present from the heart sound signal; a normalized
mid-range energy calculator for computing a normalized mid-range
energy profile from the heart sound signal, the normalized
mid-range energy profile representative of a median systolic
interval of the heart sound signal; a clinical guideline referral
converter for mapping the normalized mid-range energy profile to a
first clinical guideline condition associated with a referral
indication and a second clinical guideline condition associated
with an absence of the referral indication and determining
probabilities that the normalized mid-range energy profile is
associated with the first or the second clinical guideline
conditions; and a display for displaying the normalized mid-range
energy profile, the murmur detection statistic, and the determined
probabilities.
24. The system according to claim 23, wherein the normalized
mid-range energy calculator computes a further normalized mid-range
energy profile from the heart sound signal, the further normalized
mid-range energy profile representative of a median diastolic
interval of the heart sound signal, the system includes a diastolic
murmur converter for mapping the further normalized mid-range
energy profile to a diastolic murmur condition and a non-diastolic
murmur condition and determining further probabilities that the
further normalized mid-range energy profile is associated with the
diastolic murmur condition or the non-diastolic murmur condition,
and the display displays the further normalized mid-range energy
profile and the determined further probabilities.
25. A display for a clinical device, the display comprising: a
first normalized mid-range energy profile associated with a median
systolic interval of a heart sound signal; a second normalized
mid-range energy profile associated with a median diastolic
interval of the heart sound signal; at least one probability
indicator which indicates that the median systolic interval
represents a first clinical guideline condition associated with a
referral indication and a second clinical guideline condition
associated with an absence of the referral indication; and murmur
detection indicators indicating the detection of murmurs in the
heart sound signal.
26. The display according to claim 25, wherein the display includes
a representation of the heart sound signal and the murmur detection
indicator is presented relative to the representation of the heart
sound signal.
27. The display according to claim 25, wherein the display includes
hemodynamic parameter information.
28. The display according to claim 25, wherein the display includes
auscultation recording information including at least one of a
predetermined recording location and a predetermined posture.
29. The display according to claim 25, wherein the first and second
normalized mid-range energy profiles are presented as respective
bar graphs.
30. The display according to claim 25, wherein the at least one
probability indicator includes a range variance representing an
uncertainty measure.
31. The display according to claim 25, wherein the display includes
a further probability indicator which indicates that the median
diastolic interval represents a diastolic murmur condition and a
non-diastolic murmur condition.
32. The display according to claim 25, wherein the at least one
probability indicator includes a probability surface configured for
the median systolic interval, the probability surface including at
least one probability threshold between predetermined regions
representing the first clinical guideline condition and second
clinical guideline conditions, respectively.
33. The display according to claim 32, wherein the probability
surface is presented as a function of a duration of the median
systolic interval and a first normalized mid-range energy from the
first normalized mid-range energy profile.
34. The display according to claim 33, wherein the at least one
probability indicator includes a focal center of energy intensity
indicator representing a temporal and energy position of the median
systolic interval relative to the probability surface.
Description
FIELD OF THE INVENTION
[0001] The present invention relates to categorizing physiological
data in general and, specifically, to systems and methods for
mapping heart sounds to clinical guideline referral conditions.
BACKGROUND OF THE INVENTION
[0002] A wide variety of medical diagnostic decision support
systems are used in health care. These systems can generally record
and process physiological data to present physiological features to
assist a health care professional in determining the presence of a
pathophysiological condition. One example of a medical diagnostic
support system is an auscultation system that extracts features
from a phonocardiogram. These auscultatory features are known in
clinical practice and are readily understood by practicing
physicians. Examples of such auscultatory features include the
first heart sound (S1), second heart sound (S2), third heart sound
(S3), fourth heart sound (S4), heart murmurs, S2 splitting,
ejection sounds, opening snaps, and midsystolic clicks.
[0003] An objective of deriving physiological features such as
auscultatory features is to provide the health care professional,
such as a physician, with accurate information that can be used in
making a diagnostic decision. At the primary care level, this
typically depends on whether the physiological features are
indicative of a condition necessitating a referral to a specialist
for further evaluation.
[0004] Medical diagnostic decision support systems may attempt to
identify specific features that are indicative of a specific
pathophysiological condition. An auscultatory system, for example,
may identify specific properties of a phonocardiogram that are
consistent with a specific cardiovascular disease. This
auscultatory system may be developed with the expectation that the
physician would then refer for further evaluation patients with
heart sounds that are generated by pathophysiological
conditions.
[0005] The relationship between features extracted from
physiological data and a diagnosis of a pathophysiological
condition, however, is complex. It may be desirable to take into
account additional information about the patient, such as medical
history, symptoms, vital signs and the results of other tests, such
as an X-Ray, electrocardiogram (EKG) in order to make a referral
decision. By incorporating additional information, a physician may
be better equipped to diagnose a pathophysiological condition,
particularly if one of the examined features provides conflicting
information as compared to the additional information. The
additional information may be used to help reduce unnecessary
referral decisions. It may also be desirable to automate a portion
of the analysis used by physicians to make a referral decision. In
this manner, a number of different information sources may be
analyzed, integrated and presented to the physician for referral
review.
SUMMARY OF THE INVENTION
[0006] The present invention is embodied in a method for mapping a
physiological signal into clinical guideline parameters. The method
receives a physiological signal. The physiological signal has a
characteristic that may represent an anomaly. The method maps the
received physiological signal to a clinical guideline condition
space. The method further determines a probability that the mapped
signal with which the anomaly is associated represents one of a
first clinical guideline condition corresponding to a referral
indication or a second clinical guideline condition corresponding
to an absence of the referral indication. The method also presents
the determined probability to a user.
[0007] The present invention is further embodied in a method for
mapping a physiological signal into clinical guideline parameters
for distinguishing between a first condition corresponding to a
referral indication and a second condition corresponding to an
absence of the referral indication. The method receives the
physiological signal. The physiological signal including a
characteristic that that may contain an anomaly representing a
referral indication. The anomaly is within at least one
physiological event of the physiological signal. The method also
computes a normalized energy profile for the at least one
physiological event representative of the anomaly and maps the
normalized energy profile to a clinical guideline condition space.
The method also determines probabilities that the mapped normalized
energy profile is associated with each of the first condition and
the second condition and presents the determined probabilities.
[0008] The present invention is further embodied in a method for
assisting in a referral indication of heart murmurs. The method
receives an acoustic signal representing heart sounds and parses
the received acoustic signal into physiological events including a
plurality of systolic intervals and a plurality of diastolic
intervals. The method further computes a first normalized mid-range
energy representative of a median systolic interval and a second
normalized mid-range energy representative of a median diastolic
interval, respectively. The first and second normalized mid-range
energies are computed from the parsed acoustic signal. The method
also characterizes the median systolic interval based on the first
normalized mid-range energy. The method further determines
probabilities relative to a first condition associated with the
referral indication and a second condition associated with an
absence of the referral indication based on the characterized
median systolic interval. The method further presents the first
normalized mid-range energy, the second normalized mid-range energy
and the determined probabilities. The presented first normalized
mid-range energy, the second normalized mid-range energy and the
determined probabilities may be used to determine whether a
referral is indicated.
[0009] The present invention is further embodied in a system for
mapping a heart sound signal to clinical guideline referral
indicators. The system includes an input terminal for receiving the
heart sound signal comprising systolic intervals and diastolic
intervals and a murmur detector for computing a statistic that a
murmur is present from the heart sound signal. The system further
includes a normalized mid-range energy calculator for computing a
normalized mid-range energy profile from the heart sound signal.
The normalized mid-range energy profile is representative of a
median systolic interval of the heart sound signal. The system
further includes a clinical guideline referral converter for
mapping the normalized mid-range energy profile to a first clinical
guideline condition associated with a referral indication and a
second clinical guideline condition associated with an absence of
the referral indication and determining probabilities that the
normalized mid-range energy profile is associated with the first or
the second clinical guideline conditions. The system further
includes a display for displaying the normalized mid-range energy
profile, the murmur detection statistic, and the determined
probabilities.
[0010] The present invention is further embodied in a display for a
clinical device. The display includes a first normalized mid-range
energy profile associated with a median systolic interval of a
heart sound signal and a second normalized mid-range energy profile
associated with a median diastolic interval of the heart sound
signal. The display also includes at least one probability
indicator which indicates that the median systolic interval
represents a first clinical guideline condition associated with a
referral indication and a second clinical guideline condition
associated with an absence of the referral indication. The display
further includes murmur detection indicators indicating the
detection of murmurs in the heart sound signal.
BRIEF DESCRIPTION OF THE DRAWINGS
[0011] The invention is best understood from the following detailed
description when read in connection with the accompanying drawing.
It is emphasized that, according to common practice, the various
features of the drawing are not to scale. On the contrary, the
dimensions of the various features are arbitrarily expanded or
reduced for clarity. Included in the drawing are the following
figures:
[0012] FIG. 1 is a flowchart illustrating an exemplary method for
mapping a physiological signal into clinical data according to an
aspect of the present invention;
[0013] FIG. 2 is a functional block diagram illustrating an
exemplary system for mapping heart sound signals to clinical
guideline referral indicators according to an aspect of the present
invention;
[0014] FIG. 3 is a flowchart illustrating an exemplary method for
mapping heart sounds to clinical guideline referral conditions
according to an aspect of the present invention;
[0015] FIG. 4 is a flowchart illustrating an exemplary method for
computing normalized energies representative of a median systolic
interval and a median diastolic interval of a heart signal
according to an aspect of the present invention;
[0016] FIG. 5 is an example display of normalized energy
representative of a median systolic interval generated using the
exemplary method shown in FIG. 4 according to an aspect of the
present invention;
[0017] FIG. 6 is a flowchart illustrating an exemplary method for
characterizing a median systolic interval according to an aspect of
the present invention;
[0018] FIG. 7 is a flowchart illustrating an exemplary method for
converting a characterized median systolic interval to clinical
guideline referral probabilities according to an aspect of the
present invention;
[0019] FIG. 8 is a flowchart illustrating an exemplary method for
presenting clinical guideline probabilities relative to a
probability surface according to an aspect of the present
invention;
[0020] FIG. 9 is an example display of clinical guideline
probabilities relative to a probability surface using the
normalized energy of FIG. 5 and the exemplary method shown in FIG.
8; and
[0021] FIG. 10 is an example display of the exemplary system shown
in FIG. 2 that employs an embodiment of the present invention.
DETAILED DESCRIPTION OF THE INVENTION
[0022] There are known diagnostic decision support systems which
attempt to diagnose a pathophysiological condition or to diagnose a
likelihood of a pathophysiology on the basis of a single
physiological signal. It is desirable, however, for a decision
support system that does not attempt to develop a diagnosis based
on a physiological signal but instead analyzes features of the
physiological signal to provide data that can be integrated by a
physician along with other patient information that is relevant to
making a diagnosis or referral decision.
[0023] In the art there are known industry referral guidelines,
such as guidelines established by the American College of
Cardiology (ACC) and American Heart Association (AHA) ACC/AHA which
provide a physician with recommendations for making a referral
decision. It is thus desirable to provide a diagnostic decision
support system that presents analyzed features in accordance with
industry guidelines such as ACC/AHA guidelines. In this manner, the
physician may follow the industry guidelines to make a referral
decision.
[0024] FIG. 1 shows a flowchart illustrating an exemplary method
for mapping a physiological signal to clinical guideline parameters
according to the present invention. In step 100, physiological data
is received. The physiological data may have a characteristic that
indicates an anomaly, for example a systolic murmur. In step 102,
the received physiological signal is mapped to a clinical guideline
condition space. The clinical guideline condition space is
desirably associated with predetermined clinical guidelines, i.e.
indications, for a condition related to the anomaly developed to
assist a physician with making a referral decision. In step 104, a
probability is determined that the mapped physiological signal
represents a first clinical guideline condition. This condition is
associated with a referral guideline condition. In step 106, a
probability is determined that the mapped physiological signal
represents a second clinical guideline condition. This second
condition is associated with an absence of a referral condition. In
step 108, the resulting probabilities are presented. The second
condition may also be associated with indications that a referral
is not advisable. For example, if a mid-systolic murmur is detected
having less than grade 2 loudness and no other significant
auscultatory features, this may represent a functional murmur where
a referral is not advisable. In alternate step 110, derived
features may be presented relative to a predetermined probability
surface. The derived features relate to the clinical guideline
space and thus allow a physiological signal to be mapped to a space
over which clinical guidelines are interpretable.
[0025] In an exemplary embodiment, ACC/AHA clinical guidelines by
Bonow et al. entitled "ACC/AHA guidelines for the management of
patients with valvular heart disease: executive summary. A report
of the American College of Cardiology/American Heart Association
Task Force on Practice Guidelines (Committee on Management of
Patients with Valvular heart Disease) published Circulation in 1998
are used as the clinical guideline space for determining whether to
refer an asymptomatic patient for evaluation by echocardiography
when that patient has a murmur of unknown origin. These ACC/AHA
guidelines are written in terms of three classes which vary in
expected efficacy and safety for an echocardiogram referral (herein
echo).
[0026] In an exemplary embodiment, heart sound signals are of
interest. The heart sound signal may be separated into repeatable
physiological events, typically the first heart sound, S1, caused
by the closing of the atrioventricular valves, followed by the
second heart sound, S2, caused by the closing of the semilunar
valves. Intervals of interest are the systolic interval, defined
from the end of S1 to the onset of S2, and the diastolic interval,
defined from the end of S2 to the onset of S1.
[0027] In a first condition according to the present invention,
i.e. a class I condition, there is evidence and/or general
agreement that a given procedure or treatment is useful and
effective. Patients with murmurs that have certain well-defined
auscultatory findings, according to the ACC/AHA guidelines,
described below, fall into the first condition, where evaluation by
echo is deemed useful and effective.
[0028] In a second condition according to the present invention,
i.e. a class III condition, there is evidence and/or general
agreement that the procedure/treatment is not useful and in some
cases may be harmful. Asymptomatic patients with other well-defined
auscultatory features, according to the ACC/AHA guidelines,
described below, fall into the second condition.
[0029] In a further condition, i.e. class II according to clinical
guideline standards, conditions exist for which there is
conflicting evidence and/or a divergence of opinion about the
usefulness/efficacy of a procedure or treatment. Class II can be
further separated into a class IIa and class IIb. Under class IIa,
the weight of evidence/opinion is in favor of usefulness/efficacy.
Under class IIb, the usefulness/efficacy is less well established
by evidence/opinion. The present invention does not distinguish
class II conditions. Rather, the present invention maps the
physiological signal to the class I (first condition) and class III
(second condition) space. A physician can then interpret the
probability findings as well as other presented information to help
distinguish class II conditions. Class II conditions may correspond
to findings that are not related to auscultation, and may be
derived from sensors. For example, murmurs associated with abnormal
EKG use EKG sensors to distinguish murmurs in this class. It is
contemplated, however, that the present invention may also
distinguish Class II conditions by incorporating other input
information and class II condition guideline parameters into an
exemplary method as described below.
[0030] The ACC/AHA have assigned diastolic or continuous murmurs to
the first condition. In addition, holosystolic or late systolic
murmurs meet with the first condition. Finally, midsystolic murmurs
having a grade 3 or higher loudness are also assigned to the first
condition.
[0031] The ACC/AHA have assigned midsystolic murmurs of grade 2 or
lower loudness identified as innocent or functional by an
experienced observer to the second condition. The guidelines
further stipulate that such innocent murmurs have the following
characteristics: a grade 1 to 2 intensity at the left sternal
border, a systolic ejection pattern, a normal intensity and
splitting of the second heart sound, no other abnormal sounds or
murmurs, no evidence of ventricular hypertrophy or dilation and the
absence of increased murmur with the Valsalva maneuver.
[0032] According to the ACC/AHA guidelines, a murmur profile
(early, mid, late, holo) and/or murmur loudness are used to
distinguish between the first condition and the second condition in
the case of systolic murmurs. In an exemplary embodiment of the
present invention, a murmur energy for the systolic interval is
desirably used to map the auscultatory signal to the clinical
guideline condition space. It is noted that the presence of a
continuous or diastolic murmur is evidence of the first condition
and a further mapping may not be needed to support the ACC/AHA
guidelines.
[0033] Murmur energy for the diastolic interval, however, as well
as a combination of the systolic and diastolic interval, for
continuous murmurs, may be used to map the acoustic signal to a
first predetermined condition space and a second predetermined
condition space. The first and second predetermined condition
spaces may be associated with murmur energy and non-murmur energy,
respectively. It is contemplated that the second condition space
may be associated with nonreferrable murmur energy, for example,
the nonreferrable murmur energy may correspond to an innocent
murmur. The murmur energy may be associated with continuous and/or
diastolic murmurs. The first and second condition spaces may be
determined from signals having known diastolic and/or continuous
murmurs and signals not including diastolic and/or continuous
murmurs.
[0034] Although an exemplary embodiment illustrates computing an
energy profile of heart murmurs, it is contemplated that the
present invention may be used with other physiological data. For
example low frequency heart sounds such as S3 and S4 may be
determined from an energy measure. These sounds are typically in
the range of 50-90 Hz making them difficult to detect by a
physician using standard auscultatory practices. Other
physiological sounds may include peristaltic sounds such as renewal
for bowel action post-surgery and lung sounds for characterizing
crackles and wheezes. The present invention may be used with any
physiological sound where an energy profile of a physiological
feature may be used to map the sound into a guideline space.
[0035] FIG. 2 is a functional block diagram of an exemplary system
200 for mapping heart sound signals to clinical guideline referral
indicators according to the present invention. The system shown in
FIG. 2 includes many of the elements of the system described in
U.S. Pat. Nos. 6,572,560 and 6,953,436 entitled MULTI-MODAL CARDIAC
DIAGNOSTIC DECISION SUPPORT SYSTEM AND METHOD, which describes
cardiac diagnostic systems.
[0036] The present invention, however, includes additional features
related to the mapping of heart sounds to clinical guideline
referral indicators based on analysis of mid-range energy in
acoustic heart signals. In exemplary system 200, heart sounds are
detected by phonocardiograph instrument (PCG) 202, which may be,
for example, an electronic stethoscope. Output signals provided by
PCG 202 may be amplified and filtered by a preamplifier, filter or
any combination thereof (not shown) to increase the amplitude of
signals that are in a range of frequencies corresponding to heart
sounds while attenuating signals outside of that frequency range.
The preamplification and/or filtering may be performed within PCG
202.
[0037] Time-frequency analysis circuit 204 receives the signals
provided by PCG 202 and analyzes these signals using, for example,
a wavelet decomposition to extract frequency information from the
signal. Although an exemplary embodiment described below employs a
wavelet transform and a Morlet wavelet, it is contemplated that
other time-frequency analysis methods may be used and that other
wavelets may be used. The wavelet decomposition is desirably scaled
to compensate for variations in amplitude of the acoustic heart
sounds provided by PCG 202. The wavelet decomposition may be
sampled logarithmically. In an exemplary embodiment, magnitude
squared wavelet coefficients are computed and scaled to compensate
for logarithmic frequency spacing. The output data of the wavelet
decomposition circuit is applied to feature extraction circuit 206
and to a circuit 208 that calculates a normalized mid-range energy
(NMRE) of the acoustic heart sounds.
[0038] Feature extraction circuit 206 receives the signals provided
by the wavelet decomposition of circuit 204 and identifies basic
heart sounds, clicks and murmurs. In an exemplary embodiment,
feature extraction circuit 206 uses Mel cepstrum signal analysis.
MEL cepstrum signal analysis is well known in speech analysis. For
example, see U.S. Pat. No. 6,725,190 entitled "Method and system
for speech recognition features, pitch and voicing with resampled
basis functions providing reconstruction of the spectral envelope."
The Mel cepstral coefficients may include total energy and first
and second differences. Cepstral mean subtraction may be
implemented to remove channel differences such as filtering by PCG
202. Features extracted by the MEL cepstrum signal analysis are
provided to sequence interpretation circuit 210.
[0039] In an alternate embodiment, a feature extraction circuit 206
may use a neural network trained from labeled examples of heart
sounds generated by experts in auscultation. The neural network
feature extraction circuit 206 is desirably of the time-delay type,
where the input layer, number of layers, unit function, and initial
weight selection are appropriately chosen using well-known methods.
Although a neural network of time-delay type is utilized, it is
contemplated that other types of neural networks may be
employed.
[0040] Sequence interpretation circuit 210 parses the extracted
features from feature extraction circuit 206 using a
state-transition model of the heart to determine the most probable
sequence of cardiac events. The state machine may be a hidden
Markov model (HMM) or may be another type of state transition
model. The output of sequence interpretation circuit 210 is applied
to duration and phase measurement circuit 212.
[0041] Duration and phase measurement circuit 212 computes the
average state durations of the sequence model, murmur duration and
phase alignments. The output data of the duration and phase
measurement circuit is applied to NMRE circuit 208 and to murmur
detection circuit 214.
[0042] NMRE circuit 208 desirably calculates mid-range energy using
the wavelet decomposition from time-frequency analysis circuit 204
over the frequency region where the majority of anomalous heart
murmurs may be found. A method for computing a NMRE is disclosed in
copending U.S. patent application Ser. No. 11/037,665 entitled
COMPUTER-ASSISTED DETECTION OF SYSTOLIC MURMURS ASSOCIATED WITH
HYPERTROPHIC CARDIOMYOPATHY. Wavelet decomposition scales may
correspond to the frequency region of 150-600 Hz or more
particularly the range of 206 Hz-566 Hz. The wavelet decomposition
scales of interest are summed together over the duration of the
heart signal to represent the energy in the bandwidth of interest
across the entire recorded heart sound signal.
[0043] The energy computed in NMRE circuit 208 may be dependent
upon the recording level, signal artifacts, or heart signal
transmission strength from the chest wall to PCG 202. NMRE circuit
208 also normalizes the mid-range energy for a desired interval. In
an exemplary embodiment, the system normalizes the mid-range energy
for each detected systolic and diastolic interval across the
sequence of heartbeats. A summary interval energy is then
calculated representing median systolic and median diastolic
energies across a sequence of heart sounds.
[0044] The data provided by NMRE circuit 208 may be shown on
graphical display 226. Graphical display 232 may provide the NMRE
for a median systolic and a median diastolic interval as energy
profiles for the respective intervals.
[0045] Murmur detection circuit 214 detects murmurs in the heart
sounds according to the output data of the duration and phase
measurement circuit 212 and the data received from NMRE circuit
208. In an exemplary embodiment, murmur detection circuit 214
assesses the probability of the presence of a murmur in the heart
sounds using HMM matching and the received NMRE according to
Bayesian statistics. Murmurs may be further classified relative to
systolic/diastolic intervals and may be further labeled with
respect to early, mid, late, holo-systolic, holo-diastolic or
continuous. Graphical display 226 may be utilized to display the
detection results.
[0046] The output data of NMRE circuit 208 is applied to a clinical
guideline condition referral mapping circuit 216. In addition, any
input 228 from medical personnel, regarding dynamic auscultation
maneuvers, posture, or recording site may be applied to clinical
guideline mapping circuit 216.
[0047] Clinical guideline mapping circuit 216 includes systolic
characterization circuit 218 that characterizes the median systolic
interval based on output data from NMRE circuit 208. Systolic
characterization circuit 218 desirably models the median systolic
interval according to the energy profile of the median systolic
interval. Clinical guideline mapping circuit 216 may also include
diastolic characterization circuit 222 that characterizes the
median diastolic interval based on output data from NMRE circuit
208. Diastolic characterization circuit 222 desirably models the
median diastolic interval according to the energy profile of the
median diastolic interval.
[0048] Clinical guideline mapping circuit 216 further includes
probability circuit 220 that determines the probability that the
characterized median systolic interval data corresponds to the
first condition or the second condition.
[0049] Clinical guideline mapping circuit 216 may further include
probability circuit 224 that determines a probability that the
characterized median diastolic interval data output from diastolic
characterization circuit 222 corresponds to a diastolic murmur
condition or an absence of a diastolic murmur condition.
Probability circuit 224 may additionally receive the characterized
median systolic interval data from systolic characterization
circuit 218 and determine a probability that the characterized
median systolic and diastolic interval data corresponds to the
presence or absence of a continuous murmur condition.
[0050] Clinical guideline mapping circuit 216 may alternatively
include probability surface circuit 226 that further provides a
probability surface of a median systolic interval determined from
training data representing the first condition and the second
condition. In an exemplary embodiment, the probability surface is a
function of a range of normalized mid-range energies and a temporal
position within the median systolic interval. Probability surface
circuit 226 may also provide a probability surface of a median
diastolic interval and/or a median heart beat (systolic and
diastolic interval), determined from training data, representing
the presence or absence of a diastolic and/or continuous murmur
condition.
[0051] The data output by the clinical guideline mapping circuit
216, murmur detection circuit 214 and input from user 228 may,
alternatively, be provided to summary findings circuit 230. Summary
findings circuit 230 may determine a probability of murmurs
detected for a subject from heart sounds recorded over all
auscultation and/or postures associated with an auscultation
protocol. Although not illustrated, summary findings circuit may
use a combination of one or more of murmur detection results,
median systolic energy, median diastolic energy, a clinical
guideline referral probability for the median systolic interval, a
diastolic murmur probability, and a continuous murmur probability.
An output of summary findings circuit may be displayed on graphical
display 232.
[0052] The data output by the clinical guideline mapping circuit
216 may be displayed on graphical display 232. Graphical display
232 may show the determined median systolic interval probabilities
as a probability representative of the first condition, and/or a
probability representative of the second condition. Graphical
display 232 may also include the determined median diastolic
interval probabilities to provide a probability representative of
the presence or absence of a diastolic and/or a continuous murmur
condition. The determined probabilities may be shown with a range
variance indicative of a confidence measure. Alternatively, the
determined median systolic interval derived features may be shown
as a probability surface that is a function of systolic interval
duration and range of energies along with a focal position of NMRE
energy within the median systolic interval. Derived features from
median diastolic interval probabilities similarly may be shown on a
probability surface except that the probability surface is a
function of the diastolic interval durations and range of energies
for the median diastolic interval. It is contemplated that
continuous murmur probabilities may be similarly presented.
[0053] It may be desirable to determine a probability surface of
the median diastolic interval to provide a comparison with murmur
detection results from murmur detection circuit 214. For example,
murmur detection results may not detect the presence of diastolic
murmur energy whereas the probability surface may show that murmur
is present. Alternatively, murmur detection may over-determine the
presence of diastolic murmurs. Furthermore, a NRME may be computed
for any physiological signals where an energy profile may be used
according to clinical guideline indicators.
[0054] FIG. 3 shows a flowchart illustrating an exemplary method
for mapping heart sounds to clinical guideline referral indicators
according to the present invention. In step 300, heart sounds are
obtained, for example from PCG 202 (FIG. 2). In step 302, the heart
sounds are parsed for physiological events. Physiological events
may include for example, S1, S2, systolic intervals, diastolic
intervals, clicks, split S1, split S2, S3, S4 and ejection sounds.
The processing in step 302 may be performed, for example by
time-frequency circuit 204, feature extraction circuit 206,
sequence interpretation circuit 210 and duration and phase
measurement circuit 212 (FIG. 2).
[0055] In step 304, NMREs are calculated for a median systolic and
a median diastolic interval, for example by NMRE circuit 208 (FIG.
2). In step 306, the median systolic interval is characterized, for
example using characterization circuit 218 (FIG. 2). In step 308,
the heart sounds are processed to detect murmurs, for example using
murmur detection circuit 214 (FIG. 2). In step 310, the
characterized systolic interval is converted to clinical guideline
probabilities, for example using probability circuit 220 (FIG. 2).
It is understood that steps 308 and steps 310 may be sequentially
performed in either order or performed concurrently.
[0056] Although not shown, it is contemplated that a median
diastolic interval may be characterized and converted to diastolic
murmur probabilities. The median systolic and median diastolic
interval may be used similarly to determine continuous murmur
probabilities.
[0057] In step 312, the determined clinical guideline referral
probability is displayed. In step 314, the NMREs of the median
systolic interval and the median diastolic interval are displayed.
In step 316, murmur detection results are displayed. The presented
data of steps 312, 314 and 316 may be shown on graphical display
232 (FIG. 2). It is understood that steps 312, 314 and 316 may be
presented in any order including concurrently.
[0058] Alternate step 318 determines summary findings based on a
combination of one or more of murmur detection results, median
systolic energy, median diastolic energy, a clinical guideline
referral probability for the median systolic interval, a diastolic
murmur probability, and a continuous murmur probability over all
auscultation sites and/or postures representing an auscultation
protocol. Summary findings circuit 230 (FIG. 2) may, for example,
determine the summary findings. The referral probabilities may
additionally include diastolic and/or continuous murmur
probabilities, as described above. Alternate step 320 presents the
summary findings, for example, on a graphical display 232 (FIG.
2).
[0059] The NMREs are desirably presented as energy profiles of the
median systolic and diastolic intervals and may assist physicians
in making a referral of the patient for more detailed testing. For
example, ACC/AHA guidelines for echo referral include having the
physician determine if a murmur is present, and whether it is in
systole or diastole. If it is in systole, its loudness and profile
are analyzed. With auscultation alone, this is done entirely by
listening. The presentation of energy profiles provides a graphical
means for assertion of murmur presence, location, magnitude and
profile.
[0060] A physician desirably examines the presented probability
(step 312), NMREs of the median systolic and median diastolic
intervals (step 314) and murmur detection results (step 316) to
determine whether a referral decision may be warranted. In this
manner, physiological data are presented according to clinical
guideline referral conditions. If the referral probabilities, for
example, are borderline between the two conditions (referral and
absence of referral indication), the physician may still use murmur
detection results and NMRE results. These results may provide more
evidence to support or reject a referral indication. The physician
is thus presented with auscultatory features analyzed in multiple
ways, including according to clinical guidelines, with which to
provide assistance with making a referral decision.
[0061] FIG. 4 is a flowchart illustrating an exemplary method for
computing normalized energies (step 304 of FIG. 3) representative
of a median systolic interval and a median diastolic of a heart
signal according to the present invention. In step 400, the
resulting heart sound locations from duration and phase measurement
circuit 212 (FIG. 2) are parsed to find systolic interval and
diastolic interval timestamps from each detected heartbeat. The
NMREs as described herein are measured for all detected systolic
and diastolic intervals using the parsed timestamps.
[0062] In step 402, the systolic and diastolic intervals are
divided into multiple subintervals. Subdivision into a plurality of
subintervals is used to provide energy profiles of systole and
diastole. In an exemplary embodiment, the systolic and diastolic
intervals are each divided into thirty subintervals to provide
time-normalized windows for the systolic and diastolic intervals.
It is contemplated, however, that any number of subintervals that
may sufficiently represent the energy profile of systolic and
diastolic intervals may be of interest.
[0063] In step 404, a subinterval energy is calculated across the
sequence of heartbeats. Mid-range energy may be computed as
described in NMRE circuit 208 (FIG. 2) over each subinterval
duration. Each subinterval across the sequence of heartbeats may be
represented by an average value for that subinterval duration. The
average value may be computed by the mean, median, frequency, or
other methods over the duration of the interval. In an exemplary
embodiment, the average value is computed from the median. A median
systolic interval and a median diastolic interval are provided from
the respective averaged subintervals.
[0064] In step 406, a normalization factor is calculated. The
normalization factor may be the nominal mid-range energy over the
entire heart sound signal. The nominal mid-range energy may be
computed from mean energy, median energy, frequency or by other
means. In an exemplary embodiment, it is calculated from the median
energy and the nominal energy is computed from the same frequency
range of interest as for the mid-range energy.
[0065] In step 408, a NMRE is then computed for the median systolic
interval. The mid-range energy for the median systolic interval is
divided by the normalization factor determined in step 406. In step
410, a NMRE is computed for the median diastolic interval. The
mid-range energy for the median diastolic interval is divided by
the normalization factor determined in step 406. The resulting
NMREs for the median systolic and median diastolic intervals may
further be presented as a logarithmic ratio or a decibel ratio.
[0066] The resulting normalized energy may be converted to a murmur
grade based on a correlation between the normalized energy to a
standard auscultation murmur grade. For example, a study of a
population with heart murmurs, may be undertaken to record and
analyze the heart murmurs. The recordings may be further reviewed
by a trained cardiologist who may assign a standard murmur grade to
the study population. NMREs may then be correlated against the
cardiologist's grading of the study population to provide a
translation between the NMREs and the murmur grades. The heart
murmurs may be reviewed in terms of any of murmur duration,
magnitude and frequency spectrum. Psychoacoustics of the heart
signal may be taken into account during heart murmur review, such
as the murmur appearing to be fainter in the presence of another
loud sound.
[0067] After the NMREs are computed they may be presented, for
example using graphical display 232 of FIG. 2. NMREs are desirably
displayed graphically as bar graphs to illustrate the energy
profiles by processing the plurality of subintervals. FIG. 5
illustrates an example display of a normalized energy
representative of a median systolic interval when using the
exemplary method shown in FIG. 4. FIG. 5 shows energy profile 500
of a median systolic interval. Although not shown, a similar bar
graph desirably shows the energy profile of a median diastolic
interval. The bar graph may show the energy level by the y axis and
time along the x axis. In an exemplary embodiment, the y-axis shows
a decibel ratio representative of the NMRE. Alternatively, this
ratio may be further converted to a standard auscultation murmur
grade.
[0068] FIG. 6 is a flowchart illustrating an exemplary method for
characterizing a median systolic interval (step 306 of FIG. 3)
according to the present invention. In step 600, a maximum energy
(E) is determined for the median systolic interval from the
respective NMRE. In step 602, an energy-weighted time index for the
plurality of subintervals representing the median systolic interval
is computed based on the respective NMRE. In step 602, each time
index may be weighted by an amount of energy in that
subinterval.
[0069] In step 604, a murmur temporal center of energy intensity
(T) is determined for the median systolic interval based on the
energy-weighted time index. In step 606, a probability density
function (pdf) model of the median systolic interval is generated
using the computed (E,T). In an exemplary embodiment, weighted
Gaussian mixture models (GMM) are trained to fit a distribution of
the median systolic interval from the (E,T) information for the
first condition and the second condition. It is understood that the
median diastolic interval may be similarly characterized.
[0070] GMM is a known in the art method for determining membership
of data points in one of the model distributions. The pdf for the
first condition and second condition can be represented using a GMM
as:
P ( E , T C 1 ) = i = 1 M k 1 i N ( E , T .mu. 1 i , E 1 i ) and (
1 ) P ( E , T C 2 ) = i = 1 M k 2 i N ( E , T .mu. 2 i , E 2 i ) (
2 ) ##EQU00001##
where P(E,T|x) is the pdf, x represents the first condition (C1) or
second condition (C2), M is the number of components in the mixture
model, k.sub.xi is a mixture proportion of component i, and N( ) is
a probability distribution function parameterized by .mu., mean,
and .SIGMA., the covariance matrix for class x and component i. It
is contemplated that the pdf's for the first and second condition
may further be modeled according to other factors such as
auscultation site and/or posture.
[0071] FIG. 7 is a flowchart illustrating an exemplary method for
converting a characterized median systolic interval into clinical
guideline referral probabilities (step 310 in FIG. 3) according to
the present invention. In alternative step 700, the maximum NRME of
the median systolic interval is compared to a predetermined energy
threshold. In alternate step 702, a decision is made whether the
energy is greater than or equal to the predetermined threshold. If
the energy is less than the threshold, processing proceeds to
alternate step 704. If the energy is greater than or equal to the
threshold, processing proceeds to step 706.
[0072] If the energy is less than the predetermined threshold,
alternate step 702 proceeds to alternate step 704. In alternate
step 704, a predetermined probability may be retrieved to indicate
that the median systolic interval represents the second condition.
The predetermined probability may also be presented as indicating
that non-murmur energy was found. In this manner, processing
according to alternate steps 700, 702 and 704 may reduce a
computational processing cost if the NMRE is a small value, such as
being indicative of a non-murmur.
[0073] If the energy is greater than or equal to the threshold,
alternate step 702 proceeds to step 706. In step 706, a probability
is determined that the median systolic interval represents the
first condition. In step 708, a probability is determined that the
median systolic interval represents the second condition.
[0074] In alternate step 710, the determined probabilities are
presented including a range variance representative of a confidence
measure. The presented probabilities may be associated with the
focal point of energy (E,T) of the median systolic interval.
[0075] In alternate step 712, derived features from the determined
median systolic interval may be presented with respect to a
probability surface that is a function of a range of energies and a
duration of the median systolic interval.
[0076] To determine the probabilities, independent Gaussian
distributions, for example, may be generated separately for maximum
energy (E) and temporal center (T) from example data representing
the first condition and the second condition. In practice, the
median systolic interval generated for a heart sound signal may be
mapped to the individual (E) and (T) Gaussian distributions and use
the independent probabilities based on energy and timing that the
median systolic interval is associated with the first and second
conditions.
[0077] In an exemplary embodiment, the first and second condition
spaces are determined and optimized from training the GMM,
equations (1) and (2), using example data. In an exemplary
embodiment, the GMM include different centers and covariance
matrices. The example data is desirably a mixture of referable
murmurs, non-referable murmurs and non-murmurs. The example data is
desirably classified into the first condition and the second
condition by expert listeners. During training, the maximum energy
(E) and temporal center (T) of each example are provided as inputs
to the GMM. The GMM are desirably trained to match the distribution
of positive (first condition) and negative (second condition)
training examples. The GMM may then be used to approximate a
decision boundary between the first and second conditions.
[0078] In practice, the modeled heart sounds from step 606 of FIG.
6 may then be compared to the GMM representing the first condition
and the second condition. Probabilities that the heart signal is
associated with the first and second conditions may then be
determined. These probabilities may be determined for the focal
point of the energy (E,T). The probabilities may be presented with
a range variance.
[0079] In an alternative embodiment, the multiple training examples
may be used to derive mean and standard deviation by use provided
by a number of experts and merged into a distribution. In a further
alternative embodiment, the training data having energy and
temporal center (E,T) and received heart sounds may be provided to
a neural network to determine whether the median systolic interval
of the received heart sounds matches the first condition or the
second condition.
[0080] In alternative step 712, the probability that the focal
center of the median systolic interval is within the first
condition may be determined graphically. For example, the focal
center may be shown on the probability surface as a point and the
probability can be determined directly from the location of this
point on the probability surface. In an alternate embodiment, the
probability that the focal center is within the first condition may
be determined using a lookup table (LUT) having predetermined
measured values computed from the probability surface. The focal
center may be compared to the LUT values and the closest LUT value
may be presented.
[0081] FIG. 8 is a flowchart illustrating an exemplary method for
presenting clinical guideline probabilities relative to a
probability surface (step 712 of FIG. 7) according to an aspect of
the present invention. In step 800, a probability surface is
determined. The model of the received signal is compared against
the mixture of Gaussian models representing the first and second
conditions over a two-dimensional space representing the normalized
duration of the median systolic interval and a range of
energies.
[0082] Given the GMM of equations (1) and (2) representing the
first condition (C1) and the second condition (C2), a posteriori
probabilities may be determined from an observation
(E.sub.O,T.sub.O) according to the following equation:
P ( C 1 ( E O , T O ) = P ( E O , T O C 1 ) P ( C 1 ) P ( E O T O C
1 ) P ( C 1 ) + P ( E O , T O C 2 ) P ( C 2 ) . ( 3 )
##EQU00002##
After rearranging terms,
[0083] P ( C 1 ( E O , T O ) = 1 1 + P ( E O , T O C 2 ) P ( C 2 )
P ( E O T O C 1 ) P ( C 1 ) = 1 1 + exp [ - ( log P ( E O , T O C 1
) - log P ( E O , T O C 2 ) + log P ( C 1 ) - log P ( C 2 ) ) ] ( 3
a ) ##EQU00003##
The a posteriori probability can also be represented as shown in
equation (3b):
[0084] P(C1|E.sub.O,T.sub.O)=sigmoid[log P(E.sub.O,T.sub.O|C1)-log
P(E.sub.O,T.sub.O|C2)+log P(C1)-log P(C2)] (3b)
The probabilities P(C1) and P(C2) can be estimated, for example,
based on information about the prevalence of the first condition
and second condition murmurs. Probabilities P(C1) and P(C2) can
also be made adjustable to reflect characteristics of individual
patients, such as based on age, gender, patient groups or any
combination thereof. For example, the a priori probability of young
men having an innocent murmur (a second condition murmur) may be
lower as compared with the a priori probability for young women in
their 3.sup.rd trimester of pregnancy. In this example, the a
priori probability of an innocent murmur for the young men group
may be reduced relative to the young women group. As another
example, the a priori probability of older adults having a
pathological murmur (a first condition murmur) may be higher as
compared with the a priori probability for children. In this
further example, the a priori probability of the pathological
murmur for the older adults group may be increased relative to the
children group. The a posteriori probability may also be generated
according to other information such as auscultation site and/or
posture.
[0085] In step 802, the probability surface induced by the Gaussian
models is presented as a function of a range of energy and systolic
interval duration corresponding to the NMRE and median systolic
interval duration. In an exemplary embodiment, the probability
surface is presented as a contour map representing the probability
that the first condition (associated with a referral indication) is
present.
[0086] In step 804 a receiver operating characteristic (ROC) curve
can be derived from the first and second condition probabilities
for the training data. An operating point may be selected on the
ROC curve to determine a decision boundary between the first
condition and the second condition according to a desired
sensitivity and specificity. It is desirable to reduce false
positives without increasing false negative. Thus, in an exemplary
embodiment, the sensitivity and specificity are selected to be 95%
and 80%, respectively. The decision boundary represents the
referral probability threshold used to achieve the selected
performance. It is understood that these sensitivities and
specificities are not meant to be limiting. Any desired sensitivity
and specificity may be used to compute the threshold. The decision
boundary is desirably presented between the first and second
conditions on the probability surface
[0087] In step 806, the focal center of energy for the median
systolic interval (step 306 of FIG. 3) is presented as a point on
the probability surface. The position of this point on the
probability surface indicates the probability that the detected
anomaly corresponds to the first condition.
[0088] FIG. 9 illustrates an example display of clinical guideline
probabilities relative to probability surface 900. Probability
surface 900 is determined using the NMRE of FIG. 5 and the
exemplary method shown in FIG. 8. Probability surface 900 includes
a plurality of contours such as 902 representing the probability
that the median systolic interval represents the first condition (a
referral indication). A decision boundary 904 determined for a
sensitivity and a specificity illustrates a boundary between the
first condition, region 906 above boundary 904 and the second
condition, region 908 below boundary 904. Region 906 or 908 may be
highlighted to further distinguish the two conditions.
[0089] Probability surface 900 is desirably presented as a function
of median systolic interval duration and a range of energies
computed from the NMRE of the median systolic interval. Probability
surface further includes focal center of energy position 910
representing the focal center (E,T) in normalized mid-range energy
and time of the median systolic interval. In this manner, a
physician may readily determine the probability that the detected
anomaly corresponds to a referral indication based upon its
position relative to one of the contours 902.
[0090] Presentation of the focal center 910 on the probability
surface 900 may provide a confidence measure regarding the location
of focal center 910 within region 906. The physician may
immediately judge whether focal center 910 has a high probability
of referral, a borderline or low probability of referral. The
physician can judge the sensitivity of the probability measure to
small changes in energy or timing. For example, if the focal center
910 is on the leftmost edge of region 906, a small change in timing
may substantially change the probability. If the focal center 910
is in a plateau portion of the contours 902 and in region 906, the
probability may be relatively insensitive to timing changes in the
focal point 910.
[0091] FIG. 10 is an example display 1000 of exemplary system
graphical display 232 shown in FIG. 2 that employs an embodiment of
the present invention. Display 1000 desirably includes NMRE display
1002 for the median systolic interval and NMRE display 1004 for the
median diastolic interval. As described above, guidelines for
referral include any indication of a diastolic murmur. It is thus
desirable to include NMRE display 1004. Display 1000 also includes
probability surface map 1006 to provide clinical guideline referral
indication for systolic murmurs.
[0092] NMRE displays 1002 and 1004 may include information
associated with the respective median systolic and diastolic
intervals. Such information may include the interval duration, a
murmur onset time and a murmur duration time, as well as clinical
guideline information. NMRE displays 1002 and 1004 may additionally
illustrate the energy profile to highlight regions suspected to
contain murmurs by murmur detection circuit 214 (FIG. 2). For
example, regions suspected to contain murmurs may be colored
differently from the NMRE results (for example, from NMRE circuit
208 of FIG. 2).
[0093] An audio signal display 1008 desirably includes annotated
heart sounds such as S1 and S2. Murmur detection display 1010 may
include highlighting such as enclosing within a box regions of the
audio signal suspected to contain murmurs. A probability of murmurs
detected (not shown) may also be displayed.
[0094] Although not shown, it is contemplated that display 1000 may
include a summary findings indicator that summarizes the presence
of murmurs over an auscultation protocol. The summary findings
indicator may present results from summary findings circuit 230
(FIG. 2) that may use a combination of one or more of murmur
detection results, median systolic energy, median diastolic energy,
a clinical guideline referral probability for the median systolic
interval, a diastolic murmur probability, and a continuous murmur
probability.
[0095] A physician may use NMRE displays 1002 and 1004, audio
signal display 1008 with murmur detection display 1010 and
probability surface 1006 to make a referral decision. If, for
example, a continuous murmur is present and illustrated in audio
signal display 1008 but is illustrated in probability surface map
1006 as being in the non-referable region, the presented data may
provide the physician with additional information for making an
appropriate referral decision based on other parameters (e.g. EKG,
heart rate, audio signal display 1008).
[0096] Display 1000 may further include audio signal indicators
1012 to allow review of audio signal 1008. Indicators 1012 may
include a slower audio playback suitably processed to maintain the
same pitch/frequency content of the audio signal. Slower playback
may provide a more detailed listening analysis of a desired heart
sound. Indicators 1012 may also include a position indicator (not
shown) such as a vertical bar on audio signal display 1008
indicating the current audio playback location within the audio
signal. Indicators 1012 may further include playback volume
control.
[0097] Indicators 1012 may allow for the display and navigation
through portions of audio signal display 1008. Indicators 1012 may
include scroll bars for panning forward and backward relative to a
currently displayed portion. Indicators 1012 may also include
controls for expanding and contracting the portion of the signal
that is displayed.
[0098] Display 1000 may include patient information 1014 including
hemodynamic parameters computed by exemplary system 200.
Hemodynamic parameters may include a heart rate, an RR interval, a
systolic interval duration and a diastolic interval duration. One
or more of the hemodynamic parameters may be presented including a
mean and a variance. Patient information 1014 may also include
recording posture, patient identification such as an identification
number, age and/or gender as well as the date of recording.
[0099] Display 1000 may include recording site indicators 1016
which indicate the recording location relative to standard
auscultation locations, such as 2R, 2L, 4L and apex locations.
Recording site indicators 1016 may include links to data analyzed
for other recording sites for the patient. Selecting a link may
present a display similar to display 1000 except that the results
are processed for the selected recording site.
[0100] Display 1000 may include options 1018 for generating a
hardcopy printout of display 1000 or for closing the display.
[0101] Although the invention has been described as apparatus and a
method, it is contemplated that it may be practiced by a computer
configured to perform the method or by computer program
instructions embodied in a computer-readable carrier such as an
integrated circuit, a memory card, a magnetic or optical disk or an
audio-frequency, radio-frequency or optical carrier wave.
[0102] Although the invention is illustrated and described herein
with reference to specific embodiments, the invention is not
intended to be limited to the details shown. Rather, various
modifications may be made in the details within the scope and range
of equivalents of the claims and without departing from the
invention.
[0103] While preferred embodiments of the invention have been shown
and described herein, it will be understood that such embodiments
are provided by way of example only. Numerous variations, changes
and substitutions will occur to those skilled in the art without
departing from the spirit of the invention. Accordingly, it is
intended that the appended claims cover all such variations as fall
within the spirit and scope of the invention.
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