U.S. patent application number 12/308755 was filed with the patent office on 2010-06-10 for multi parametric classification of cardiovascular sounds.
Invention is credited to Claus Graff, Samuel Schmidt, Johannes Struijk.
Application Number | 20100145210 12/308755 |
Document ID | / |
Family ID | 37680714 |
Filed Date | 2010-06-10 |
United States Patent
Application |
20100145210 |
Kind Code |
A1 |
Graff; Claus ; et
al. |
June 10, 2010 |
Multi parametric classification of cardiovascular sounds
Abstract
The present application relates to a method for classifying a
cardiovascular sound recorded from a living subject. The method
comprises the step of extracting at least two signal parameters
(309) from said cardiovascular sound, said at least two signal
parameters characterizes at least two different properties of at
least a part of said cardiovascular sound. The method further
comprises the step of classifying said cardiovascular sound using
said at least two signal parameters in a multivariate
classification method (310). Furthermore, the application relates
to a system, stethoscope and server for classifying a
cardiovascular sound recorded from a living subject, where the
above-described method has been implemented.
Inventors: |
Graff; Claus; (Klarup,
DK) ; Schmidt; Samuel; (Aalborg, DK) ;
Struijk; Johannes; (Terndrup, DK) |
Correspondence
Address: |
JACOBSON HOLMAN PLLC
400 SEVENTH STREET N.W., SUITE 600
WASHINGTON
DC
20004
US
|
Family ID: |
37680714 |
Appl. No.: |
12/308755 |
Filed: |
June 26, 2006 |
PCT Filed: |
June 26, 2006 |
PCT NO: |
PCT/DK2006/000374 |
371 Date: |
October 5, 2009 |
Current U.S.
Class: |
600/528 |
Current CPC
Class: |
A61B 7/04 20130101; A61B
5/7267 20130101; A61B 5/7264 20130101; A61B 5/0002 20130101; A61B
5/02007 20130101; A61B 5/7239 20130101 |
Class at
Publication: |
600/528 |
International
Class: |
A61B 5/02 20060101
A61B005/02 |
Claims
1. A method for classifying a cardiovascular sound recorded from a
living subject, said method comprises the steps of: extracting at
least two signal parameters from said cardiovascular sound, said at
least two signal parameters characterizes at least two different
properties of at least a part of said cardiovascular sound,
classifying said cardiovascular sound using said at least two
signal parameters in a multivariate classification method.
2. A method according to claim 1 characterized in that at least one
of said at least two signal parameters is a frequency parameter
describing a property in the frequency domain of at least a part of
said cardiovascular sound.
3. A method according to claim 1 characterized in that at least one
of said at least two signal parameters describing a property in the
time domain of at least a part of said cardiovascular sound.
4. A method according to claim 2 characterized in that at least one
of said frequency parameters is a frequency level parameter
describing a frequency level property of at least a part of said
cardiovascular sound.
5. A method according to claim 2 characterized in that at least one
of said at least two signal parameters is a frequency bandwidth
parameter describing a frequency bandwidth property of at least a
part of said cardiovascular sound.
6. A method according to claim 4 characterized in that at least one
of said frequency level properties characterizes the most powerful
frequency component of at least a part of said cardiovascular
sound.
7. A method according to claim 5 characterized in that at least one
of said frequency bandwidth properties characterizes the bandwidth
of the most powerful frequency component of at least a part of said
cardiovascular sound.
8. A method according to claim 3 characterized in that at least one
of said time parameters is a property characterizing the mobility
of at least a part of said cardiovascular sound.
9. A method according to claim 1 characterized in that said method
further comprises the step of dividing said cardiovascular sound
into at least one sub-segment and at least one of said signal
parameters is extracted from said at least one sub-segment.
10. A method according to claim 1 characterized in that said method
further comprises the step of modelling at least a part of said
cardiovascular sound and at least one of said signal parameters is
extracted from said model.
11. A method according to claim 1 characterized in that said
multivariate classification method is a discriminant function.
12. A system for classifying a cardiovascular sound recorded from a
living subject, said system comprises: processing means for
extracting at least two signal parameters from said cardiovascular
sound, said at least two signal parameters characterizes at least
two different properties of at least a part of said cardiovascular
sound, processing means for classifying said cardiovascular sound
using said at least two signal parameters using a multivariate
classification method.
13. A system according to claim 12 characterized in that said
processing means for extracting at least two signal parameters from
said cardiovascular sound is adapted to extract at least one
frequency parameter describing a property in the frequency domain
of at least a part of said cardiovascular sound.
14. A system according to claim 12 characterized in that said
processing means for extracting at least two signal parameters from
said cardiovascular sound is adapted to extract at least one time
parameter describing a property in the time domain of at least a
part of said cardiovascular sound.
15. A system according to claim 13 characterized in that said
processing means adapted to extract at least one of said frequency
parameters are further adapted to extract at least one frequency
level parameter describing a frequency level property of at least a
part of said cardiovascular sound.
16. A system according to claim 13 characterized in that said
processing means adapted to extract at least one frequency
parameter are further adapted to extract at least one frequency
bandwidth parameter describing a frequency bandwidth property of at
least a part of said cardiovascular sound.
17. A system according to claim 13 characterized in that said
processing means adapted to extract at least one frequency level
property are further adapted to extract the most powerful frequency
component of at least a part of said cardiovascular sound.
18. A system according to claim 13 characterized in that said
processing means adapted to extract at least one of said frequency
bandwidth properties are further adapted to extract the bandwidth
of the most powerful frequency component of at least a part of said
cardiovascular sound.
19. A system according to claim 14 characterized in that said
processing means for extracting at least one time parameters are
further adapted to extract the mobility of at least a part of said
cardiovascular sound.
20. A system according to claim 12 characterized in that said
system further comprises processing means for dividing said
cardiovascular sound into at least one sub-segment and at least one
of said signal parameters is extracted from said at least one
sub-segment.
21. A system according to claim 12 characterized in that said
system further comprises processing means for modelling at least a
part of said cardiovascular sound and in that said processing means
for extracting at least two signal parameters from said
cardiovascular sound are further adapted to extract at least one of
said parameters from said model.
22. A system according to claim 12 characterized in that said
multivariate classification method used by said processing means
for classification of said cardiovascular sound is a discriminant
function.
23. A computer-readable medium having stored therein instructions
for causing a processing unit to execute a method according to
claim 1.
24. A stethoscope comprising: recording means adapted to record a
cardiovascular sound from a living subject, storing means adapted
to store said recorded cardiovascular sound, a computer-readable
medium and a processing unit, said computer-readable medium having
stored therein instructions for causing said processing unit to
execute a method according to claim 1 and thereby classify said
recorded cardiovascular sound.
25. A server device connected to a communication network
comprising: receiving means adapted to receive a cardiovascular
sound recorded from a living subject through said communication
network, storing means adapted to store said received
cardiovascular sound, a computer-readable medium and a processing
unit, said computer-readable medium having stored therein
instructions for causing said processing unit to execute a method
according to claim 1 and thereby classify said received
cardiovascular sound.
26. A server device according to claim 25 characterized in that
said receiving means are further adapted to receive said
cardiovascular sound from a client connected to said communication
network.
27. A server device according to claim 25 characterized in that
said server device further comprises means for sending said
classification of said cardiovascular sound to at least one client
unit connected to said communication network.
Description
FIELD OF THE INVENTION
[0001] The present invention relates to methods and systems for
classification of heart sounds recorded from a living subject into
classes describing whether or not murmurs due to coronary artery
stenosis is present in the heart sound.
BACKGROUND OF THE INVENTION
[0002] Coronary artery disease is the single most common cause of
death from cardiovascular disease in the western world. The heart
muscle receives its blood supply through the coronary arteries, and
atherosclerosis is the most common pathophysiologic process
occurring in the coronary arteries giving rise to coronary artery
disease (CAD). Atherosclerosis is a process that builds up plaques
within the artery, and the blood flow can therefore be is reduced
or even blocked by the plaque. The constantly working heart
requires a continuous and efficient blood supply in order to work
properly. Defects in the blood supply may be very severe and even
fatal. Increasing degrees of luminal diameter reduction or stenosis
of the coronary artery will first limit reserve flow, then reduce
flow at rest and may finally totally occlude the vessel.
[0003] There is a need for measuring/detecting coronary artery
stenosis for clinicians and other medical professionals to diagnose
CAD. Once a diagnose has been made a cure/treatment could be
started.
[0004] Today several non-invasive techniques for
measuring/detecting the severity of a stenosis or its presence
inside a coronary artery exist. This can be done by magnetic
resonance imaging (MRI), in vivo intravascular ultrasound (IVUS) or
optical coherence tomography (OCT). However, the above-mentioned
techniques are all rather complicated and expensive to use and
therefore only patients with specific symptoms are offered such
examinations. The consequence is that most patients have a critical
stenosis when examined.
[0005] Clinicians and other medical professionals have long relied
on auscultatory sounds to aid in the detection and diagnosis of
physiological conditions. For instance, a clinician may utilize a
stethoscope to monitor and record heart sounds in order to detect
heart valve diseases. Furthermore, the recorded heart sounds could
be digitized, saved and stored as data files for later analysis.
Devices have been developed that apply algorithms to electronically
recorded auscultatory sounds. One example is an automated
blood-pressure monitoring device. Other examples include analysis
systems that attempt to automatically detect physiological
conditions based on the analysis of auscultatory sounds. For
instance, artificial neural networks have been discussed as one
possible mechanism for analyzing auscultatory sounds and providing
an automated diagnosis or suggested diagnosis. Using these
conventional techniques, it is difficult to provide an automated
device for diagnosis of coronary stenosis using auscultatory
sounds. Moreover, it is often difficult to implement the
conventional techniques in a manner that may be applied in
real-time or pseudo real-time to aid the clinician.
OBJECT AND SUMMARY OF THE INVENTION
[0006] The object of the present invention is to solve the
above-mentioned problems.
[0007] This is achieved by a method for classifying a
cardiovascular sound recorded from a living subject. The method
comprises the step of extracting at least two signal parameters
from said cardiovascular sound, said at least two signal parameters
characterize at least two different properties of at least a part
of said cardiovascular sound. The method further comprises the step
of classifying said cardiovascular sound using said at least two
signal parameters in a multivariate classification method.
[0008] Hereby a simple method for classifying cardiovascular sounds
is achieved and the method is furthermore very robust since
different properties of the cardiovascular sound is taken into
account and used in a multivariate classification method. The
cardiovascular sound related to turbulence consists of at least two
components: a broad band component caused by turbulent blood flow
colliding with the arterial wall and a narrow banded component
related to the resonance frequency of the artery wall, therefore
different variables describing different properties are needed in
order to perform a robust classification. The different properties
describe different characteristics of the cardiovascular sound and
would therefore be uncorrelated and therefore provide different
information of the cardiovascular sound. Different properties could
for instance be the time duration of the diastolic segment of
cardiovascular sound, the time duration of the systolic
cardiovascular sound, the most dominant frequency component of the
sound, the bandwidth of different frequency components, the energy
in two frequency bands, the mobility of part of the signal, the
complexity of the signal, the power ratio between different parts
of the signal, e.g. two different segments or two different
frequency bands, morphological characteristics such as correlation
ratios between different segments or amplitude change over time.
The method could easily be implemented in any kind of data
processor unit and therefore be e.g. integrated in a software
program which clinicians and doctors could use in order to classify
the cardiovascular sound. Furthermore, the method could be
integrated in a digital stethoscope and the stethoscope could
therefore be used in order to classify a patient's cardiovascular
sound. Since doctors and other clinicians are familiar with a
stethoscope, they could easily be taught to use the stethoscope to
classify the cardiovascular sound. The result is that the
classification could assist the doctor or other clinicians to
diagnose whether or not the patient suffers from CAD.
[0009] In another embodiment of the method, at least one of said at
least two signal parameters is a frequency parameter describing a
property in the frequency domain of at least a part of said
cardiovascular sound. Hereby the frequency components of the
cardiovascular sound could be used as a parameter in the
multivariable classification method. Frequency parameters are very
good parameters for classifying whether or not murmurs due to
stenosis are present in a cardiovascular sound because the stenosis
would change the frequency components of the cardiovascular
sound.
[0010] In another embodiment of the method, at least one of said at
least two signal parameters describes a property in the time domain
of at least a part of said cardiovascular sound. Hereby time
properties of the cardiovascular sound could be used as a parameter
in the multivariable classification method. Time properties like
the mobility or number of turning points are good indicators,
whether or not murmurs due to stenosis are present in
cardiovascular sound. Furthermore, by using both time and frequency
parameters a very robust classification of the cardiovascular is
achieved since time and frequency properties are often
uncorrelated.
[0011] In another embodiment of the method, at least one of said
frequency is parameters is a frequency level parameter describing a
frequency level property of at least a part of said cardiovascular
sound. Hereby it is achieved that a frequency level property of the
cardiovascular sound is used in the multivariable classification
method. The murmurs would typically change the frequency level of
the cardiovascular sound, and by using parameters describing the
frequency level of the sound a robust classification of the
cardiovascular sound could be achieved.
[0012] In another embodiment of the method, at least one of said at
least two signal parameters is a frequency bandwidth parameter
describing a frequency bandwidth property of at least a part of
said cardiovascular sound. Hereby the bandwidth of, for instance,
dominating frequency components could be used in the multivariable
classification method. The advantage of using a frequency bandwidth
property of the cardiovascular sound is that murmurs often has a
limited frequency bandwidth, and the frequency bandwidth parameter
would therefore be a good indicator of whether or not murmurs due
to stenosis are present in the cardiovascular sound.
[0013] In another embodiment of the method, at least one of said
frequency level properties characterizes the most powerful
frequency component of at least a part of said cardiovascular
sound. This parameter is a very useful parameter as the murmurs due
to stenosis typically have a dominating frequency component between
200-800 Hz. And if the most powerful frequency component is inside
this interval, it would be a good indication of the presence of
murmurs due to stenosis.
[0014] In another embodiment of the method, at least one of said
frequency bandwidth properties characterizes the bandwidth of the
most powerful frequency component of at least a part of said
cardiovascular sound. Hereby the bandwidth of the most powerful
frequency component could be used in the multivariable
classification method. This bandwidth would most likely depend on
whether or not murmurs due to stenosis are present in the
cardiovascular sound.
[0015] In another embodiment of the method, at least one of said
time parameters is a property characterizing the mobility of at
least a part of said cardiovascular sound. The mobility is a good
indicator of whether or not murmurs due to stenosis are present in
the cardiovascular sound. The mobility describes the variance of
the sound, and since murmurs would cause larger variance in the
sound the mobility would be a good indicator.
[0016] In another embodiment of the method, the method further
comprises the step of dividing said cardiovascular sound into at
least one sub-segment and at least one of said signal parameters is
extracted from said at least one sub-segment. Hereby it is achieved
that the cardiovascular sound could be divided into sub-segments,
e.g. into a systolic part and a diastolic part. Thereby relevant
sub-segments could be used to extract the above-described different
parameters.
[0017] In another embodiment of the method, the method further
comprises the step of modelling at least a part of said
cardiovascular sound and at least one of said signal parameters is
extracted from said model. Hereby time models and frequency models
of the cardiovascular sound or sub-segments of the sound could e.g.
be used to extract the above-described parameters. The advantage of
using models is that the models could enhance the signal
properties, e.g. by using an envelope function or an autoregressive
model. Furthermore, models would simplify and optimize the
calculation process when the method is implemented in a data
processor.
[0018] In another embodiment of the method, the multivariate
classification method is a discriminant function. Hereby a simple
and fast implementation of the classification method is achieved.
Furthermore, any number of parameters could be used in the
discriminant function, and the different parameters could also be
weighted differently depending on the parameters' significance. The
discriminant function could also be trained using cardiovascular
test sounds is recorded from patients suffering from stenosis and
healthy patients. Thereby the weights of the different parameters
could be optimized to experimental data.
[0019] The invention further relates to a system for classifying a
cardiovascular sound recorded from a living subject, said system
comprises processing means for extracting at least two signal
parameters from said cardiovascular sound, said at least two signal
parameters characterizes at least two different properties of at
least a part of said cardiovascular sound; processing means for
classifying said cardiovascular sound using said at least two
signal parameters using a multivariate classification method.
Hereby a system for classifying a cardiovascular sound can be
constructed and hereby the same advantages as described above are
achieved.
[0020] In a further embodiment of the system, said processing means
for extracting at least two signal parameters from said
cardiovascular sound is adapted to extract at least one frequency
parameter describing a property in the frequency domain of at least
a part of said cardiovascular sound. Hereby the same advantages as
described above are achieved.
[0021] In a further embodiment of the system, said processing means
for extracting at least two signal parameters from said
cardiovascular sound is adapted to extract at least one time
parameter describing a property in the time domain of at least a
part of said cardiovascular sound. Hereby the same advantages as
described above are achieved.
[0022] In a further embodiment of the system, said processing means
adapted to extract at least one of said frequency parameters are
further adapted to extract at least one frequency level parameter
describing a frequency level property of at least a part of said
cardiovascular sound. Hereby the same advantages as described above
are achieved.
[0023] In a further embodiment of the system, said processing means
adapted to extract at least one frequency parameter is further
adapted to extract at least one frequency bandwidth parameter
describing a frequency bandwidth property of at least a part of
said cardiovascular sound. Hereby the same advantages as described
above are achieved.
[0024] In a further embodiment of the system, said processing means
adapted to extract at least one frequency level property is further
adapted to extract the most powerful frequency component of at
least a part of said cardiovascular sound. Hereby the same
advantages as described above are achieved.
[0025] In a further embodiment of the system, said processing means
adapted to extract at least one of said frequency bandwidth
properties are further adapted to extract the bandwidth of the most
powerful frequency component of at least a part of said
cardiovascular sound. Hereby the same advantages as described above
are achieved.
[0026] In a further embodiment of the system, said processing means
for extracting at least one time parameter are further adapted to
extract the mobility of at least a part of said cardiovascular
sound. Hereby the same advantages as described above are
achieved.
[0027] In a further embodiment of the system, said system further
comprises processing means for dividing said cardiovascular sound
into at least one sub-segment and at least one of said signal
parameters is extracted from said at least one sub-segment. Hereby
the same advantages as described above are achieved.
[0028] In a further embodiment of the system, said system further
comprises processing means for modelling at least a part of said
cardiovascular sound and in that said processing means for
extracting at least two signal parameters from said cardiovascular
sound are further adapted to extract at least one of said
parameters from said model. Hereby the same advantages as described
above are achieved.
[0029] In a further embodiment of the system, said multivariate
classification method used by said processing means for
classification of said cardiovascular sound is a discriminant
function. Hereby the same advantages as described above are
achieved.
[0030] The invention further relates to a computer-readable medium
having stored therein instructions for causing a processing unit to
execute a method as described above. Hereby the same advantages as
described above are achieved.
[0031] The invention further relates to a stethoscope comprising
recording means adapted to record a cardiovascular sound from a
living subject, storing means adapted to store said recorded
cardiovascular sound, a computer-readable medium and a processing
unit, said computer-readable medium having stored therein
instructions for causing said processing unit to execute a method
according to claims 1-12 and thereby classify said recorded
cardiovascular sound. Hereby the method according to the present
invention can be implemented in a stethoscope and the
above-described advantages are achieved.
[0032] The invention further relates to a server device connected
to a communication network comprising receiving means adapted to
receive a cardiovascular sound recorded form a living subject
through said communication network, storing means adapted to store
said received cardiovascular sound, a computer-readable medium and
a processing unit, said computer-readable medium having stored
therein instructions for causing said processing unit to execute a
method as described above and thereby classify said received
cardiovascular sound. Hereby the method according to the present
invention can be implemented in a server connected to a
communication network. The server could then perform the
above-described method and the above-described advantages are
achieved.
[0033] In another embodiment of the server, said receiving means
are further adapted to receive said cardiovascular sound from a
client connected to said communication network. Hereby a
clinician/doctor could send a cardiovascular sound to the server
using a client device such as a laptop. The server could thereafter
classify the received cardiovascular sound. The above-described
advantages are hereby achieved.
[0034] In another embodiment of the server, the server device
further comprises means for sending said classification of said
cardiovascular sound to at least one client unit connected to said
communication network. Hereby the result of the classification can
be sent back to a client, and the clinician/doctor can therefore
receive the result of the classification. The above-described
advantages are hereby achieved.
BRIEF DESCRIPTION OF THE DRAWINGS
[0035] FIG. 1 illustrates a graph of a typical heart sound,
[0036] FIG. 2 illustrates a fluid dynamic model of an arterial
stenosis,
[0037] FIG. 3 illustrates an overview in form of a flow diagram of
the method according to the present invention,
[0038] FIG. 4 illustrates an embodiment of the system according to
the present invention,
[0039] FIG. 5 illustrates another embodiment of the method
according to the present invention,
[0040] FIG. 6 illustrates a flow diagram of the segmentation
method,
[0041] FIG. 7 illustrates for a heart sound the relationship
between the envelope autocorrelation of a cardiac cycle and the
cardiac cycle,
[0042] FIG. 8 illustrates the implementation of a Bayesian network
used to calculate the probability of a sound being an S1, S2 and
noise sound.
DESCRIPTION OF EMBODIMENTS
[0043] FIG. 1 illustrates a graph of a typical heart sound recorded
by a stethoscope and shows the amplitude (A) of the sound pressure
at the y-axis and time (t) at the x-axis. The heart sounds reflect
events in the cardiac cycle: the deceleration of blood, turbulence
of the blood flow and the closing of valves. The closing of the
valves is typically represented by two different heart sounds, the
first (S1) and the second (S2) heart sound. The first and second
heart sounds are illustrated in the figure, and S1 marks the
beginning of systole which is the part of the cardiac cycle in
which the heart muscle contracts, forcing the blood into the main
blood vessels, and the end of the diastole which is the part of the
heart cycle during which the heart muscle relaxes and expands.
During diastole, blood fills the heart chambers. The duration of
systolic segments is nearly constant around 300 ms for healthy
subjects. Given a pulse of 60 beats per minute the duration of a
cardiac cycle will be one second on average, and the duration of
the diastole will be 700 ms. However, the diastolic durations are
not constant, but will vary depending on the subject's pulse. In
addition, smaller variations of the diastolic duration are
introduced due to neural regulation and the effects of
respiration.
[0044] FIG. 2 illustrates a fluid dynamic model of an arterial
stenosis and shows an artery (201) with a stenotic lesion (202).
The arrows (203) indicate the blood flow through the artery.
Vortices (204) will occur when high velocity blood exits a stenotic
lesion (202). These vortices collide with the arterial wall (205)
and are transformed into pressure vibrations that cause the
arteries to vibrate at their resonance frequencies. The result is
that soundwaves in the form of murmers (206) with a frequency
corresponding to the aterial wall's resonance frequencies are
created and emitted from the arterial wall. Resonance frequencies
in the arterial segment are increased if a stenosis is present and
their frequencies depend on the diameter of the stenotic segment
compared to the diameter of the artery. As the severity of a
stenosis increases, so does the resonance frequency. The resonance
frequency of a partial occluded stenotic artery is most likely
between 200 Hz to 1100 Hz. The intensity of the vortice
fluctuations depends on the blood flow so that murmurs from the
left coronary arteries are most intense during diastole, when the
blood flow through these arteries is highest. Murmurs from the
right coronary arteries are most intense during diastole if there
is a stenosis in branches of the right coronary artery supplying
the right-sided cavities, whereas the murmur more likely will be
systolic from those branches of the right coronary artery giving
arterial blood to the left ventricle. The intensity of murmurs not
only depends on the blood flow, but also on the frequency content
of a murmur. High murmur frequencies are more suppressed by the
chest wall compared to low frequencies. The murmurs caused by the
arterial vibrations would affect the graph of a heart sound
recorded by e.g. a stethoscope.
[0045] FIG. 3 illustrates an overview in form of a flow diagram of
the method according to the present invention. The method could for
instance be implemented as a software program running on a computer
or on a microcontroller implemented in a stethoscope. In short, the
method starts with an initialization (301), receiving a test signal
(302), dividing the test signal into relevant segments (306),
filtering the relevant segments (307); calculating/developing a
model of the signal (308) in relevant segments; extracting
different parameters from the signal and the model (309),
performing an analysis of the signal (310) using the extracted
parameters and classifying the relevant segments into two groups:
one indicating that the signal contains murmurs due to stenosis
(311), and one indicating that the signal does not contain murmurs
due to stenosis (312).
[0046] After the method has been initialized (301) the method
receives the test signal (302) as a data file (303). The test
signal would be the heart sound from a person (304) recorded and
digitalized into a data file, e.g. by a digital stethoscope (305).
The test signal would be similar to the heart sound illustrated in
FIG. 1, however, the duration of the test signal would typically be
5-15 times longer than the signal shown in FIG. 1. Once the test
signal has been received (302), segmentation (306) is performed in
order to detect and divide the test signal into segments. The
segmentation process would typically detect the heart sounds S1 and
S2 and thereafter divide the test signal into systolic and
diastolic parts. Hereafter the test signal is filtered (307), and
the filtration process includes an autoregressive filter that
reduces white noise in the signal and a band pass filter that only
lets frequencies between 450-1100 Hz pass. The test signal would
thereafter contain the frequencies caused by the vibrations of the
arterial wall when stenosis is present in the artery. The
autoregressive filter could be implemented as a Kalman filter that
is a powerful estimator of past, present and future states and it
can do so even when the precise nature of the modelled system is
unknown. This is a desirable feature in the present application
when reducing the effects of noise since the exact composition of a
murmur is unknown. A first order Kalman filter can reduce the
effects of white noise and smooth the noisy heart sound recordings
for further processing. The band pass filter could be implemented
as a wavelet filter. In another embodiment the Kalman filter is
omitted in order to simplify the implementation of the method in
e.g. a microprocessor and further to reduce the number of
calculations performed by the microprocessor.
[0047] When the signal has been filtered (307), relevant segments
are selected for further analysis. In one embodiment a part of the
diastolic segment is selected for further analysis as the murmur
due to stenosis is most likely to be audible in the diastolic
segment.
[0048] A mathematical model of the signal in the selected segment
is hereafter calculated/developed (308) using the sampled heart
sound in the data file. The model is used to extract parameters
that characterize the sound in the segment and could be used to
categorize whether or not the murmurs due to stenosis exist in the
sound segment. In the present embodiment an autoregressive all-pole
parametric estimation (AR-model) is used to model the signal. In
the AR-model the sampled sound signal, y, from the data file is
modelled as a linear combination of M past values of the signal and
the present input, u, driving the sound generating process. The
model can be described by the following equation:
y ( n ) = - p = 1 M a p y ( n - p ) + u ( n ) [ 3.1 ]
##EQU00001##
[0049] where M represents the model order, A.sub.p the AR
coefficients and n the sample number. The AR coefficients are
determined through an autocorrelation and by minimizing the error
associated with the model.
[0050] The AR model in this embodiment is used to extract frequency
parameters describing the heart sound. A second order model M=2 is
preferred because it makes a better separation between the
frequency parameters extracted from a heart sound with murmurs
present and the frequency parameters extracted from a heart sound
with murmurs present.
[0051] Thereafter different parameters are extracted (309) from the
sampled signal and the AR model using signal processing techniques.
Some parameters could be extracted from the selected segments. Each
parameter characterize the heart sound in the selected segments and
could therefore be used to categorize the heart sound, e.g. whether
or not murmurs due to stenosis are present in the heart sound. The
parameters can in this embodiment be the number of turnings points
per signal length, TP; the mobility of the signal, MB; pole
magnitude, PM; normalized AR-peak frequency, NF; and AR spectral
ratio, SR.
[0052] The number of turning points TP is extracted from the
sampled signal in the time domain, and it is found by calculating
the number of turns the signal performs in the time domain per unit
time. This could be done by determining the amount of local maxima
in a time period. Thus:
T P = number of turns signal length [ 3.2 ] ##EQU00002##
[0053] The mobility MB is extracted from the sampled signal in the
time domain and found by calculating the variance, .sigma..sub.y,
of the signal in the time domain and the variance of the signal's
first derivative, .sigma..sub.y'. The mobility is hereafter found
by:
M B = .sigma. y ' 2 .sigma. y 2 = .sigma. y ' .sigma. y [ 3.3 ]
##EQU00003##
[0054] The pole magnitude PM is found by transforming the AR-model
into the z-domain and calculating the magnitude of the poles in
z-domain described by the AR-spectrum.
[0055] The normalized AR peak frequency NF is based on the
assumption that murmurs due to stenosis are more likely to be found
in the diastolic segment than in the systolic segment. The NF is
found by calculating the angle of the poles in the AR-spectrum in
the z-plane and transforming this into a frequency of both a
diastolic segment and a systolic segment. If the absolute
difference between the two is less than 25 Hz, which is typical in
cases where no murmurs due to stenosis are present, then 25 Hz is
subtracted from the diastolic peak frequency. If the average
diastolic frequency is more than 50 Hz greater than the average
systolic peak frequency, which is typical when murmurs due to
stenosis are present, then 25 Hz is added to the average peak
diastolic frequency.
[0056] The AR spectral ratio SR is found by calculating the ratio
of the energy in the frequency rang 200-500 Hz to the energy in the
frequency range 500-1000 Hz of a diastolic segment.
[0057] The extracted parameters are thereafter used in a
multiparametric discriminant function in order to classify whether
or not the sound segment contains murmurs due to stenosis (310). In
this embodiment a linear discriminant function is used to classify
the sound segments. The linear discriminant function combines
weighted features into a discriminant score g(x) and could be
described by:
g(x)=w.sub.1x.sub.1+w.sub.2x.sub.2+w.sub.3x.sub.3+ . . .
+w.sub.kx.sub.k+w.sub.i0=w.sup.Tx+w.sub.0 [3.4]
where x is the feature vector consisting of the extracted
parameters, k represents the number of features, i represents the
classes and w is a weight vector that holds the discriminant
coefficients. In the case where only two classes must be separated,
a single discriminant function is used. A two class classifier is
called a dichotomizer. A dichotomizer normally classifies the
feature vectors with the decisions boarder g(x)=0 (due to the
constant w.sub.0). If the discriminant score g(x) is greater than
zero the segment is assigned to class 1, otherwise it is assigned
to class 2. Since g is a linear function g(x)=0 it defines a
hyperplane decision surface, dividing the multi dimensional space
into two half sub spaces. The discriminant score g(x) is the
algebraic distance to the hyper-plane. The discriminant function
needs to be trained in order to find the weights values, w, and
make a safe and robust classification of the sound segments. The
discriminant training procedure needs to be performed before using
the system, and the purpose of the procedure is to find the optimal
weights values of w so that the hyper plane separates the feature
vectors optimally. The training procedure is in one embodiment
carried out by using 18 test sounds recorded from 18 test persons
where nine test persons have coronary stenosis and nine test
persons do not have coronary stenosis. The discriminant training
procedure is performed by using the statistical software program
SPSS v.12.0 for windows (SPSS inc., Chicago Ill., USA). The
above-mentioned parameters are extracted from the 18 training
sounds and used as statistical inputs to the software program. The
resulting discriminant could be:
g(x)=164.709MB-0.061NF-78.027PM+27,188SR+91.878TP+33,712 [3.5]
where MB is the mobility of the signal, NF the AR-peak frequency,
PM the pole magnitude, SR the AR spectral ratio and TP the number
of turning point.
[0058] If the result of the discriminant function is larger than
zero (g(x)>0) then the sound segment does not contain murmurs
due to stenosis (312). On the other hand, if the discriminant
function is smaller than zero (g(x)<0) then the sound segment
contains murmurs due to stenosis (311).
[0059] The discriminant function could by a person skilled in the
art easily be adjusted to include additional or fewer parameters in
order to develop a proper discriminant function that can be used to
classify the heart sound. Further parameters could for instance
be:
[0060] The Complexity, CP, of the sampled signal in the time
domain. This parameter is based on the ratio of the mobility of the
first derivative of the signal to the mobility of the signal itself
where y'' is the second derivative of the filtered heart sound
signal. The complexity measure is relatively sensitive to noisy
signals since it is based on the second derivative.
C P = MB y ' MB y = .sigma. y '' / .sigma. y ' .sigma. y ' /
.sigma. y [ 3.6 ] ##EQU00004##
[0061] Further, the AR-peak frequency (PF) could be extracted and
used in the discriminant function. The AR-peak frequency could be
found by calculating the angle of the AR poles in the z-plane.
[0062] The parameters used in the discriminant function could be
extracted from different segments of the heart sound, e.g. a number
of different diastolic segments where a number of parameters is
extracted from each diastolic segment. Thereafter an average value
of each parameter could be calculated and used as input in the
discriminant function.
[0063] FIG. 4a illustrates an embodiment of the system according to
the present invention where a server (401) is programmed to execute
the method described in FIG. 3. Furthermore, the server is
connected to a network (402), e.g. the Internet and adapted to on
request to receive and analyze heart sound. Clinicians or other
medical professionals would record the heart sound from a patient
by a digital stetoscope (305) and thereafter transmit the
digitalized heart sound to a personal computer (403). The clinician
can hereafter send a request to the server in order to have the
heart sound analyzed. Once the server has analyzed the heart sound
the result is automatically sent back to the clinician. FIG. 4b
illustrates a flow diagram of the process and the communication
between the personal computer (403) and the server. The left hand
side represents the client side (410) and the right hand side
represents the server side (411). First the client sends a heart
sound in digital form to the server (412). Thereafter the server
performs the method illustrated in FIG. 3 and sends (413) the
result of the analysis back to the client where it is displayed
(414) to the clinician. The clinician could hereafter evaluate the
result in order to choose the right treatment of the patient.
[0064] The system according to the present invention could also be
implemented as an all in one digital stethoscope. The stethoscope
would therefore automatically perform the analysis described in
FIG. 3 when a heart sound has been recorded. This means that the
method described in FIG. 3 needs to be implemented in stethoscopes'
processing means, and the result of the analysis could e.g. be
displayed on a small LCD integrated in the stethoscope. An
advantage of this embodiment is that most clinicians are familiar
with a digital stethoscope and could therefore easily learn to use
the stethoscope to diagnose whether or not the patient has a
coronary stenosis.
[0065] FIG. 5 illustrates another embodiment of the method
described in FIG. 3. When the signal has been filtered (307),
relevant segments are selected for further analysis. In one
embodiment a part of the diastolic segment is selected for further
analysis as the murmur due to stenosis is most likely to be audible
in the diastolic segment. In this embodiment the diastolic segment
comprising respiration sounds is discarded (501). This is done by
calculating the energy level of the diastolic segment in the
frequency band 200-440 Hz and comparing this energy level with the
median energy level of the entire diastolic segment. The diastolic
segment would be discarded if the energy level of the 200-440 Hz
frequency band is a factor 1.1 larger than the energy level in the
entire diastolic segment.
[0066] The remaining diastolic segments are hereafter divided into
sub-segments (502) with a duration of 37.5 ms or 300 samples. This
is done because the blood flow in the coronary artery is not
constant during a diastole, and the murmurs due to stenosis would
therefore not be constant.
[0067] The variance of the signal in all sub-segments is then
calculated and the sub-segments with a variance larger than 1.3 of
the median variance of all sub-segments are then discarded (503).
Hereby sub-segments comprising high noise spikes are removed.
[0068] Thereafter (504) none stationary sub-segments are removed.
This is done by dividing the sub-segment into sub-sub-segments with
a duration of 3.75 ms or 30 samples and then calculate the variance
of each sub-sub-segments. Thereby an outline of the variance
throughout the sub-segment is constructed. The variance of the
outline is then calculated and the sub-segment is removed if the
variance of the outline is larger than 1.
[0069] At this point a number of sub-segments have been discarded
in order to remove noisy and none stationary sub-segments. This
would typically result in 30-50 sub-segments from a cardiovascular
recording of approximately 10 seconds.
[0070] The remaining sub-segments are thereafter used in step (308)
and (309) as described in FIG. 3 in order to extract parameters
describing different properties of the cardiovascular signal.
Thereafter the median of each parameter is calculated using the
values of the parameter form each sub-segment (505). The median of
each parameter is thereafter used in the multiparametric
discriminant function as described in FIG. 3. In this embodiment
the following parameters are used: the mobility, the power-ratio
and the pole-amplitude of a 3 pole in an AR model of order 6.
[0071] FIG. 6 illustrates a flow diagram of the segmentation method
(306) according to the present invention used to automatic divide a
heart sound (601) into sub-segments. The heart sound (601) has been
recorded by a stethoscope and the signal has been digitized in
order to digitally process the signal. The graph shows the
amplitude (A) of the sound intensity as a function of time (t). The
heart sounds reflect events in the cardiac cycle; the deceleration
of blood, turbulence of the blood flow and the closing of valves.
The closing of the valves is typically represented by two different
heart sounds, the first (S1) and the second (S2) heart sound. The
first and second heart sounds are illustrated in the figure, and
(S1) marks the beginning of systole, which is the part of cardiac
cycle in which the heart muscle contracts, forcing the blood into
the main blood vessels, and the end of the diastole which is the
part of the heart cycle during which the heart muscle relaxes and
expands. During diastole, blood fills the heart chambers.
[0072] The purpose of the segmentation method is to classify the
recorded heart sound into systolic, diastolic and noise segments.
The illustrated method includes steps of noise reduction (602)
followed by envelope creation (603). The noise reduction could be
implemented as a high-pass filer followed by removal of high
amplitude friction noise spikes due to external noise like movement
of the stethoscope during recording and thereafter a low pass
filter. The purpose of the envelope creation is to enhance the
trend of the signal. The envelope is in this embodiment created by
calculating the Shannon energy of the signal:
se(n)=x(n).sup.2log x(n).sup.2
where x is the signal and se is the Shannon energy. The high
amplitude components in the signal are weighted higher than low
amplitude components when calculating the Shannon energy. The
envelope (613) of the heart sound (601) calculated by using the
Shannon energy is shown in figure (613), and it can be seen that
the heart sounds S1 and S2 are enhanced.
[0073] In order to classify the detected sounds into systolic
segments, diastolic segments and noise components based on interval
durations on either side of the heart sounds S1 and S2, it is
necessary to know how long the intervals between S1's and S2's are.
Therefore, the durations of the heart cycles (systolic and
diastolic intervals) are extracted from an autocorrelation of the
envelope (604). This process is described in detail in FIG. 7.
[0074] Candidates S1's and S2's are then detected (605) using the
time intervals extracted above and a threshold (614) on the
envelope (613). To reduce the number of detected noise spikes, a
minimum requirement is applied to the candidate segments, which
effectively removes some of the erroneously detected noise spikes.
In some recordings there is a big difference between the intensity
of S1 and S2 sounds. This causes a problem since some of the low
intensity sounds may be missed by the threshold. As a result the
segmentation method performs a test for missing S1 and S2 sounds
(606). If it can be determined that some segments are missing, the
threshold procedure is rerun (607) using lower local
thresholds.
[0075] Once the signal has been divided into segments as described
above interval parameters and frequency parameters for each segment
are then extracted (608). The parameters aid in the classification
of the sounds into systolic segments and diastolic segments.
[0076] The interval parameters are four Boolean parameters
extracted for each sound by comparing the time duration to the
previous sound and to the next sound with the time intervals
extracted using the autocorrelation. The parameters are: [0077]
AfterDia: Is true if the sound is succeeded by a second sound after
a period corresponding to the duration of a diastole, [0078]
AfterSys: Is true if the sound is succeeded by a second sound after
a period corresponding to the duration of a systole, [0079]
BeforeDia: Is true if the sound follows a second sound after a
period corresponding to the duration of a diastole, [0080]
BeforeSys: Is true if the sound follows a second sound after a
period corresponding to the duration of a systole.
[0081] The frequency parameter divides the sounds into low
frequency and high frequency sounds by calculating the median
frequency of the sound. This is useful information as the first
heart sound is expected to be a low frequency sound and the second
heart sound is expected to be a high frequency sound.
[0082] The parameters are parsed into a Bayesian network where the
probability of a segment being a S1, S2 and noise sound is computed
(609). The figure illustrates a bar chart (615) of the probability
calculated for each sound in the heart signal (601). Each sound
would typically have one dominating probability indicating the type
(S1, S2 or noise) of the sound. Thereby all sounds are classified
into S1, S2 and noise sounds. However, the probability of the three
types would in some cases be more or less equal and in such cases
it is not possible to classify the sound into a S1, S2 or noise
sound using the Bayesian network.
[0083] The probabilities are used in the last step (610) to divide
and verify the heart signal into systole and diastole segments.
This is done by using the position of the identified S1 and S2
sounds to mark the beginning of a systolic and diastolic sound
segment respectively
[0084] The final result of the method (611) is the beginnings and
ends of all identified systoles and diastoles. Therefore a "train"
(616) of alternating systoles (617) and diastoles (618) can be
created. Once the systoles and diastoles have been identified they
can be used in further data handling, e.g. to extract further
parameters from these segments and thereafter use the parameters to
classify the medical condition of the recorded heart sound.
[0085] FIG. 7 illustrates the relationship between the envelope
autocorrelation and the cardiac cycle, and how the intervals
between heart sounds S1 and S2 can be found from the
autocorrelation.
[0086] FIG. 7a illustrates the envelope autocorrelation with the
normalized autocorrelation at the y-axis (NA) and the displacement
(m) of the shifted envelope at the x-axis.
[0087] FIG. 7b illustrates the displacement (m1) when the shifted
envelope (701) is displaced by the duration of the systole
corresponding to the unshifted envelope (702). The y-axis shows the
amplitude (A) of the envelope and the x-axis the time (t). The S1's
in the displaced envelope are multiplied by the S2's in the
unshifted envelopes resulting in the first peak (703) seen in the
autocorrelation.
[0088] FIG. 7c illustrates the displacement (m2) when the shifted
envelope (701) is displaced by the duration of the diastole
corresponding to the unshifted envelope (702). The displaced S2's
are multiplied by the S1's in the unshifted envelope resulting in
the second peak (704) seen in the autocorrelation.
[0089] FIG. 6b illustrates the displacement (m3) when the shifted
envelope (701) is displaced by the duration of the cardiac cycle
corresponding to the unshifted envelope (702). The S1's in the
displaced envelope are multiplied by the S1's in the unshifted
envelope, and the S2's in the displaced envelope are multiplied by
the S2's in the unshifted envelope. When this occurs the dominating
peak (705) in the autocorrelation is produced.
[0090] The interval between the heart sounds could therefore be
found by measuring the distance between the peaks in the
autocorrelation as described above.
[0091] FIG. 8 illustrates the implementation of the Bayesian
network used to calculate the probability of a sound of being an
S1, S2 and noise sound in step (809). The basic concept in the
Bayesian network is the conditional probability and the posterior
probability. The conditional probability describes the probability
of the event a given the event b.
P(a|b)=x.sub.c [8.1]
[0092] If the above equation describes the initial conditional
probability, the posterior probability would be:
P(b|a)=x.sub.p [8.2]
[0093] According to Bayes' rule the relation between the posterior
probability and the conditional probability is:
P ( b a ) = P ( a b ) P ( b ) P ( a ) [ 8.3 ] ##EQU00005##
where P(a) is the prior probability for the event a, and P(b) is
the prior probability for the event b. Equation [8.3] only
describes the relation between one parent and one child, but since
the event a can be the combination of several events {a.sub.1,
a.sub.2 , , , a.sub.n} the equation can be expanded to:
P ( b a 1 , a 2 , , , , , a n ) = P ( a 1 , a 2 , , , , , a n b ) P
( b ) P ( a 1 , a 2 , , , , , a n ) [ 8.4 ] ##EQU00006##
[0094] Since the goal is to find the probability for the different
states of b when a.sub.1 and a.sub.2 are known, P(a.sub.1, a.sub.2
, , , a.sub.n) is just a normalizing constant k and [7.4] can be
simplified to:
P(b|a.sub.1,a.sub.2 , , , a.sub.n)=kP(a.sub.1,a.sub.2 , , ,
a.sub.n|b)P(b) [8.5]
[0095] If child events (a.sub.1, a.sub.2 . . . a.sub.n) are
conditionally independent, equation [8.5] can be generalized
to:
P ( b a 1 , a 2 , , , , , a n ) = k P ( b ) i = 1 N P ( i b ) [ 8.6
] ##EQU00007##
where N is the number of known events a. Equation [8.6] is useful
in determining the probability of the event b if the states of all
a events are known and if all a events are conditionally
independent. A Bayesian network based on equation [8.6] is called a
naive Bayesian network because it requires conditional independency
of the children.
[0096] The task for the Bayesian network is to evaluate the type of
each detected sound above the detection threshold. For each of
these sounds, the posterior probability of being an S1 sound, an S2
sound or a noise component is calculated and the Bayesian network
is constructed using one parent and five children. The parent is a
sound above the envelope threshold (801), and the children are the
five parameters described above: Frequency (802), AfterSys (803),
AfterDia (804), BeforeSys (805) and BeforeDia (806). When
determining the posterior probability for the type of a particular
sound, the prior probability for the different states of a sound
type P(S) and the conditional probabilities must be known, i.e. the
conditional probabilities that "AfterSys" is in a given state when
S is a given type, P(AfterSys|S). This posterior probability
requires definition of P(S), P(AfterSys|S), P(AfterDia|S),
P(BeforeSys|S), P(BeforeDia|S) and P(Frequency|S) before the
equation [8.6] can be used to calculate the posterior probability
of a sound being a particular type of sound.
[0097] The prior probability that a sound is an S1, S2 or a noise
component changes between recordings. In the optimal recording,
where no noise components are detected, the prior probability for
noise is zero, P(S.sub.=Noise)=0. If this is the case and an equal
number of S1's and S2's are detected, the prior probability that
the detected sound is an S1 is 50%, and similar for S2. Therefore,
P(S.sub.=S1)=P(S.sub.=S2)=0.5 if P(S.sub.=noise)=0. However, this
optimal condition cannot be assumed for real signals, and noise
sounds would be detected. This will increase the prior probability
that a given sound is noise.
[0098] The exact probability of a detected sound being noise,
P(S.sub.=noise) can be defined if the number of detected noise
sounds, N.sub.noise and the total number of detected sounds,
N.sub.sounds are known. For instance, if it is known that four
noise sounds are detected, N.sub.noise=4, and the total number of
detected sounds is 20, the probability that the sound being
examined is a noise sound is P(S.sub.=Noise)=4/20. However, in most
signals N.sub.noise is unknown and an estimate of N.sub.noise is
therefore necessary, and this estimate can be based on already
available information since the duration of a heart cycle is known
from the envelope autocorrelation (804). The expected number of
cardiac cycles in one recording can therefore be calculated by
dividing the length of the recording with the length of the cardiac
cycles. The number of S1's and S2's in a recording is therefore
twice the number of cardiac cycles in a recording. The prior
probability of the sound type would therefore be:
P ( S = noise ) = N noise N sound [ 8.7 ] ##EQU00008##
and the prior probability that the detected sound is an S1 or
S2:
P ( S = s 1 ) = P ( S = s 2 ) = 1 -= P ( S = noise ) 2 [ 8.8 ]
##EQU00009##
[0099] The conditional probability that an S1 is followed by an S2
sound after an interval corresponding to the duration of a systole,
P(AfterSys|S.sub.=S1), depends on several factors. The S1 sounds
will normally be followed by S2 sounds after an interval of
duration equal to the systole. Deviations from this can also occur,
e.g. when S1 is the last sound in the recording, or if S2 is
missing because it is not detected by the threshold. It may also
occur that a weak (below threshold) S2 is detected because noise
occurs in the tolerance window associated with those sounds. The
probability that "AfterSys" is false if the sound is an S1 sound
may thus be calculated as
P(AfterSys.sub.=false|S.sub.=S1)=P(EndSound.orgate.Singlesound),NoiseInW-
in) [8.9]
where "EndSound" is an event describing that the sound is the last
sound in the recording. "SingleSound" describes that S1 is not
followed by S2 as the next S2 sound is not detected due to
sub-threshold amplitude. "NoiselnWin" describes noise occurrence in
the window, where the S2 sound was expected. The conditional
probability that "AfterSys" is true given that the examined sound
is an S1 sound is given by:
P(AfterSys.sub.=true|S.sub.=S1)=1-P(AfterSys.sub.=false|S.sub.=S1)
[8.10]
[0100] If the examined sound is an S2 sound it is not likely that
any sound occurs after an interval corresponding to the systolic
duration since the next S1 sound will occur after the duration of
the diastole. An exception is if a noise sound occurs in the window
P(NoiseInWin) or if the systole and diastole durations are equal.
If the duration of the diastole is equal to the duration of the
systole, the S1 sound which follows the S2 sound after the duration
of a diastole occurs in both the systole tolerance window and in
the diastole tolerance window. This will happen if the heart rate
of the subject is high. The probability that a sound occurs in both
tolerance windows (overlap) is equal to the degree of the overlap
between the systole and diastole tolerance window. This probability
is termed P(Overlap). Therefore, the conditional probability that a
sound occurs in the window after systole duration if the examined
sound is an S2 sound is:
P(AfterSys.sub.=true|S.sub.=S2)=P(Overlap.orgate.NoiseInWin)
[8.11]
[0101] The conditional probability that a sound does not occur
after a systole duration, if the examined sound is an S2, is the
opposite of the conditional probability that it does occur:
P(AfterSys.sub.=false|S.sub.=S2)=1-P(AfterSys.sub.=true|S.sub.=S2)
[8.12]
[0102] The conditional probability that a detected noise sound is
followed by another sound after the systole duration is based on
the probability that a sound of any kind is present in a segment
with the length of the used tolerance window. This can be estimated
from the ratio of the tolerance window length multiplied by the
number of detected sounds minus one to recording length.
P(SoundInWin|S.sub.=S2)=1-P(AfterSys.sub.=true|S.sub.=S2)
[8.12]
[0103] The conditional probability that a detected noise sound is
followed by another sound after the systole duration,
P(AfterSys|S.sub.=noise), is based on the probability that a sound
of any kind is present in a segment with the length of the used
tolerance window. This can be estimated from the ratio of the
tolerance window length multiplied by the number of detected sounds
minus one to recording length. The conditional probability that a
noise sound is followed by another sound after a systole duration
is therefore:
P ( AfterSys = true S = noise ) = P ( SoundInWin ) = ( N sound - 1
) 2 Sys tol RecLength [ 8.13 ] ##EQU00010##
[0104] where N.sub.sound is the number of sounds within the
recording, Sys.sub.tot is the duration of a systole and RecLength
is the length of the recording. The conditional probability that a
noise is not followed by another sound after the systole interval
is the opposite:
P(AfterSys.sub.=false|S.sub.=noise)=1-P(SoundInWin) [8.14]
[0105] The conditional probabilities for P(AfterDia|S),
P(BeforeSys|S) and P(BeforeDia|S) are based on the same assumptions
used to define P(AfterSys|S). These conditional probabilities can
be found in the tables below:
TABLE-US-00001 False True P(AfterSys|S) S1 P((EndSound .orgate.
SingleSound), NoiseInWin) 1 - P((EndSound .orgate. SingleSound),
NoiseInWin) S2 1 - P(Overlap .orgate. NoiseInWin) P(Overlap
.orgate. NoiseInWin) Noise 1 - P(SoundInWin) P(SoundInWin)
P(AfterDia|S) S1 1 - P(Overlap .orgate. NoiseInWin) P(Overlap
.orgate. NoiseInWin) S2 P((EndSound .orgate. SingleSound),
NoiseInWin 1 - P((EndSound .orgate. SingleSound), NoiseInWin) Noise
1 - P(SoundInWin) P(SoundInWin) P(AfterSys|S) S1 1 - P(Overlap
.orgate. NoiseInWin) P(Overlap .orgate. NoiseInWin) S2 P((EndSound
.orgate. SingleSound), NoiseInWin 1 - P((EndSound .orgate.
SingleSound), NoiseInWin) Noise 1 - P(SoundInWin) P(SoundInWin)
P(AfterDia|S) S1 P((EndSound .orgate. SingleSound), NoiseInWin) 1 -
P((EndSound .orgate. SingleSound), NoiseInWin) S2 1 - P(Overlap
.orgate. NoiseInWin) P(Overlap .orgate. NoiseInWin) Noise 1 -
P(SoundInWin) P(SoundInWin)
[0106] It has previously been found that the frequency parameter
classified 86% of the S1 sounds as low frequent and 80% of the S2
sounds as high frequent. 85% of all noise sounds were classified as
high frequent. This information was used as the conditional
probabilities between the frequency parameter P(Frequency|S):
TABLE-US-00002 P(Frequency|S) Low High S1 0.86 0.14 S2 0.20 0.80
Noise 0.15 0.85
[0107] When all conditional probabilities are found, equation [8.6]
is used by the Bayesian network to calculate the posterior
probabilities for all detected sounds. This way, three
probabilities are calculated for each sound that reflect how likely
it is that the current sound is a given type.
[0108] It should be noted that the above-mentioned embodiments
rather illustrate than limit the invention, and that those skilled
in the art will be able to suggest many alternative embodiments
without departing from the scope of the appended claims.
* * * * *