Transcript SEGMENTATION ON PHONOCARDIOGRAM

SEGMENTATION ON
PHONOCARDIOGRAM
Professor Kuh
EE645 FINAL PROJECTS
By Rebecca Longstreth
DEFINITIONS
• Phonocardiogram
– Graph representing sounds made by the human heart
• Cardio cycle
– Represented by S1, S2, S3 and S4
• S1
– Orignates at the closure of the atrioventricular valves
– recordable between 91hz and 179 hz
• S2
– Originates at the aortic an pulmonic valves
– Can reach 200 hz
• S3 and S4
– Represent cardiac wall vibrations
– Not all audible, low density
DEFINITIONS
• S1 and S2
– Sound goes left to right
• S3 and S4
– Right to left
Reason for Segmentation
• Tool to assist doctors in diagnosing heart
malfunctions accurately
• Prevents mistakes in diagnoses
• Improved quality care for patients
• Doctors experience benefits everyone
Choices of Segmentation
• Fourier transform
• Laplace transform
• Wavelet decomposition
Fourier transform
• Reasons not used:
– Phonocardiograms are not smooth wave
signals
– Not linear time invariance
– Non-stationary
– Non-harmonic
Laplace transform
• Reasons not used:
– Initial condition does not exist
Wavelet decomposition
• Preferred method of analyzing
phonocardiograms
• Time limited and frequency limited
• Utilize digital filters and down sampling
Wavelet decomposition conditions
• Normalize the amplitude of all artifacts
before transformation to avoid
amplification errors
• Short segmentation window for high
frequency
• Long segmentation window for low
frequency
• Short lengths of data are considered due
to file size considerations
Phonocardiogram anomalies
• Fetal acoustic signals inconsistent from
one sound to the next
• Wave shape can change
• Frequency could shift
• Amplitude, duration and position in the
cardiac cycle can change
• Background noise of the mother and the
shielding affect of the womb
GOAL
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Isolate one cycle for analysis
Identify S1 and S2
Boundaries of S1 and S2
Systolic and diastolic periods
– Systolic: time interval from beginning of S1 to
the beginning of S2
– Diastolic: time interval from beginning of S2 to
the beginning of S1
• Associate frequency spectrum
Research
• According to Cardiologists interviewed
– There is no way to recognize S1 and S2 using
only a phonocardiogram
– Location of the probe can radically affect the
signal strengths of S1 and S2 on the same
individual
Finding S1 and S2: Method 1
• Compare EKG and the phonocardiogram
simultaneously to ensure accurate
designation of S1 and S2
• S1 occurs shortly after the EKG peak
– The first PCG signal following the EKG peak
is S1
• S2 occurs shortly after the 2nd EKG peak
Finding S1 and S2: Method 1
• Disadvantage
– EKG requires expensive bulky and fragile
equipment to perform
• Need
– Robust, portable, easy to use low cost
equipment
System for heart
sound
classification
Wavelet Decomposition
• Heart sounds (sampled at an 8kHz sample
rate, 16 bits/sample) are first hand
segmented into 4096 sample segments,
each consisting of a single heartbeat
cycle.
Feature Reduction & Denoising
• Figure 1. A Simple Heart Sound Classification System
• .Each segment is transformed using a 7 level wavelet
decomposition, based on a Coifman 4th order wavelet
kernel (relative symmetry and fast execution).
• The resulting transform vectors, 4096 values in length,
are reduced to 256 element feature vectors by
discarding the 4 levels with shortest scale.
• Neural network in the classifier reduces noise. The
magnitudes of the remaining coefficients in each vector
are calculated, then normalized by the vector’s energy.
Classification
Each feature vector is classified using a
three layer neural network (256 input
nodes, 50 hidden nodes, and 5 output
nodes).
Results And Discussion
• RESULTS AND DISCUSSION
• The system was evaluated using heart sounds
corresponding to
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normal
mitral valve prolapse(MVP)
coarctation of the aorta (CA),
ventricular septal defect (VSD),
and pul-monary stenosis (PS).
• The classifier was trained using 10 shifted
versions (over a range of 100 samples) of a
single heartbeat cycle from each type.
Figure 2. Representative Heart Sounds (left to right) Without Added Noise, with Noise Variance 1000,
and with Noise Variance 3000, x-axis is the number of sample segments
Figure 2. Representative Heart Sounds (left to right) Without Added Noise, with Noise
Variance 1000,
and with Noise Variance 3000,x-axis is the number of sample segments
Figure 3. Feature Vectors Corresponding to the Heart
Sounds in Figure 2, x-axis is the number of feature vectors
Figure 3. Feature Vectors Corresponding to the Heart
Sounds in Figure 2, x-axis is the number of feature vectors
Figure 3. Feature Vectors Corresponding to the
Heart Sounds in Figure 2,x-axis is the number of
feature vectors
Figure 3. Feature Vectors Corresponding to the
Heart Sounds in Figure 2,x-axis is the number of
feature vectors
Feature vectors with additive noise
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The feature vectors produced for these examples in Figure 3.
key features remain relatively stable even with addiditive noise.
Figure 4. Classification Accuracy (in Percent) as a
Function of the Variance of the Added Noise
Figure 5. Classification Accuracy as a Function of
Signal-to-Noise Ratio (in dB)
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the sounds differ widely (e.g., by a factor of approximately 16:1
comparing a typical normal heartbeat
with one exhibiting VSD). Accounting for this variation, classification
accuracy as a function of signal-tonoise ratio (SNR) is shown in Figure 5. For an SNR above 31dB (which
is easily obtainable under
most practical circumstances) classification accuracy is 100%.
• REFERENCES
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Barschdorff, D., U. Femmer, and E. Trowitzsch (1995, Sept. 10-13). Automatic phonocardiogram
signal analysis in infants based on wavelet transforms and artificial neural networks. In Computers
in Cardiology 1995, pp. 753–756. IEEE, Vienna, Austria.
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http://www.ida.liu.se/~rtslab/publications/2001/reed01a-eurosim.pdf
Barschdorff, D., U. Femmer, and E. Trowitzsch (1995, Sept. 10-13). Automatic
phonocardiogram signal analysis in infants based on wavelet transforms and artificial
neural networks. In Computersin Cardiology 1995, pp. 753–756. IEEE, Vienna,
Austria.
Donnerstein, R. L. and V. S. Thomsen (1994, September). Hemodynamic and
anatomic factors affecting the frequency content of Still’s innocent murmur. The
American Journal of Cardiology 74, 508–510.
Durand, L.-G. and P. Pibarot (1995). Digital signal processing of the
phonocardiogram: review of the most recent advancements. Critical Reviews in
Biomedical Engineering 23(3/4), 163–219.
El-Asir, B., L. Khadra, A.H. Al-Abbasi, and M.M.J. Mohammed (1996, Oct. 13-16).
Multireso-lution analysis of heart sounds. In Proc. of the Third IEEE Int’l Conf. on
Elec., Circ., and Sys., Volume 2, pp. 1084–1087. Rodos, Greece.
Rajan, S., R. Doraiswami, R. Stevenson, and R. Watrous (1998, Oct. 6-9). Wavelet
based bank
of correlators approach for phonocardiogram signal classification. In Proc. of the
IEEE-SP Int’l Symp. on Time-Frequency and Time-Scale Analysis, pp. 77–80.
Pittsburgh, PA.
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• REFERENCES
• Shino, H., H. Yoshida, K. Yana, K. Harada, J. Sudoh, and E.
Harasawa (1996, Oct. 31 - Nov. 3). Detection and classification of
systolic murmur for phonocardiogram screening. In Proc. of the
• 18th Int’l Conf. of the IEEE Eng. in Med. and Biol. Soc., Volume 1,
pp. 123–124. Amsterdam, The Netherlands.
• http://www.cinc.org/Program/p7b-1.htm
• THE ANALYSIS OF HEART SOUNDS FOR SYMPTOM
DETECTION AND MACHINE-AIDED DIAGNOSIS, Todd R. Reed,
Nancy E. Reed and Peter Fritzson, The Netherlands
• H. Liang, S. Lukkarinen, and I Hartimo, Heart Sound Segmentation
Algorithm Based on Heart Sound Envelogram, Helsinki, Finland
• H. Liang, S. Lukkarinen, and I Hartimo, A Boundary Modification
Mehtod for Sound Segmentation Algorithm, Helsinki, Finland
• Abdelhani Djebbari, and Fethi Bereski Reguig, Short-time Fourier
Transform Analysis of the Phonocardiogram Signal, Algiers