 Open Access
 Total Downloads : 142
 Authors : G. Venkata Hari Prasad, Dr. P. Rajesh Kumar
 Paper ID : IJERTV4IS041236
 Volume & Issue : Volume 04, Issue 04 (April 2015)
 DOI : http://dx.doi.org/10.17577/IJERTV4IS041236
 Published (First Online): 30042015
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
Analysis of Various DWT Methods for Feature Extracted PCG Signals

Venkata Hari Prasad1,

Research scholar, Andhra University, Visakhapatnam
Dr. P. Rajesh Kumar2
Andhra University College of Engineering, Visakhapatnam
Abstract: One of the main obstacles states that the widespread use of phonocardiogram (PCG) in modern days medicine is the various noise components they invariably contain. Although many advances have been made towards automated heart sound segmentation and heart pathology detection and classification, an efficient method for noise handling would come as a major aid for further development in this field, especially when it comes to working with PCGs collected in realistic environments such as hospitals and clinics. The feature extraction has been gone through 10 levels on PCG recorded signals using transformation techniques. Analyzing PCG signals with calculating parameters Energy, Standard deviation, Variance, Mean square error (MSE), Peak Signal to Noise Ratio (PSNR), Root Mean Square Error (RMSE) and Maximum Entropy (ME) values of human heart signal which were extracted from Phonocardiogram were calculated. These calculations are based on the filtration process. Wavelets considered as filtration technique as well as under goes 10 leveling factors. Different wavelets compared for analysis part such as Haar, Daubechies, Orthogonal, Coiflets and Biorthogonal and also finding histograms and denoising the signal were part in this proposed scheme using wave menu analysis.
Keywords: ECGElectrocardiogram , PCGPhonocardiogram, AS Aortic Stenosis, AR Aortic Regurgitation , MS Mitral Stenosis, MR Mitral Regurgitation , DWT Discrete Wavelet Transform, ENEREnergy ,STDStandard Deviation, VAR Variance, NHS Normal Heart Sound

INTRODUCTION
The heart is a hollow muscular organ that pumps blood throughout the blood vessels to various parts of the body by repeated, rhythmic contractions. It is found in all animals with a circulatory system, which includes the vertebrates and heart is divided into chambers namely atrium and ventricles. The upper two chambers are known as atria while the lower two chambers are known as ventricles. Heart muscles squeeze the blood from chamber to chamber. During this squeezing process, the valves help the blood to keep flowing smoothly in and out of the heart. This is done by automatically opening of valves to let blood from chamber to chamber and closing to prevent the backflow of blood [1]. Heart sounds are the composite sounds produced by myocardial systolic and diastolic, hoist valve, blood flow and cardiovascular vibration impact, and contain a great deal of physiological and pathological information regarding human heart and vascular.
The average human heart, beating at 72 beats per minute, will beat approximately 2.5 billion times during an average 66 year lifespan, and pumps approximately 4.75.7 litres of blood per minute. It weighs approximately 250 to 300 grams (9 to 11 oz) in females and 300 to 350 grams (11 to 12 oz) in males.
Research on diagnosis of cardiac abnormalities using wavelet techniques has been carried out from the past few years, due to its good performance in analyzing the signals that present non stationary characteristics, this technique has eventually become a powerful alternative when compared to the traditional Fourier Transform (FT) [1] [2]. Fig.(1) shows the normal heart sounds, composed of four different sounds, namely S1, S2, S3 and S4.The pumping action of a normal heart is audible by the 1st heart sound (S1) and 2nd heart
Figure (1): Heart Sounds for S1and S2 Signals.
Sound (S2). During systole, the AV valves are closed and blood tries to flow back to the atrium, causing back bulging of the AV valves. But the taut chordatetendineae (cordlike tendons that connect the papillary muscles to the tricuspid valve and the mitral valve in the heart) stop the back bulging and causes the blood to flow forward. This leads to vibration of the valves, blood and the walls of the ventricles which is presented as the 1st heart sound. During diastole, blood in the blood vessels tries to flow back to the ventricles causing the semi lunar valves to bulge. But the elastic recoil of the arteries cause the blood to bounce forward which vibrates the blood, the walls and the ventricular valves which is presented as the 2nd heart sound . The 3rd heart sound (S3) is heard in the mid diastole due to the blood that fills the ventricles. The 4th heart sound (S4), also known as atrial heart sound, occurs when the atrium contracts and pumps blood to the ventricles. S4 appears with a low energy and is almost never heard by the stethoscope [3].

Methodology

Input Heart signal Acquisition:
Input heart signals for investigation are downloaded from standard biomedical website, these signals are converted into (.wav) format. Phonocardiogram is a graphic method of recording noises during his heart activity. Heart and vessels sounds are composed by audible and inaudible oscillations, but recordable. If a microphone specially designed to detect lowfrequency sound is placed on the chest, the heart sounds can be amplified and recorded by a highspeed recording apparatus.
Figure 2. Recording A is an example of normal heart sounds, showing the vibrations of the first, second and third heart sounds and even the very weak atrial sound. Note specifically that the third and atrial heart sounds are each a very low rumble. The third heart sound can be recorded in only one third to one half of all people, and the atrial heart sound can be recorded in perhaps one fourth of all people.

Wavelet families:
Figure 2: Phonocardiograms for normal (A) and abnormal heart sounds
Wavelet analysis has practically become a ubiquitous tool in signal processing. Two basic properties, space and frequency localization and multiresolution analysis, make this a very attractive tool in signal analysis. The wavelet transform method processes perfect local property in both time space and frequency space and it use widely in the region of vehicle Faults detection and identification.
Several families of wavelets that have proven to be especially useful. Some wavelet Families are: Haar, Daubechies, Bi orthogonal, Coiflets, Sym lets, Morlet's, Mexican hat, Meyer, Other real wavelets, complex wavelets

Haar:
Any discussion of wavelets begins with Haar wavelet, the first and simplest. Haar wavelet is discontinuous, and resembles a step function. It represents the same wavelet as Daubechies db1.

Daubechies:
Figure 3: Step Response for Haar Wavelet
Ingrid Daubechies, one of the brightest stars in the world of wavelet research, invented what are called compactly supported orthonormal wavelets thus making discrete wavelet analysis practicable. The names of the Daubechies family wavelets are written dbN, where N is the order, and db the surname of the wavelet. The db1 wavelet, as mentioned above, is the same as Haar wavelet. Here is the wavelet functions psi of the next nine members of the family:
Figure 4: Different Daubechies Wavelets

Biorthogonal Wavelet: This family of wavelets exhibits the property of linear phase, which is needed for signal and image reconstruction. By using two wavelets, one for decomposition (on the left side) and the other for reconstruction (on the right side) instead of the same single one, interesting properties are derived.
Figure 5: Different Biorhogonal Wavelets

Coiflets: Built by I. Daubechies at the request of R. Coifman. he wavelet function has 2N moments equal to 0 and the scaling function has 2N1 moments equal to 0. The two functions have a support of length 6N1. You can obtain a survey of the main properties of this family by typing wave info ('coif') from the MATLAB command line
.
Figure 6: Various coiflets

Symlets: The symlets are nearly symmetrical wavelets proposed by Daubechies as modifications to the db family. The properties of the two wavelet families are similar. Here is the wavelet functions psi.
Figure 7: Representing the Symlets



PARAMETERS:

Variance:
The variance of a random variable X is its second central moment, the expected value of the squared deviation from the mean = E[X]:
This definition encompasses random variables that are discrete, continuous, neither, or mixed. The variance can also be thought of as the covariance of a random variable with itself:
The expression for the variance can be expanded:

MAX ENTROPY:
The statistics and information theory, a maximum entropy probability distribution is a probability distribution whose entropy is at least as great as that of all other members of a specified class of distributions.

Standard Deviation:
The standard deviation (SD) measures the amount of variation or dispersion from the average. A low standard deviation indicates that the data points tend to be very close to the mean (also called expected value); a high standard deviation indicates that the data points are spread out over a large range of values.

ENERGY:
Energy is a word with more than one meaning. Energy means something has the ability to cause change.
Mostly it is used in science to describe how much potential a physical system has to change.
It may also be used in economics to describe the part of the market where energy itself is harnessed and sold to consumers. It can sometimes refer to the ability for someone to act or speak in a lively and vigorous way.

Peak signalto noiseratio:
Peak signaltonoise ratio, often abbreviated PSNR, is an engineering term for the ratio between the maximum possible power of a signal and the power of corrupting noise that affects the fidelity of its representation. Because many signals have a very wide dynamic range, PSNR is usually expressed in terms of the logarithmic decibel scale.
Signaltonoise ratio (often abbreviated SNR or S/N) is a measure used in science and engineering that compares the level of a desired signal to the level of background noise. It is defined as the ratio of signal power to the noise power, often expressed in decibels. A ratio higher than 1:1 (greater than 0 dB) indicates more signal than noise. While SNR is commonly quoted for electrical signals, it can be applied to any form of signal (such as isotope levels in an ice core or biochemical signalling between cells).



EXPERIMENTAL RESULTS
Figure 8: Block Diagram for Proposed Scheme.

SIGNAL ACQUISITION:
Acquiring the heart beat signal from the database which is extracted from heart biometrics. DWT based biomedical systems has been developed by using the heart Sounds, obtained from a total of 5 heart sounds. Stages of feature extraction have been realized by using MATLAB R2012R software package. Using wave read function converting the heart signal into a graphical representation.
Fig 9. Converting heart signal in to Graphical form

Wave menu: By accessing wave menu comparing different wavelets along denoising the signal.
Fig 10. Wave menu
DWT has been used to derive feature Vectors from the heart sound signals. Selecting proper level of Wavelet and determining decomposition at best level play a significant role in the analysis of Heart Sound using discrete wavelet transform method. This decomposition levels is figured out based on the sampling frequency components of the signal. Using DWT as a filter at several levels helps in finding the best level of decomposition. There are apparent Differences between the graphics of the heart sound of a Normal subject and those from patients with Aortic Stenosis, Mitral Stenosis, Aortic Regurgitation & Mitral Regurgitation Diseases. These differences being reflected to heart sound Graphics are also reflected largely to DWT graphics. Therefore, such a classification system, established by taking such variances in DWT graphics into consideration enables to Decide on respective diseases.

Calculation: After wave menu and denoising signal PSNR, Variance, Energy, Entropy were calculated.

FLOW CHART
START
Acquiring Signals
Converting Signals into Graphs
Denoising using Wave menu
Calculating PSNR, Variance, Energy and Entropy
STOP

A. Selecting Haar Wavelet:



ANALYSIS OF DIFFERENT WAVELETS:
Figure 11. Haar wavelet applying the input signal

Comparison of original and denoised signal:
Figure 12. Comparison of original and denoised signal

Analyzing Signal with decomposition levels:
Figure 13. Analyzing the signal for all decomposition levels
Decomposition levels s =a10+d9+d8+d7+d6+d5+d5+d4+d3+d2+d1

Calculation of Parameters:

A. Selecting Biorthogonal Wavelets:
Figure 14. Different parameters
Figure 15. Biorthogonal wavelet applying the input signal

Comparison of original and denoised signal:
Figure 16. Comparison of denoised and original signal

Analyzing Signal with decomposition levels:
Figure 17. Analyzing the signal for all decomposition levels
Decomposition Levels S =a10+d9+d8+d7+d6+d5+d5+d4+d3+d2+d1

Calculation of parameters:


A. Selecting Coiflet Wavelet:
Figure 18. Different parameters
Figure 19. Coiflet wavelet applying the input signal
B. Comparison of denoised and original signal:
Figure 20. Comparison of original and denoised signal
Figure 21. Analyzing the signal for all decomposition levels
Decomposition Levels S =a10+d9+d8+d7+d6+d5+d5+d4+d3+d2+d1
D. Calculation of parameters:

A. Selecting Symlet Wavelet:
Figure 22. Different parameters
Figure 23. Symlet wavelet applying the input signal
B. Comparison of denoised and original signal:
Figure 24. Comparison of original and denoised signals
Figure 25. Analyzing the signal for all decomposition levels
Decomposition Levels S =a10+d9+d8+d7+d6+d5+d5+d4+d3+d2+d1
D. Calculation of parameters:
Figure 26. Different parameters


COMPARISON TABLES
BIORTHOGONAL: PARAMETERS
LEVEL
ENERGY
STD.DEVIATION
VARIANCE
RMS
MEAN
ENTROPY
1
1.3333
72.32757
0.0552
102.2412
71.7038
1.0000
2
1.2222
99.0206
0.0981
120.6622
70.5855
1.1345
3
0.2222
129.1677
0.1668
148.8588
76.0548
1.1038
4
0
167.4848
0.2805
186.8588
85.0539
1.0868
5
1.7778
219.2796
0.4808
239.7027
99.4809
1.2263
6
1.7778
289.9728
0.8408
312.5573
119.9432
1.5095
7
0.8889
386.4620
1.4935
412.3109
147.8239
1.8041
8
0
518.2618
2.6860
548.6257
185.2386
2.1766
9
0.6667
698.5166
4.8793
374.9157
235.1667
2.3310
10
0.6667
945.3923
8.9377
989.7441
301.7005
2.4729
HAAR WAVELET:PARAMETERS
LEVEL
ENERGY
STD.DEVIATION
VARIANCE
RMS
MEAN
ENTROPY
1
0.6667
77.5479
0.0601
103.3729
72.6199
1.0000
2
0.5556
96.3941
0.0929
108.9710
58.5455
0.9457
3
0.6667
114.2607
0.1306
121.8419
53.6462
0.9183
4
0.6667
120.9712
0.1463
121.8419
37.8276
0.8113
5
0.7778
164.4227
0.2703
165.1935
48.3080
0.7793
6
0.8889
224.1892
0.5026
224.8771
62.4415
0.7496
7
1.2222
306.4556
0.9392
307.0739
81.4885
0.7219
8
1.1111
419.8046
1.7624
420.3642
107.1675
0.6962
9
1.4444
576.1471
3.3195
576.6566
141.8220
0.6723
10
1.5556
792.0186
6.2739
792.4848
188.6489
0.6500
COIFLET WAVELET:PARAMETERS
LEVEL
ENERGY
STD.DEVIATION
VARIANCE
RMS
MEAN
ENTROPY
1
0.8889
76.8953
0.0591
103.8852
72.8121
1.0000
2
0.5556
100.1194
0.1002
117.7363
66.0721
0.9980
3
0.3333
126.0577
0.1589
139.9195
66.1651
1.1916
4
0.2222
164.7530
0.2714
178.9175
76.4024
1.1393
5
0.1111
217.4429
0.4728
232.7706
91.2881
1.1546
6
0
289.2748
0.8368
306.5477
111.7750
1.2784
7
1.0000
387.3072
1.5001
407.3683
139.3936
1.3991
8
0.2222
521.2832
2.7174
545.1303
176.3294
1.4917
9
1.0000
704.6552
4.9654
733.5294
225.5993
1.5744
10
0.4444
986.0221
9.1398
991.5075
291.3196
1.5374
SYMLET WAVELET:PARAMETERS
LEVEL
ENERGY
STD.DEVIATION
VARIANCE
RMS
MEAN
ENTROPY
1
0.8889
78.2069
0.0612
105.4748
74.2853
1.0000
2
0.4444
100.6584
0.1013
115.0163
61.8091
0.9852
3
0.6667
124.5850
0.1552
133.6927
57.6402
0.9544
4
1.0000
163.3963
0.2670
173.2281
68.6646
0.9819
5
1.1111
216.4295
0.4684
227.4835
83.8819
0.9940
6
1.0000
288.8590
0.8344
301.6666
104.4068
0.9988
7
1.7778
387.8446
1.5042
403.0321
131.8471
1.0000
8
1.6667
523.2856
2.7383
541.6296
168.4277
0.9992
9
1.5556
708.8686
5.0249
731.3587
217.1863
0.9975
10
1.0000
963.5249
8.2838
991.4417
282.2549
0.9953
Table 1: Comparison of different wavelets

CONCLUSION
Different wavelets are considered for the analysis of heart sounds such as HAAR, DB, Bior, Ortho, Coiflets and Symmlets. In this paper PSNR, Variance, Mean, Entropy, Energy and denoising the values along level 10decomposition has been calculated and shown the graphs above section. The decomposition and Denoising are calculated and plotted under the basis of wave menu from MATLAB. The factors are calculated and were shown their analysis under different wavelets. So, the maximum probable output has been shown and plotted as bar graphs even histogram levels were calculated and plotted in this paper. After completing all dwt methods it is found that coiflet is the best method among the various wavelet methods

REFERENCES:



Todd R. Reed, Nancy E. Reed, Peter Fritzson, Heart sound analysis for symptom detection and computeraided diagnosis, Simulation Modeling Practice and Theory 12 (2004) 129146

Guy Amit, Noam Gavriely, Nathan Intrator, Cluster analysis and classification of heart sounds, Biomedical Signal Processing and Control 4 (2009) 2636

Faizan Javed, P A Venkatachalam, Ahmad Fadzil M H, A Signal Processing Module for the Analysis of Heart Sounds and Heart Murmurs, Journal of Physics: Conference Series 34 (2006) 10981105

Sumeth Yuenyong, Akinori Nishihara, Waree Kongprawechnon, Kanokvate Tungpimolrut, A framework for automatic heart sound analysis without segmentation, Biomed Eng Online. 2011; 10: 13 Published online 2011 February 9

Dr. H. B. Kekre, Tanuja Sarode, Shachi Natu & Prachi Natu Performance Comparison Of 2D DCT On Full/BlockSpectrogram And 1 D DCT On Row Mean Of Spectrogram For Speaker Identification, International Journal of Biometrics and Bioinformatics (IJBB), Vol (4): Issue 3 [6] Dr. H B Kekre , Vaishali Kulkarni, Speaker Identification using Row Mean of Haar and Kekres Transform on Spectrograms of Different Frame Sizes, (IJACSA)
International Journal of Advanced Computer Science and Applications Special Issue on Artificial Intelligence

http://www.peterjbentley.com/heartchallenge/index.html