Analysis of Various DWT Methods for Feature Extracted PCG Signals

DOI : 10.17577/IJERTV4IS041236

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Analysis of Various DWT Methods for Feature Extracted PCG Signals

  1. Venkata Hari Prasad1,

    1. 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:- ECG-Electrocardiogram , PCG-Phonocardiogram, AS- Aortic Stenosis, AR- Aortic Regurgitation , MS- Mitral Stenosis, MR- Mitral Regurgitation , DWT- Discrete Wavelet Transform, ENER-Energy ,STD-Standard Deviation, VAR- Variance, NHS- Normal Heart Sound

      1. 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 4.7-5.7 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 (cord-like 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].

      2. Methodology

        1. 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 low-frequency sound is placed on the chest, the heart sounds can be amplified and recorded by a high-speed 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.

        2. 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 multi-resolution 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

            • Bi-orthogonal 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 2N-1 moments equal to 0. The two functions have a support of length 6N-1. 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

        3. 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 signal-to noise-ratio:

          Peak signal-to-noise 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.

          Signal-to-noise 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).

      3. EXPERIMENTAL RESULTS

        Figure 8: Block Diagram for Proposed Scheme.

        1. 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

        2. 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.

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

        4. FLOW CHART

          START

          Acquiring Signals

          Converting Signals into Graphs

          Denoising using Wave menu

          Calculating PSNR, Variance, Energy and Entropy

          STOP

          1. A. Selecting Haar Wavelet:

      4. ANALYSIS OF DIFFERENT WAVELETS:

        Figure 11. Haar wavelet applying the input signal

        1. Comparison of original and denoised signal:

          Figure 12. Comparison of original and denoised signal

        2. 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

        3. Calculation of Parameters:

          1. A. Selecting Biorthogonal Wavelets:

            Figure 14. Different parameters

            Figure 15. Biorthogonal wavelet applying the input signal

            1. Comparison of original and denoised signal:

              Figure 16. Comparison of denoised and original signal

            2. 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

            3. Calculation of parameters:

          2. 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:

          3. 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

      5. 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

      6. 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

      7. REFERENCES:

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

  3. 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

  4. 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

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    International Journal of Advanced Computer Science and Applications Special Issue on Artificial Intelligence

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