40120140504007 2

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40120140504007 2

  1. 1. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 4, April (2014), pp. 50-56 © IAEME 50 SEGREGATION OF ACOUSTIC SIGNAL USING WAVELET TRANSFORM Er. Tarana Afrin Chandel Department of Electronics and Communication Engineering, Integral University, Lucknow, India ABSTRACT Auscultation, the technique of listening to heart sound with a stethoscope, thus diagnosing heart valve disorder. In Phonography (PCG) the heart sound are recorded using stethoscopes and displayed on the PC/laptop rather than listening to the heart sound as done in traditional auscultation. More detail is accessible visually, because the analysis is not limited by the human audibility or experience of the physician while listening. The heart disease can be detected by the symptoms of pathology appears and this makes it a high potential diagnosis test for future the aim of this study is to detect various disease using PCG signals Many diagnostic feature can be extracted using PCG which otherwise require test like Electrocardiography (ECG) or Echocardiography. In this paper we present a method that is channel noise reduction of heart sound. The fetal sound signals are detected and reconstructed by utilizing wavelet transform based on signal. Keywords: Signal to noise ratio (SNR), Peak signal to noise ratio (PSNR), PCG signal, Wavelet transform. I. INTRODUCTION Auscultation of the heart remains an important examination for the detection of cardiovascular disease. The auscultatory exam is expedient and cost effective. When completed by an experienced clinician, auscultation carries a high predictive value for identification of many serious heart diseases. Definitive diagnosis may be possible by auscultation, as when classic murmurs of patent ductus arteriosus or mitral regurgitation are identified. Often the combination of signalment, cardiac and pulmonary auscultation, and general physical examination point to a tentative cardiac diagnosis. This presumption can then be confirmed, refined, or refuted by echocardiography (for valvular disease, pericardial disease, cardiomyopathy) or by electrocardiography (for arrhythmias). INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATION ENGINEERING & TECHNOLOGY (IJECET) ISSN 0976 – 6464(Print) ISSN 0976 – 6472(Online) Volume 5, Issue 4, April (2014), pp. 50-56 © IAEME: www.iaeme.com/ijecet.asp Journal Impact Factor (2014): 7.2836 (Calculated by GISI) www.jifactor.com IJECET © I A E M E
  2. 2. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 4, April (2014), pp. 50-56 © IAEME 51 The essential abnormalities of cardiac auscultation include: abnormal heart rate (bradycardia, tachycardia), irregular cardiac rhythm, abnormal intensity of heart sounds, extra heart sounds, cardiac murmurs, and pericardial friction rubs. II. TECHNIQUES FOR CARDIAC AUSCULTATION The choice of a stethoscope is very personal. The traditional stethoscope has an operator selected diaphragm and bell. Some stethoscope designs allow a single chest piece to function as both a bell and a diaphragm. However, some of the newer models combine both “adult” and “pediatric” single chest piece stethoscopes into one rotating head. Amplified stethoscopes generally are not recommended because of the potential for blooming artifacts and distortion; however, they can be useful for those with a hearing impairment. The clinician must understand that many heart sounds fall below the frequency-threshold limit; accordingly, careful auscultation is necessary to detect the vibrations that are audible. The stethoscope tube length should not be excessively long, the binaural and ear pieces should be directed so that their orientation is rostral and aligned with the ear canals, and earpieces should be inserted snugly but comfortably to obtain an airtight seal. The flat diaphragm chest piece is applied gently but firmly to the chest to accentuate higher frequency sounds such as normal heart and breath sounds and most cardiac murmurs. The bell, which is applied lightly to achieve an airtight seal, enhances detection of lower frequency sounds such as the third and fourth heart sounds. The bell is also useful for detection of the more uncommon diastolic murmurs. The entire pericardium is examined, with particular attention directed to the cardiac valve areas. [1, 2] While the exact anatomic location of the valve areas depends on the chest conformation, and size of the heart, a common relative location is found from cranial to caudal: pulmonic–aortic– tricuspid–mitral with the tricuspid valve on the right. A useful clinical pointer is to first palpate the left apex beat where mitral sounds radiate and the first heart sound is best heard. Find other valve areas from this point. The aortic valve area is located craniodorsal to the left apex and the second heart sound is best heard there. Once the aortic second sound is identified, the stethoscope can be moved one interspace’s cranial and slightly ventrad (over the pulmonary valve area).The pulmonary artery extends dorsally from the pulmonic valve. The tricuspid valve is over the right hemi thorax, cranial to the mitral area, and covers a relatively wide area. The LV outlet is in the center of the heart and aortic murmurs usually radiate well to each hemi thorax. Cardiac apex and cardiac base are commonly used expressions to designate the regions ventral and dorsal to the atrioventricular groove. The mitral and tricuspid valvular sounds often radiate ventrally towards the apex. Murmurs originating at the semi-lunar valves and great arteries are detected best over the base. The author typically defines auscultation areas as caudal (chest piece centered over the apex beat), cranial (1–2 intercostals spaces cranial to the apex), left sternal and right sternal. Fig1 shows typical auscultation sites to place microphone. Fig.1: Typical auscultation sites to place microphones
  3. 3. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 4, April (2014), pp. 50-56 © IAEME 52 III. MECHANISM FOR TRANSIENT CARDIOVASCULAR SOUND In this section we show the mechanism for the production of transient cardiovascular sound. Cross sectional view of human heart is shown in fig2. The human heart has four chambers, two superior atria and two inferior ventricles. The atria are the receiving chambers and ventricles are the discharging chambers.[3] Normal heart sounds are associated with valves closing, causing changes in blood flow.[4] The main frequency range of the PCG signal is in the range of 10 - 35 KHz however in the case of artificial heart valves frequencies up to 50 KHz is recorded. Heart sound is generated by the vibration of heart valves during their opening and closure and by the vibration of myocardium and the associated structure. The sound generated by the human heart during the cardiac cycle consists of two domain component called the first heart sound S1 and the second heart sound S2. Fig2. Show a phonocardiogram signal from a healthy person containing the first heart sound (S1) and the second heart sound (S2). Fig. 2: Cross-section of a typical human heart The first heart sound S1, forms the lub-of lub-dub and is composed of component M1 and T1. Normally M1 precedes T1 slightly. It is caused by sudden blocks of reversal blood flow due to closure of the atrioventricular valves (tricuspid) and mitral (bicuspid) at the beginning of ventricular contraction, or systole. When the ventricles begin to contract, so do the papillary muscles in each ventricle. The papillary muscles are attached to the tricuspid and mitral valves via chordate Fig. 3: A phonocardiogram signal from a healthy person containing the first heart sound (S1) and the second heart sound (S2) tendineae, which brings the cusps or leaflet of the valve close, the chordae tendineae also prevent the valve from blowing into the atria as ventricular pressure rise due to contraction. The closing of the inlet valves prevents regurgitation of blood from the ventricles back to atria. The S1 sound results from reverberation within the blood associated with the sudden block of flow reversal by the valves.[3] If M1 occurs slightly after T1, then the patient likely has a dysfunction of conduction of the left side of the heart such as a left bundle branch block.
  4. 4. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 4, April (2014), pp. 50-56 © IAEME 53 The second heart sound S2, forms the "dub" of "lub-dub" and is composed of components A2 and P2. Normally A2 precedes P2especially during inspiration when a split of S2 can be heard. It is caused by the sudden block of reversing blood flow due to closure of the semilunar valves (aortic valve and pulmonary valve) at the end of ventricular systole and the beginning of ventricular diastole. As the left ventricle empties, its pressure falls below the pressure in the aorta. Aortic blood flow quickly reverses back toward the left ventricle, catching the pocket-like cusps of the aortic valve, and is stopped by aortic valve closure. Similarly, as the pressure in the right ventricle falls below the pressure in the pulmonary artery, the pulmonary valve closes. The S2 sound results from reverberation within the blood associated with the sudden block of flow reversal. Splitting of S2, also known as physiological split, normally occurs during inspiration because the decrease in intrathoracic pressure increases the time needed for pulmonary pressure to exceed that of the right ventricular pressure. A widely split S2 can be associated with several different cardiovascular conditions, including right bundle branch block, pulmonary stenosis, and atrial septal defects. [5, 6, 7] Heart murmurs are produced as a result of turbulent flow of blood strong enough to produce audible noise. [8, 9] They are usually heard as a whooshing sound. The term murmur only refers to a sound believed to originate within blood flow through or near the heart; rapid blood velocity is necessary to produce a murmur. It should be noted that most heart problems do not produce any murmur and most valve problems also do not produce an audible murmur. Heart murmurs of aortic (stenosis, regurgitation) and mitral (stenosis, regurgitation) is in fig 4. Fig. 4: Heart murmurs IV. METHOD Due to the overlap of the heart sound components and the noises caused by other internal organs, a full analysis of the heart sound in the time-domain is difficult. The heart sound is more important than the heart rate and accordingly we will subsequently restrict the processing of heart sound to the frequency domain. Wavelet transform is used for the segregation of PCG signal, as wavelet helps to do multi-resolution analysis to achieve time and frequency domain. Wavelet functions used for signal analysis are derived from the initial function W(t) forming basis for the set of functions. Wm, k (t) = (1/√a) W (1/a (t – b) = (1/ √2m ) W (2-m t – k) (1) For discrete parameters of dilation a =2m and translation
  5. 5. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 4, April (2014), pp. 50-56 © IAEME 54 b =k 2m .Wavelet dilation, which is closely related to spectrum compression, local and global signal analysis. For short-time Fourier transform, as in the case of speech signals, a short-time stationary property for heart sounds is assumed, which conveniently allows a STDFT analysis. [12] N −1 X [n, k] = ∑ x ∗ w [m + (n − 1) S] exp (-j 2π km) (2) m=0 N Where n is the frame index, k is the frequency index, N is the frame length, S is the frame shift and w denotes the window. Unlike speech signals, where the vocal track is changing after each 20-25ms, heart sounds are more stationary and therefore the window length should be larger. The optimal window length was found to be about 500ms through experiments conducted in subsequent section. A related issue is the effect of the window shift on the performance of the pattern recognition method. Gaussian white noise mixture modelling is used between samples in order to accurately estimate the probability distribution. [7] Since the human heart sound is quasi-stationary with much longer segment than speech and consequently the standard 20-25ms frame length for speech processing did not matched accurately. This point was detected by the experiments done. The frame length is very important issue. The standard in speech frame length is ineffective for the heart sounds, yielding less than 70% accuracy. Thus 512ms is shown to be the optimal choice of frame length. After scaling the component at different threshold levels, wavelet coefficient reaches zero if it is below threshold level and contains the same value if it is above the threshold level at last the filtered and reconstructed signals can be retrieved by wavelet inverse transform. V. RESULT Comparative analysis is done between two signal ie original PCG signal mixed with low intensity Gaussian noise and the other signal is original PCG signal mixed with high intensity Gaussian noise. Simulated results are obtained through signal to noise ratio and peak signal to noise ratio of the denoise signal using wavelet transform. SNR= power of signal/ power of noise (1) PSNR= 10*log10 ((N*(X^2))/E diff (2) Table. 1: Signal to noise ratio for sampled signals Level / wavelet Norma heart sound Artial Spectral defect Artial Spectral defect Patent Ductus Arterios us db2 7.5656 3.876 4.269 1.0234 db4 2.8190 6.358 6.853 5.293 db6 2.1053 6.213 6.248 4.967
  6. 6. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 4, April (2014), pp. 50-56 © IAEME 55 IV. CONCLUSION Phonocardiography is a method of accessory value in cardiac conditions. It is technically a difficult procedure. It has been of greatest help in the timing of heart sounds having abnormal components or unusual accentuation of normal components-split sounds, third sounds, auricular sounds, and gallop rhythms. This difficulty is overcome using wavelet transform. In this paper different cardiac disease was detected using wavelet transform. In only rare instances are the data provided by phonocardiography of critical importance in diagnosis. Such instances include the recording of murmurs which are inaudible due to masking and fatigue effects on the ear from previous loud sounds; the demonstration of true presystolic murmurs of mitral stenosis; and the definition of characteristic patterns of pulmonic and aortic stenosis. The discriminating use of phonocardiography will undoubtedly be increasingly helpful in the diagnosis of valvular and congenital heart disease. ACKNOWLEDGMENT The authors would like to thank Dr. Shefta T Chandel for her contribution to prepare this paper. Also the author would like to thank Prof. (Dr.) S. Hasan Saeed, HOD, ECE Department I.U for his whole hearted support. Furthermore, the authors would like to acknowledge their friends and colleague for their fruitful and constructive comments. REFERENCES [1] J. Ortega-Garcia, Bigun J., D. Reynolds and J. Gonzalez-Rodriguez., “Authentication gets personal with biometrics”, Signal Processing Magazine, IEEE, vol. 21 , issue 2, pp. 50 - 62, March 2004. [2] Amit G, Gavriely N, Lessick J, Intrator N. Acoustic indices of cardiac functionality. In: International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS). Setubal, Portugal: INSTICC, 2008, p. 77–83. [3] J.C. Wood and D.T. Barry, "Time-Frequency Analysis of the First Heart Sound", IEEE Engineering In Medicine and Biology Magazine, pp. 144-151, 1995. [4] Human identification using heart sound Koksoon Phua, Tran Huy Dat,Jianfeng Chen and Louis Shue [5] F. G. William. Review of Medical Physiology, Prentice Hall, 1997. [6] B. N. Robert. Noninvasive Instrumentation and Measurement in Medical Diagnosis, CRC Press, 2002. [7] D. A. Reynolds and R. C. Rose. “Robust Text-Independent Speaker Identification using Gaussian Mixture Speaker Models.”, IEEE Transactions on Speech and Audio Processing, vol. 3, pp. 72 – 83, January 1995 [8] D.W. Sapire, "Understand and Diagnosing pediatric Heart Disease: Heart Sounds and Murmurs", Norwalk, Connecticut, Appleton & Lange, pp. 27-43, 1992. [9] Z. Xuan, L-G. Durand, L. Senhadji, H.C. Lee, J-L.Coatrieux, "Analysis - Synthesis of the Phonocardiogram Based on the Matching Pursuit Method ", IEEE Trans. Biomed. Eng. Vol.45. pp.962-971, 1998. [10] J.C. Wood and D.T. Barry, "Time-Frequency Analysis of the First Heart Sound", IEEE Engineering In Medicine and Biology Magazine, pp. 144-151, 1995.
  7. 7. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 4, April (2014), pp. 50-56 © IAEME 56 [11] A. K. Jain, A. Ross, and S. Prabhakar. “An Introduction to Biometric Recognition”, IEEE Transactions on Circuits and Systems for Video Technology, vol. 14, no. 1, pp. 4 – 20, January 2004. [12] J. P. Campbell Jr. ”Speaker Recognition: A Tutorial”, Proceeding of the IEEE, vol. 85, no. 9, pp 1437-62, September 1997. [13] Chissanuthat Bunluechokchai and Weerasak Ussawawongaraya, A Wavelet-based Factor for Classification of Heart Sounds with Mitral Regurgitation International Journal Of Applied Biomedical Engineering 2(1) 2009 44-48. [14] AF Quiceno, E Delgado, M Vallverd, AM Matijasevic and G Castellanos-Domnguez, Effective Phonocardiogram Segmentation Using Nonlinear Dynamic Analysis and High- Frequency Decomposition Computers in Cardiology 35 2008 161-164. [15] Er. Ravi Garg and Er. Abhijeet Kumar, “Compression of SNR and MSE for Various Noises using Bayesian Framework”, International Journal of Electronics and Communication Engineering & Technology (IJECET), Volume 3, Issue 1, 2012, pp. 76 - 82, ISSN Print: 0976- 6464, ISSN Online: 0976 –6472. [16] Mohamed Basheer. K. P and Dr. T. Abdul Razak, “Enhanced Biometric Based Authentication for Network Security using IRIS”, International Journal of Computer Engineering & Technology (IJCET), Volume 4, Issue 6, 2013, pp. 412 - 422, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. [17] Atul Pradhan, Vidushi Kapoor, Sanjay Kumar, Prateek Tandon and Priyanka Kumari, “Analytical Techniques used for Disease Diagnosis–Invasive and Non-Invasive Tools”, International Journal of Advanced Research in Engineering & Technology (IJARET), Volume 4, Issue 1, 2013, pp. 9 - 27, ISSN Print: 0976-6480, ISSN Online: 0976-6499.

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