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International Journal of Scientific Research Engineering & Technology (IJSRET), ISSN 2278 – 0882 Volume 3, Issue 4, July 2014 A Novel Technique for Analysis of Heart Sound Signal 1 Alpana Sahu, 2Chinmay chandrakr, 3Dr Monisha sharma 1 1,2,3 Research scholar (PG Student), Department of Electronic and Telecommunication, CSVTU, Chhattisgarh, India ABSTRACT In today medical prevention, the early diagnosis of cardiac diseases is one of the most important topic. the heart sound signal stores a huge amount of information regarding the pathological status of every part of the heart and the interaction among them. A phonocardiogram is a recording of the acoustic waves produced by the mechanical action of the heart. It generally consists of two kind of acoustic vibration: the heart sound and the heart murmur. These murmurs create problem in the heart and causes heart disease although different diseases produce characteristic sound. Auditory discrimination of heart sounds is inherently difficult as these sounds are faint and lie at the lower end of the audible frequency range. The proposed paper works on the identification of diseases present in the heart but we are using this algorithm only for aortic insufficient, aortic stenosis, patent ductus arterious, ebsteins disease or innocent murmurs venous hum diseases. The paper focus on the input sound entered into the stethoscope and compares their values with the database already mentioned for different types of diseases by calculating their mean, variance, and standard deviation, maximum value of power spectral density. KEYWORD:- phonocardiogram signal, acoustic wave, cardiac disease I. INTRODUCTION Heartbeats vary depending on various factors such as age, physical state, and stimuli. A child has a smaller heart and therefore their heart needs to beat faster in order to pump the proper amount of blood. The heartbeat rate for infants is 120 per minute, for a child is about 90 times per minute, and for a person over age 18 is about 70 times per minute. A physically fit person has a lower heart rate as compared to an inactive person. Stimuli resulting in stress, fear or excitement will result in a rapid heartbeat. Nerves connected to the heart regulate the speed with which the cardiac muscle contracts. Interestingly, in an average lifetime, the heart continuously beats more than 2.5 billion times. Figure: 1.1 Diagram of the heart The PCG is a trace of acoustic energy produced by the mechanical activity of various cardiac components and processes (Durand and Pibarot 1995). As a result, any abnormality in cardiac components manifests itself in the corresponding sounds in the PCG. The phonocardiogram (PCG) is a sound signal related to the contractile activity of the cardio hemic system. The general state of the heart in terms of contractility and rhythm can be provided by heart sounds characteristics Cardiovascular diseases and defects cause changes or additional sounds and murmurs that could be useful in their diagnosis. A normal cardiac cycle contains two major sounds: the first heart sound S1 and the second heart sound S2. S1 occurs at the onset of ventricular contraction and corresponds in timing to the QRS complex hence it can be easily identifiable if the ECG is available which is frequently the case. S2 follows the systolic pause and is caused by the closure of the semilunar valves[1,3,4]. Heart sound analysis by auscultation depends highly on the skills www.ijsret.org 733 International Journal of Scientific Research Engineering & Technology (IJSRET), ISSN 2278 – 0882 Volume 3, Issue 4, July 2014 and experience of the listener. Despite the importance of auscultation the internal medicine and cardiology training programs underestimate the value of cardiac auscultation and the new clinicians are not well trained in this field. It has been reported that extremely high percentage of patients referred to the cardiologist for evaluation have begin heart sounds[4,7]. Iwata et al (1980) developed a detection algorithm to detect the first (S1, caused by the closure of the mitral and the tricuspid valves) and the second (S2, caused by the closure of the aortic and the pulmonary valves) heart sounds based on frequency domain characteristics of the PCG evaluated by linear prediction methods. Lehner and Rangayyan (1987) proposed an algorithm for PCG segmentation using the electrocardiogram (ECG) and the carotid pulse as references. In their algorithm the beginning of S1 is estimated by the onset of the R wave in the ECG and the beginning of S2 is estimated by using the Dicrotic Notch in the carotid pulse. In Baranek et al (1989), major events in the cardiac cycle are detected by analyzing the PCG envelope found by either low pass filtering or with the Hilbert transform. Haghighi-Mood and Torry (1995) developed an algorithm that tracks the energy in a certain frequency band to detect S1 and S2. They also use an ECG reference to isolate cardiac cycles within a PCG. Liang et al (1997) proposed an algorithm that uses the envelogram of the PCG, which is calculated by using Shannon energy to detect S1 and S2 peaks. Later (Liang et al 1998) they proposed an improvement in their algorithm by utilizing the quantized values of the spectrogram of the PCG. Recently, Reed et al (2004) proposed a model-based approach to detect the presence of major heart sounds. They model the occurrence of S1 and S2 by a pair of impulses, respectively, that are convolved with the thoracic transfer function to yield S1 and S2 on the chest. Hult et al (2005) proposed a tailored wavelet-based approach to detect the presence of the low amplitude and low frequency S3 sound. Gamero and Watrous (2003) proposed a hidden Markov model (HMM) based probabilistic approach to model the systolic and diastolic interval durations, which are subsequently used to detect the presence of S1 and S2. Although a model-based approach has the ease of algorithmic implementation, it is sensitive to modeling (such as the choice of the wavelet) and model parameter estimation (like determining the parameters of an HMM). A major disadvantage of using the ECG as a reference is that the timing between electrical and mechanical activities in a cardiac cycle will not be exactly constant for all patients because of a variety of pathological conditions (Haghighi-Mood and Torry 1995). II. PROBLEM IN THE HEART Heart Valve disease occurs when a valve doesn't work properly. If a valve doesn't open all the way, less blood can move through the smaller opening. If a valve doesn't close tightly, blood may leak backward. These problems can cause the heart to work harder to pump the same amount of blood[1]. Or blood may back up in the lungs or body because it's not moving efficiently through the heart. When the valve doesn't open completely, it is called Stenosis. When it doesn't close completely it is called Insufficiency or Regurgitation some of them are: Aortic stenosis. Aortic insufficiency. Ebstein's disease. Functional or innocent murmurs venous hum. Patent ductus arteriosus. Many pathological conditions that occur in the cardiovascular system surface as murmurs and aberrations in a PCG much before they are reflected in other symptoms, such as changes in the electrocardiogram signal. The PCG is a trace of acoustic energy produced by the mechanical activity of various cardiac components and processes. As a result, any abnormality in cardiac components manifests itself in the corresponding sounds in the PCG. Most of the automated diagnostic algorithms that use the PCG as a reference signal to detect symptoms of cardiac abnormalities apply time segmentation as a pre-processing step to extract successive cardiac cycles[6]. Automatic time segmentation of the PCG is a difficult task as it is normally superimposed by environmental, sensor and other physiological noises. Here we proposed an improvement in their algorithm by utilizing the quantized values of the spectrogram of the PCG. A major disadvantage of using the ECG as a reference is that the timing between electrical and mechanical activities in a cardiac cycle will not be exactly constant for all patients because of a variety of pathological conditions [4]. Furthermore, gating a cardiac murmur based on energy methods will not yield accurate results for murmurs that are of the crescendo or decrescendo type due to their weak initial or final amplitude. Similarly, frequency domain based methods also suffer from the problem of detecting inaccurate boundaries due to time-frequency resolution. Most of the disadvantages of existing time-segmentation algorithms occur because they depend on absolute measures like the amplitude or the frequency distribution of the PCG. These absolute measures show a large variation within subject groups. When visually inspecting a signal, one of the first impressions of the observer is that of the, „complexity‟, of the signal. For example, while examining a PCG, S1 and S2 (also S3 and S4 if they are present) appear to be less www.ijsret.org 734 International Journal of Scientific Research Engineering & Technology (IJSRET), ISSN 2278 – 0882 Volume 3, Issue 4, July 2014 complex as compared to the murmurs, which are less complex as compared to the background noise. Complexity is invariant to envelope amplitude variations, and hence measuring complexity alleviates many of the problems of traditional segmentation algorithms. In this paper we propose a method that utilizes the inherent complexity of heart sound components to isolate them from a composite PCG. It has been reported that extremely high percentages (as much as 87 %) of patients referred to cardiologists for evaluation have benign heart sounds [9]. So a computer assisted system can help the general physician in coming up to a more accurate and reliable diagnosis at early stages and also can reduce unnecessary referrals of patients to expert cardiologists at a distant. As part of our major effort towards developing a system for tele-diagnosing heart diseases, a signal processing module has been developed that can assist the general physicians. The module uses heart sound signals that are taken as input from an electronic stethoscope which can be made available to the primary healthcare units [1-10]. These signals are then processed through embedded sophisticated signal processing algorithms before a final diagnosis can be made. Heart murmurs are generated by turbulent flow of blood, which may occur inside or outside the heart. Murmurs may be physiological (benign) or pathological (abnormal). Abnormal murmurs can be caused by stenosis restricting the opening of a heart valve, resulting in turbulence as blood flows through it. Abnormal murmurs may also occur with valvular insufficiency (or regurgitation), which allows backflow of blood when the incompetent valve closes with only partial effectiveness [9][10]. Different murmurs are audible in different parts of the cardiac cycle, depending on the cause of the murmur. This paper basically works to implement a project which deals with abnormality caused by different types of disease. Auditory discrimination of heart sounds is inherently difficult as these sounds are faint and lie at the lower end of the audible frequency range. The proposed paper works on the identification of diseases present in the heart but we are using this algorithm only for aortic insufficient, aortic stenosis, patent ductus arterious, ebsteins disease or innocent murmurs venous hum diseases. The paper focus on the input sound entered into the stethoscope and compares their values with the database already mentioned for different types of diseases by calculating their mean, variance, and standard deviation, maximum value of power spectral density and by using spectrogram function for the signal. It is effective method for diagnosing mentioned diseases. S1 S2 Original Signal M1 Constituent1 T1 Constituent2 A2 Constituent3 P2 Constituent4 Separation of a heart sound containing tricuspid regurgitation into its spatially distributed constituents: mitral (M1), tricuspid (T1) with regurgitant murmur, aortic (A2) and pulmonic (P2) components. Figure: waveform of normal heart sound III. METHODOLOGY The detection of heart sound is a tedious job, just by hearing any heart sound the Deficiency cannot be easily predicted. There arise lots of problems to differentiate between various heart sounds. The heart sounds varies from person to person. Noise interference is present in every heart sound due to which it is very tough to identify the exact variation in the heart sound that only the skilful and experienced physicians can trace out the deficiency. The input signal, normal heart signal, aortic insufficiency, aortic stenosis, patent ductus arterious, ebstein‟s disease & incorrect murmurs venous hum wave are taken in matrix form. The. Along with the audio heard if we display the variation in the heart sounds, it becomes so easy for the physicians to detect the variations in the heart sounds with help of various parameters viz 1. Mean 2. Variance 3. Standard deviation www.ijsret.org 735 International Journal of Scientific Research Engineering & Technology (IJSRET), ISSN 2278 – 0882 Volume 3, Issue 4, July 2014 4. Power spectral density 5. Spectrogram. 1. Mean Syntax:-M=mean(A) M = mean(A,dim) Definition:-Arithmetic average of a range of values or quantities, computed by dividing the total of all values by the number of values. 𝑋 𝑀= 𝑛 2. Variance Syntax:-V=var(X) V=var(X,1) V=var(X,w) V = var(X,w,dim) Definition:-If a random variable X has the expected value (mean) μ = E[X], then the variance of X is given by: 𝑉𝑎𝑟 𝑋 = 𝐸[(𝑋 − 𝜇)2 ] 3. Standard deviation Syntax:s=std(X) s=std(X,flag) s = std(X,flag,dim) Definitions:- There are two common textbook definitions for the standard deviation s of a data vector X. 1 n−1 s=( 1 s=( n 1 n 2 i=1(xi− x) )2 n 1 (xi− x)2 )2 1 2 i=1 Where 1 𝑥= 𝑛 𝑛 𝑥𝑖 𝑖=1 4. Maximum value of Power spectral density of the signal is calculated Definition:-The power spectral density (PSD) is intended for continuous spectra. The integral of the PSD over a given frequency band computes the average power in the signal over that frequency band. In contrast to the mean-squared spectrum, the peaks in this spectrum do not reflect the power at a given frequency. P (t) = S (t)2 S (f) = ∞ 𝑅 𝜏 𝑒 −2𝜋𝑖𝑓𝜏 𝑑𝜏 = 𝐹(𝑅 𝜏 ) −∞ 5. The functions like spectrogram can also be used for such purposes Definition:-Spectrogram, when used without any outputs, plots a spectrogram or, when used with an S output, returns the short-time Fourier transform of the input signal. To create a spectrogram from the returned short-time Fourier transform data. S = spectrogram(x) returns S, the short time Fourier transform of the input signal vector x. By default, x is divided into eight segments. If x cannot be divided exactly into eight segments, it is truncated. www.ijsret.org 736 International Journal of Scientific Research Engineering & Technology (IJSRET), ISSN 2278 – 0882 Volume 3, Issue 4, July 2014 Start . Load the heart sound Calculate the parameter of input heart sound Taking average of these parameter Compare the average of input heart sound with other heart sound in database No Is the sound matched? Yes Display the name of the sound with which it is matched End Figure: flow chart of given algorithm IV. RESULT We have so designed our algorithm that when a heart sound is inserted and processed, it is compared with the heart sound present in our data base and then displays the result as the heart sound is being suffering from disorder or else it is working properly. The comparison is based on certain parameters discussed 0.18 0.2 20 80 0.16 0.18 15 60 0.14 0.16 10 40 0.12 0.14 20 0.12 0.1 0 0.08 5 0.1 0.08 -20 0.06 -40 0.04 0 -5 0.06 0.04 0 0 20 40 60 80 100 120 0 -80 -80 140 -10 0.02 -60 0.02 -60 -40 -20 0 20 40 60 80 0.03 0 20 40 60 80 100 120 -15 -20 140 0.07 -15 -10 -5 0 5 10 15 20 30 60 0.025 40 0.02 20 0.06 20 0.05 10 0.04 0 0.015 0 0.03 -20 0.01 -10 0.02 -40 0.005 0 20 40 60 80 100 120 140 0.2 -80 -80 -20 0.01 -60 0 0 -60 -40 -20 0 20 40 60 80 0 20 40 60 80 100 120 140 -30 -25 -20 -15 -10 -5 0 5 10 15 20 25 80 0.18 60 0.16 40 0.14 20 0.12 0.1 0 0.08 -20 0.06 -40 0.04 -60 0.02 0 0 20 40 60 80 100 120 140 0.03 -80 -80 -60 -40 -20 0 20 40 60 80 20 0.03 20 15 15 0.025 0.025 10 10 0.02 0.02 5 0.015 5 0 0.015 0 -5 -5 0.01 0.01 -10 -10 0.005 0.005 -15 0 0 20 40 60 80 100 120 140 -20 -15 -15 -10 -5 0 5 10 15 20 0 0 20 40 60 80 100 120 140 -20 -15 -10 -5 0 5 10 15 20 Figure: Above figure shows different types of spectrum for different types of diseases www.ijsret.org 737 International Journal of Scientific Research Engineering & Technology (IJSRET), ISSN 2278 – 0882 Volume 3, Issue 4, July 2014 S.No. Table: 1.3 comparisons between different heart abnormality present in the signal Sounds Mean Std. Variance Psd Spectrogram Normal Heart Sound -7.0𝑒 −4 0.025 6.2614 0.0265 17.7388 2 Patent ductus arteriosus -8.44𝑒 −4 0.071 0.005 0.1848 83.2339 3 Aortic insufficiency -7.79𝑒 −4 0.068 0.0046 0.1609 84.1088 4 aortic stenosis -7.79𝑒 −4 0.044 0.002 0.0697 34.0595 innocent -7.52𝑒 −4 0.025 6.08E-04 0.0254 16.8661 0.029 8.32𝑒 −4 0.0276 17.6204 1 Functional or 5 murmurs venous hum 6 ebstein's disease -7.60𝑒 −4 V. CONCLUSION A conclusion drawn from the above suggest is that we have designed matlab program to facilitate physicians‟ daily work. Evaluation of patients with heart disease is a complex task where auscultation provides one piece of the puzzle. Therefore, our intelligent Method is not to be seen as a tool capable of replacing clinicians, but rather as a provider of quantitative decision support. The main usage of this will be to detect one‟s Heart disease deciding requires special care or not. The future work related to this are compensate the variation between same type of heart sounds from different resources, To collect the more databases of different heart sounds and to make a real time device for the phonocardiograph signals and To study variation of the different heart sounds in the time and frequency domain characteristics under normal and abnormal conditions REFERENCE 1. M. Fanfulla, M. Malcangi, M. Riva, D. Della Giustina, F. Belloni “Cardiac Sounds Segmentation Algorithm fornArrhythmias Detection by Fuzzy Logic” INTERNATIONAL JOURNAL OF CIRCUITS, SYSTEMS AND SIGNAL PROCESSING, Volume 5, December 4, 2010. 2. P.S.Vikhe, S.T.Hamde, and N.S.Nehe “Wavelet Transform Based Abnormality Analysis of Heart Sound” International Conference on Advances in Computing, Control, and Telecommunication Technologies, pp: 367-371, July 2009. 3. D. Balasubramaniam and D. Nedumaran “Efficient Computation of Phonocardiographic Signal Analysis in Digital Signal Processor Based System” International Journal of Computer Theory and Engineering, Vol. 2, No. 4, August, 2010 pp: 1793-8201 4. Vivek Nigam and Roland Priemer “Accessing heart dynamics to estimate durations of heart sounds” dol: 10.1088/0967-3334/26/6/010. 5. Xuan Zhang, Louis-Gilles Durand, Lotfi Senhadji, Howard C. Lee, and Jean-Louis Coatrieu “AnalysisSynthesis of the Phonocardiogram Based on the Matching Pursuit Method” inserm-00460389, Vol, 28 Feb 2010. 6. Ionela Iancu and Eugen Iancu” The analysis of the heart sounds for automated method” Department of physiology and medical informatics, university of medicine and pharmacy of Craiova. 7. Dr John N Torry, “Heart Sound Analysis comparing Wavelet and Autoregressive Techniques” Department of Engineering and DesignUniversity of SussexFalmer, Brighton, pp: 657−660,2003. 8. Carlos S. Lima, Manuel J. Cardoso, “Phonocardiogram Segmentation by using hidden markov models”, Department of Industrial Electronics of University of Minho, Campus de Gualtar, Braga, Portugal 9. Faizan Javed1, P A Venkatachalam2 and Ahmad Fadzil M H, “A Signal Processing Module for the Analysis of Heart Sounds and Heart Murmurs”, International MEMS Conference 2006,pp: 1098–1105,2006. 10. Tanveer Syeda-Mahmood, Fei Wang, “Shape-based Retrieval of Heart Sounds for Disease Similarity Detection”,IBM Almaden Research Center, 650 Harry Road, San Jose, CA 95120. www.ijsret.org 738