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Classification of Heart Sound Signal Using Multiple Features

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Classification of Heart Sound Signal Using Multiple Features applied sciences Article Classification of Heart Sound Signal Using Multiple Features Yaseen, Gui-Young Son and Soonil Kwon * Department of Digital Contents, Sejong University, Seoul 05006, Korea; [email protected] (Y.); [email protected] (G.-Y.S.) * Correspondence: [email protected]; Tel.: +82-2-3408-3847 Received: 10 October 2018; Accepted: 20 November 2018; Published: 22 November 2018 Abstract: Cardiac disorders are critical and must be diagnosed in the early stage using routine auscultation examination with high precision. Cardiac auscultation is a technique to analyze and listen to heart sound using electronic stethoscope, an electronic stethoscope is a device which provides the digital recording of the heart sound called phonocardiogram (PCG). This PCG signal carries useful information about the functionality and status of the heart and hence several signal processing and machine learning technique can be applied to study and diagnose heart disorders. Based on PCG signal, the heart sound signal can be classified to two main categories i.e., normal and abnormal categories. We have created database of 5 categories of heart sound signal (PCG signals) from various sources which contains one normal and 4 are abnormal categories. This study proposes an improved, automatic classification algorithm for cardiac disorder by heart sound signal. We extract features from phonocardiogram signal and then process those features using machine learning techniques for classification. In features extraction, we have used Mel Frequency Cepstral Coefficient (MFCCs) and Discrete Wavelets Transform (DWT) features from the heart sound signal, and for learning and classification we have used support vector machine (SVM), deep neural network (DNN) and centroid displacement based k nearest neighbor. To improve the results and classification accuracy, we have combined MFCCs and DWT features for training and classification using SVM and DWT. From our experiments it has been clear that results can be greatly improved when Mel Frequency Cepstral Coefficient and Discrete Wavelets Transform features are fused together and used for classification via support vector machine, deep neural network and k-neareast neighbor(KNN). The methodology discussed in this paper can be used to diagnose heart disorders in patients up to 97% accuracy. The code and dataset can be accessed at “https://github.com/yaseen21khan/Classification-of-Heart- Sound-Signal-Using-Multiple-Features-/blob/master/README.md”. Keywords: heart sound signal classification; discrete wavelets transform; mel frequency cepstral coefficient 1. Introduction Cardiovascular system is a perpetual source of data that sanctions soothsaying or distinguishing among cardiovascular diseases. External constrains can lead to cardiac diseases that can cause sudden heart failure [1]. Cardiovascular diseases are censorious and must be detected with no time delay [2]. Heart diseases may be identified by elucidating the cardiac sound data. The heart sound signal characteristics may vary with respect to different kinds of heart diseases. A huge difference in the pattern can be found between a normal heart sound signal and abnormal heart sound signal as their PCG signal varies from each other with respect to time, amplitude, intensity, homogeneity, spectral content, etc [3]. Appl. Sci. 2018, 8, 2344; doi:10.3390/app8122344 www.mdpi.com/journal/applsci Appl. Sci. 2018, 8, 2344 2 of 14 Appl. Sci. 2018, 8, x FOR PEER REVIEW 2 of 14 CardiovascularCardiovascular auscultationauscultation isis thethe principalprincipal yetyet simplesimple diagnosingdiagnosing methodmethod usedused toto assessassess andand analyzeanalyze thethe operationoperation andand functionalityfunctionality ofof thethe heart.heart. ItIt isis aa techniquetechnique ofof listeninglistening toto heartheart soundsound withwith stethoscope.stethoscope. TheThe mainmain sourcesource ofof thethe generationgeneration ofof heartheart soundsound isis duedue toto unsteadyunsteady momentmoment ofof bloodblood calledcalled bloodblood turbulence.turbulence. The openingopening andand closingclosing ofof atrioventricularatrioventricular values,values, mitralmitral andand tricuspidtricuspid valuesvalues causescauses differentialdifferential bloodblood pressurepressure and and high high acceleration acceleration and and retardation retardation of of blood blood flow flow[4]. [4]. TheThe auscultationauscultation ofof cardiaccardiac disordersdisorders isis carriedcarried outout usingusing anan electronicelectronic stethoscopestethoscope whichwhich isis indeedindeed costcost effectiveeffective and and non-invasive non-invasive approach approach [3]. [3]. The The digital digital recording recording of heart of sound heart withsound the with help ofthe electronic help of stethoscopeelectronic stethoscope is called PCG is called [4]. PCG [4]. OnceOnce thethe heartheart soundsound isis obtained,obtained, itit cancan bebe classifiedclassified viavia computercomputer aidedaided softwaresoftware techniques,techniques, thesethese techniquestechniques needneed moremore accuratelyaccurately defineddefined heartheart soundsound cyclecycle forfor featurefeature extractionextraction process [[3].3]. ManyMany differentdifferent automated automated classifying classifying approaches approaches have have been used,been includingused, including artificial artificial neural network neural (ANN)network and (ANN) hidden and Markov hidden model Markov (HMM). model Multilayer (HMM). Multilayer perceptron-back perceptron-back propagation propagation (MLP-BP), Wavelet(MLP-BP), coefficients Wavelet andcoefficients neural networks and neural were networks used for were classification used for of classification heart sound signal.of heart But sound they havesignal. often But resultedthey have in often lower resulted accuracy in due lowe tor segmentationaccuracy due errorto segmentation [2]. error [2]. ToTo achieveachieve better better accuracy, accuracy, in in this this work, work, we we propose propose SVM, SVM, centroid centroid displacement displacement based based KNN KNN and DNNand DNN based based classification classification algorithm algorithm using discreteusing di waveletsscrete wavelets transform transform (DWT) and(DWT) (MFCC) and features.(MFCC) Thefeatures. recognition The recognition accuracy canaccuracy be increased can be increased for both noisyfor both and noisy clear and speech clear signals, speechusing signals, discrete using waveletsdiscrete wavelets transform transform fused together fused together with MFCCs with MFCCs features features [5,6]. We[5,6 extracted]. We extracted MFCCs MFCCs features features and discreteand discrete wavelets wavelets transform transform features features for heart for sound heart signalssound (normalsignals and(normal abnormal) and abnormal) and classified and themclassified using them support using vector support machine, vector deep machine, neural deep network neural and network centroid and displacement centroid displacement based KNN, thebased highest KNN, accuracy the highest we achievedaccuracy sowe far achieved is 97.9%. so far is 97.9%. TheThe beginningbeginning ofof thisthis paperpaper describesdescribes somesome backgroundbackground knowledgeknowledge aboutabout heartheart soundsound signalsignal processingprocessing andand categoriescategories ofof heartheart disordersdisorders inin briefbrief detail,detail, afterwardsafterwards thethe methodologymethodology ofof featuresfeatures (MFCCs(MFCCs andand DWT)DWT) andand classifiersclassifiers wewe usedused isis discussed.discussed. InIn thethe experimentexperiment sectionsection wewe talktalk aboutabout classificationclassification ofof features,features, detaildetail aboutabout classifiers,classifiers, thethe toolstools wewe used,used, andand databasedatabase inin detail.detail. InIn thethe end, wewe havehave discussed discussed our our results results and and performance performance evaluation evaluation using using accuracy accuracy and and averaged averaged F1 score F1 score and theand last the paragraph last paragraph concludes concludes the paper. the paper. 2.2. BackgroundBackground TheThe humanhuman heart heart is is comprised comprised of of four four chambers. chambers. Two Two of themof them are are called called atrias atrias and and they they make make the upperthe upper portion portion of the of heart the heart while while remaining remaining two chambers two chambers are called are called ventricles ventricles and they and make they lowermake portionlower portion of heart, of andheart, blood and entersblood theenters heart the through heart through atrias andatrias exit and through exit through ventricles. ventricles. Normal Normal heart soundsheart sounds (Figure (Figure1a is an 1a example is an example of normal of heartnormal sound heart signal) sound are signal) generated are generated by closing by and closing opening and ofopening the valves of the of valves heart. Theof heart. heart The sound heart signals sound produced, signals produced, are directly are relateddirectly to related opening to opening and closing and ofclosing the valves, of the blood valves, flow blood and flow viscosity. and viscosity. Therefore Th duringerefore exercise during or exercise other activities or other whichactivities increases which theincreases heart rate,the heart blood rate, flow blood through flow the through valves isthe increased valves is and increased hence heartand hence sound heart signal’s sound intensity signal’s is increasedintensity is and increased during and shock during (low shock blood
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