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”.
Highlights
Cardiovascular system is a perpetual source of data that sanctions soothsaying or distinguishing among cardiovascular diseases
Our study shows that these classifiers (SVM and deep neural network (DNN) centroid displacement based KNN) have good performance for heart sound signal classification
In order toextracted improve the accuracy, we found out some optimum values for feature extraction with wavelets transform have greater performance and when those features such as sampling frequency and feature vector length on which the resulting accuracies were higher combination with Mel Frequency Cepstral Coefficient (MFCCs), the accuracy goes even higher [5,6,25]
Summary
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]. 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. Normal heart lower portion ofand heart, and enters blood the enters thethrough heart through atrias and exit through ventricles. 1a is an1aexample of normal heart sound generated by closing opening heart sounds is an example of normal heartsignal) soundare signal) are generated byand closing and of the valves of valves heart. The heartThe sound signals are directly opening and closing opening of the of heart
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