CLASSIFICATON OF HEART SOUNDS AND MURMUR USING WAVELET PACKET TRANSFORM

Abstract

The heart is one of two organs which are crucial for human life. Therefore, a disorder of the heart is of great importance to human health. Cardiovascular diseases continue to be the leading cause of morbidity and mortality worldwide. One of the first steps in evaluating the cardiovascular system after detailed history taking is physical examination. Auscultation with a stethoscope is regarded as one of the pioneer methods used in the diagnosis of heart diseases. However, the fact that auscultation using a stethoscope majorly depends on the skills of a physician‘s auscultation or his/her experience, may lead to some problems in diagnosing heart problems. Due to these difficulties, it is observed that the auscultation method has not been very successful in determining heart diseases. Despite significant interobserver variability, cardiac auscultation provides important initial clues in patient evaluation and serves as a guide for further diagnostic testing. Therefore, the use of an artificial intelligence method in the diagnosis of heart sounds may help the physicians in a clinical environment. In this study, an automatic technique is devised for the classification of heart sounds. The study was carried on a total 120 heart sounds, and the sounds were examined in two groups: normal heart sounds, and murmured heart sounds. The study consisted of three stages. In the first stage the heart sound signals are separated into sub-bands by using wavelet packet transform. In the second stage, which is the feature extraction stage, the entropy and energy of each sub-band is calculated and then PCA, is used to reduce the 8 dimensionality of the feature vectors. The feature extraction stage is one of the most significant stage affecting the performance of the classification. In the third stage, the stage of classification is carried out using the KNN and SVM. The two types of heart sound signals are used as an input of the K Nearest Neighbor and support vector machine and the sounds are classified into two groups: normal sounds and murmur sounds. In the method used, 80% of the classification performance by KNN and 70% of the classification performance by SVM is obtained