Classification of normal and abnormal heart sounds using new mother wavelet and support vector machines

Auscultation of heart sounds is very important to identify cardiovascular diseases (CVD). In this paper new approach for classification of heart sounds is introduced. This approach depends on getting features from heart sounds using statistical calculations of coefficients of new mother wavelet transform. Then, these features are classified using support vector machine (SVM). Number of features is 40 features. Number of heart sounds used for training is 90 heart sounds. Number of heart sounds used for tested data is 64 heart sounds. The obtained accuracy percent is 92.29%. The obtained specificity is 95.38%. The obtained sensitivity is 90%. So, this new tool can help physicians to diagnose patients of CVD easily.