Heart Sound Classification for Murmur Abnormality Detection Using an Ensemble Approach Based on Traditional Classifiers and Feature Sets

Abstract

Abstract— Phonocardiography (PCG) is a method based on examination of mechanical sounds coming from heart during its regular contraction/relaxation activities such as opening and closing of the valves and blood turbulence towards vessels and heart chambers. The heart sounds in some pathological cases contains a noise called as heart murmurs. Thanks to auscultation and investigation of the heart sounds, many cardiac disorders can be a preliminarily diagnosed. Today there are high technology tools to record those sounds in electronic environment and enable us to analyze them in detail. The constraints such as human’s limited audible range, environment noise and inexperience of physicians can be overcome by the use of those tools and development of state-of-art signal processing and machine learning methods. There are possible benefits of those analyses ranging from its use at home-care units to rural areas where it is difficult to consult experienced physicians. In this study we examined heart sounds and classified them as normal or abnormal. Features of heart sounds are extracted by using Discrete Wavelet Transform (DWT), Mel-Frequency Cepstral Coefficients (MFCC) and time-domain morphological characteristics of the signals. Those features are used to form three separate feature vectors. K-Nearest Neighbor (kNN), Support Vector Machine (SVM), Multilayer Perceptron (MLP) classifiers and their ensembles are used for classification. Then the ensemble classifiers’ predictions based on distinct feature vectors are combined and an ensemble classifier built from team of ensemble classifiers. Classification performances of singular classifiers, single level ensemble classifiers and final ensemble classifier are compared and better results are obtained by the proposed method.