A novel hybrid energy fraction and entropy-based approach for systolic heart murmurs identification

This paper presents a set of novel features of heart sound for the detection of the abnormality of heart sounds and classification of heart murmurs. The features include energy fraction of the first and the second heart sounds (S1–S2EF), energy fraction of heart murmur (HMEF), the maximum energy fraction of heart sound frequency sub-band (HSEFmax), sample entropy of the first and the second heart sounds component (S1–S2sampen) and sample entropy of heart murmur component (HMsampen). Firstly, the heart sound signals were de-noised and normalized, then decomposed by wavelet packet. The features, such as energy fraction and sample entropy were calculated from the reconstructed selective frequency components of heart sound signals. The support vector machine (SVM) was employed as a classifier to detect the abnormality of heart sound and discriminate heart murmurs. A dataset consisting of 80 normal heart sounds and 167 systolic heart murmurs samples, segmented from 40 healthy volunteers and 67 patients, were used to test and validate the proposed method. The performance of our proposed method was assessed in terms of sensitivity, specificity and accuracy. The result showed that our proposed method exhibited a satisfactory performance with a high accuracy of 97.17%, a specificity of over 98.55% and a sensitivity of over 93.48%. This suggests that the presented method can be used as an effective assistance for cardiac auscultation.