Classification of heart sounds using fractional fourier transform based mel-frequency spectral coefficients and traditional classifiers

Heart sounds are a rich source of information for early diagnosis of cardiac pathologies. Distinguishing normal from abnormal heart sounds requires a specially trained clinician. Our goal is to develop a machine learning application that tackle the problem of heart sound classification. So we present a new processing and classification system for heart sounds. The automated diagnostic system is described in terms of its preprocessing, cardiac cycle segmentation, feature extraction, features reduction and classification stages. Conventional architectures will be used to identify abnormal heart sounds then the performances of the proposed systems will be compared. The conventional architectures include the following traditional classifiers: SVM, KNN and ensemble classifier (bagged Trees, subspace KNN and RUSBoosted tree). The proposed system is verified on the publicly available dataset of the heart sounds. The cross-validation and local hold out train-test methods are used to perform the experiments and obtain and compare the results. The proposed system showed potential for achieving excellent performance compared to previous methods on the same dataset with a score of 0.9200 at a sensitivity of 0.8735 and specificity of 0.9666 using a support vector machine classifier with cubic kernel. The details of the methodology and the results are presented and discussed. (C) 2019 Elsevier Ltd. All rights reserved.