Abnormal Heart Sound Detection using Time-Frequency Analysis and Machine Learning Techniques

Abstract

Phonocardiogram (PCG) signals contains valuable information pertaining to heart valve functionality, rendering them potentially useful for early detection of cardiovascular diseases. Automated classification of heart sounds harbors great promise for identifying cardiac pathologies. This paper introduces a novel automated approach to classify normal and abnormal heart sounds. Our methodology involves partitioning heart sounds into four segments: S1, S2, systolic, and diastolic, followed by extraction of time–frequency and time-statistical features. Prior to data classification, we employ two techniques - particle Swarm optimization (PSO) and Sequential Forward Feature Selection (SFFS) - for efficient feature selection. We assess the performance of the proposed method on the Physionet Challenge 2016 database, utilizing the 10-fold cross-validation method. To address the issue of dataset imbalance, we apply the synthetic minority over-sampling technique (SMOTE) to create balanced datasets. Our approach surpasses existing methods in the literature, as evidenced by its superior accuracy, sensitivity, and specificity metrics. Specifically, our method achieves an accuracy of 98.03%, a sensitivity of 97.64%, and a specificity of 98.43% in distinguishing normal from abnormal heart sounds on the Physionet database. These findings outperform the results obtained by previously established methods evaluated on the Physionet 2016 challenge database.