Automated Detection of Heart Valve Disorders From the PCG Signal Using Time-Frequency Magnitude and Phase Features

In this letter, we propose a method for the automated detection of heart valve disorders namely, the aortic stenosis (AS), mitral stenosis (MS), and mitral regurgitation (MR) from the phonocardiogram (PCG) signal. The wavelet synchrosqueezing transform is used to obtain the time-frequency matrix from the segmented cycles of the PCG signal. From the time-frequency matrix, the magnitude and phase features are extracted. The random forest (RF) classifier is used for the classification. The results reveal that the proposed method has the average individual accuracy (IA) values of 98.83%, 97.66%, 91.16%, and 92.83% for normal, AS, MS, and MR classes.