Automatic segmentation and detection of heart sound components S1, S2, S3 and S4

Abstract

In this paper, we propose an automatic segmentation and detection of heart sound components (S1, S2, S3 and S4) which incorporates Empirical Mode Decomposition (EMD) denoising, autocorrelation-based cardiac cycle calculation, Shannon energy envelope extraction, first derivative peak and boundary detection, and real peak selection using Heron's formula. The proposed method is evaluated on synthetic data corrupted by white Gaussian noise. The simulation results show that the proposed method is able to segment and identify the heart sound component correctly from normal and abnormal heart sound data.

Empirical Mode Decomposition (EMD) Based Denoising Method for Heart Sound Signal and Its Performance Analysis

“…However, based on our simulation, the performance of EMD denoising method under alpha-stable noise can be improved by increasing the constant value C up to 1.5. In addition, to mitigate this impulsive disturbance in heart sound analysis, an adaptive selection algorithm based on Heron's formula can be employed in the subsequent process [21].…”

Phonocardiogram Classification Based on Machine Learning with Multiple Sound Features

“…Sound-based classification of heart diseases commonly needs a heart sound signals dataset (i.e., S1, S2, S3 and S4) to split the data into heart disease categories such as normal, murmur, extrahls and artifacts (Chao et al,;Salman et al, 2015;Vepa, 2009). In this study, Phonocardiogram (i.e., PCG) is used to obtain the digital recording dataset of the heart sound (i.e., digital heart rate recording) using an electronic stethoscope or mobile device.…”

Deterministic Approach to Detect Heart Sound Irregularities

“…Shannon Energy theorem was used in the envelope extraction process of denoised signal [11,12]. Based on these envelopes heart sound components will be determined.…”