Heart Sound Classification Based on Mel-Frequency Cepstrum Coefficient Features and Multi-Scale Residual Recurrent Neural Networks

Abstract

A rapid and accurate algorithm model of extracting heart sounds plays a vital role in the early detection of cardiovascular disorders, especially for small primary health care clinics. This paper proposes a heart sound extraction and classification algorithm based on static and dynamic combination of Mel-frequency cepstrum coefficient (MFCC) feature extraction and the multi-scale residual recurrent neural network (MsRes-RNN) algorithm model. The standard MFCC parameters represent the static characteristics of the signal. In contrast, the first-order and second-order MFCC parameters represent the dynamic characteristics of the signal. They are extracted and combined to form the MFCC feature representation. Then, the MFCC-based features are fed to a MsRes-RNN algorithm model for feature learning and classification tasks. The proposed classification model can take advantage of the encoded local characteristics extracted from the multi-scale residual neural network (MsResNet) and the long-term dependencies captured by recurrent neural network (RNN). Model estimation experiments and performance comparisons with other state-of-the-art algorithms are presented in this paper. Experiments indicate that a classification accuracy of 93.9% has been achieved on 2016 PhysioNet/CinC Challenge datasets.