Heart sound classification plays a vital role in the early detection of cardiovascular disorders, especially for small primary health care clinics. Despite that much progress has been made for heart sound classification in recent years, most of them are based on conventional segmented features and shallow structure based classifiers. These conventional acoustic representation and classification methods may be insufficient in characterizing heart sound, and generally suffer from a degraded performance due to the complicated and changeable cardiac acoustic environment. In this paper, we propose a new heart sound classification method based on improved Mel-frequency cepstrum coefficient (MFCC) features and convolutional recurrent neural networks. The Mel-frequency cepstrums are firstly calculated without dividing the heart sound signal. A new improved feature extraction scheme based on MFCC is proposed to elaborate the dynamic characteristics among consecutive heart sound signals. Finally, the MFCC-based features are fed to a deep convolutional and recurrent neural network (CRNN) for feature learning and later classification task. The proposed deep learning framework can take advantage of the encoded local characteristics extracted from the convolutional neural network (CNN) and the long-term dependencies captured by the recurrent neural network (RNN). Comprehensive studies on the performance of different network parameters and different network connection strategies are presented in this paper. Performance comparisons with state-of-the-art algorithms are given for discussions. Experiments show that, for the two-class classification problem (pathological or non-pathological), a classification accuracy of 98% has been achieved on the 2016 PhysioNet/CinC Challenge database.

心音分类在心血管疾病的早期检测中起着至关重要的作用，特别是对于小型初级卫生保健诊所。尽管近年来心音分类取得了很大进展，但大多数都是基于传统的分割特征和基于浅层结构的分类器。这些传统的声学表示和分类方法可能不足以表征心音，并且由于复杂多变的心脏声学环境通常会导致性能下降。在本文中，我们提出了一种新的基于改进梅尔频率倒谱系数 (MFCC) 特征和卷积递归神经网络的心音分类方法。首先在不划分心音信号的情况下计算梅尔频率倒谱。提出了一种新的改进的基于 MFCC 的特征提取方案来阐述连续心音信号之间的动态特性。最后，将基于 MFCC 的特征馈送到深度卷积和递归神经网络 (CRNN) 以进行特征学习和后期分类任务。所提出的深度学习框架可以利用从卷积神经网络 (CNN) 提取的编码局部特征和循环神经网络 (RNN) 捕获的长期依赖性。本文对不同网络参数和不同网络连接策略的性能进行了综合研究。给出了与最先进算法的性能比较以供讨论。实验表明，对于二分类问题（病理性或非病理性），