关键词: 心音分类, 深度卷积神经网络（DCNN）, 数据扩充

Abstract:

It is of great significance to diagnose the early pathological state of the heart by analyzing the heart sound signals. This paper presents a heart sound classification method based on Deep Convolutional Neural Network（DCNN）. Firstly, the heart sound signal is transformed into Mel feature maps with time-frequency characteristics, which are used as the input of the DCNN model. Then, the DCNN model is used to train the Mel feature maps, and the center loss function is introduced to establish the optimal deep learning model. In the testing stage, the heart sound signal is first converted into several two-dimensional Mel feature maps. Then, the feature maps are classified by the pre-trained deep learning model. Finally, the classification of heart sound signal is judged by the principle of majority voting. Due to the limited number of labeled samples, the accuracy of model is not high. In this paper, two-dimensional Mel feature maps of heart sound are randomly shielded in time domain and frequency domain in order to augment the training datasets. The experimental results show that the performance of this method is better than the state-of-the-art methods in the PASCAL heart sound datasets, which aims to classify normal, murmur and extrasystole heart sounds in the test samples.

Key words: heart sound classification, Deep Convolutional Neural Network（DCNN）, data augment