深度卷积神经网络在心音分类方法中的应用

通过分析心音信号对心脏早期的病理状态进行确诊具有重要的意义.提出了一种基于深度卷积神经网络的心音分类方法.将心音信号转化成具有时频特性的梅尔频谱系数(Mel Frequency Spectral Coefficient,MFSC)特征图,将其作为深度卷积神经网络模型的输入;利用深度卷积神经网络对MFSC特征图进行训练,...

Deep Convolutional Neural Networks for Heart Sound Classification

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.