We proposed a one-dimensional convolutional neural network (CNN) model, which divides heart sound signals into normal and abnormal directly independent of ECG. The deep features of heart sounds were extracted by the denoising autoencoder (DAE) algorithm as the input feature of 1D CNN. The experimental results showed that the model using deep features has stronger anti-interference ability than using mel-frequency cepstral coefficients, and the proposed 1D CNN model has higher classification accuracy precision, higher F-score, and better classification ability than backpropagation neural network (BP) model. In addition, the improved 1D CNN has a classification accuracy rate of 99.01%.

我们提出了一种一维卷积神经网络（CNN）模型，该模型将心音信号直接独立于ECG分为正常信号和异常信号。通过降噪自动编码器（DAE）算法提取心音的深层特征作为1D CNN的输入特征。实验结果表明，与反向传播神经网络相比，具有较深特征的模型具有比使用mel-频率倒谱系数更强的抗干扰能力，并且所提出的一维CNN模型具有更高的分类精度，更高的F分数和更好的分类能力。 BP）模型。另外，改进的一维CNN具有99.01％的分类准确率。