A 1D convolutional neural network using deep features extracted by a denoising autoencoder achieved 99.01% accuracy in classifying normal and abnormal heart sounds.
Abstract 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%.
Li et al. (Sun,) studied this question.