A Heart Sound Classification Algorithm Based on Bispectral Analysis and Deep Learning

Heart sound classification is an important research direction in the field of biomedicine, which is of great significance for reducing cardiovascular mortality. Based on the non-segmentation basis, this paper proposed to use the bispectral analysis method in the high-order spectrum for feature extraction, and then use the neural network with the attention block to perform classification learning to realize the abnormal detection of heart sound signals. The experiment used the Challenge 2016 dataset for training and testing, and finally gets a sensitivity of 0.9409, a specificity of 0.8450, and a comprehensive score of 0.8930. Compared with ResNet, MobileNet and other pre-training networks using transfer learning technology, the CNN-Attention architecture proposed in this paper has greatly reduced the number of layers. At the same time, the training time and the resources required for system operation are also drastically reduced. The performance of the proposed algorithm is generally better than the reference algorithms.