2D ViT and 1D CRNN-based Heart Sound Signals Detection Model

Heart sounds reflect the function of heart valves and are important for the diagnosis of heart-related diseases. Automated heart sound diagnosis plays an key role in the early detection of cardiovascular diseases. In this paper, a new deep learning-based detection model (2D ViT-1D CRNN) for heart sound signals is designed, which combines one-dimensional time-domain and two-dimensional time-frequency domain features. In this model, a 1D CNN and a BiLSTM are combined into a 1D CRNN module to extract 1D temporal features from the original PCG signals, while 2D time-frequency features are extracted using a 2D ViT module. The classification results are calculated by softmax function after connecting the outputs of the two modules. To validate the effectiveness of the proposed 2D ViT-1D CRNN model, it is applied to two public datasets with different classification tasks. Compared with existing SOTA methods, our proposed method performs best on both datasets, which suggests that the method in this paper can be applied to assist diagnosis of cardiovascular diseases.