WCFormer: An interpretable deep learning framework for heart sound signal analysis and automated diagnosis of cardiovascular diseases

In recent years, there has been a surge focusing on advanced heart sound (HS) signal analysis and automated diagnosis based on deep learning (DL) for cardiovascular diseases (CVDs). However, the untrust of users in decision-making caused by the complex nonlinear transformation within the model and unclear feature extraction mechanism remains a huge challenge. For the diagnosis issue of CVDs involving human life and health, if the reasons why the model obtains the final conclusion cannot be known in advance, taking actions rashly will conceal significant risks. In this paper, an interpretable wavelet convolution transformer, named WCFormer, is proposed for HS signal analysis and automated diagnosis of CVDs. This method aims to enhance the interpretability of the traditional transformer and realize the high-accuracy diagnosis of CVDs by embedding wavelet knowledge information and improving its structure. Specifically, a wavelet convolution kernel is first designed to capture disease-related information with a clear physical meaning. Then, a global–local feature extractor is designed by removing the position encoding of the transformer and combining it with the convolution module. Two case studies involving HS signals are implemented to validate the efficacy of the proposed WCFormer and the results are compared with several widely used approaches, revealing that the WCFormer can achieve more excellent performance than other comparison methods.