Electrocardiogram Classification Method Based on Nonlinear Spiking Neural Convolutional and Echo-Like Recurrent Models

Electrocardiogram (ECG) analysis is crucial in the diagnosis of cardiovascular diseases (CVDs). With the continuous development of deep learning, significant progress has been made in ECG-based automatic diagnosis of CVDs. Currently, most studies typically use variants of CNN+RNN for feature extraction of ECG signals. Common variants of RNN mainly include LSTM and GRU, which are designed to mitigate gradient vanishing by gating mechanism, but the deep network may still lose the long-distance feature information. The fluctuation and periodic changes of ECG signals exhibit stronger nonlinear characteristics, so it is very important to construct models with stronger nonlinear expression ability for ECG classification and diagnosis. To address the above challenges, this paper proposes a new ECG classification method based on spiking neural convolutional model and echo-like recurrent model. First, convolutional spiking neural P model (ConvSNP) is utilized to design a multiscale network and a residual network to alternatively extract features from the ECG beat section. Second, a bi-directional echo-like spiking neural P system (ESNP) is designed for feature extraction of the ECG rhythmic part. Finally, the ConvSNP is utilized to perform feature fusion on the information of the lead part, and the attention mechanism is used to focus on the more important leads. This paper was evaluated on three public datasets and achieved good performance, obtaining AUC metrics of 0.9324, 0.9690 and 0.9968 respectively.