ResLSTM-TemporalSE:an automated classification model for multi-lead ECG signals

QU Meng; FU Rong

doi:10.12122/j.issn.1673-4254.2025.12.18

Abstract：Objective We propose an efficient deep learning model to improve the classification accuracy in automatic classification tasks of 12-lead electrocardiogram(ECG)signals.Methods We designed a new ResLSTM-TemporalSE network architecture by incorporating a multi-layer Residual Long Short-Term Memory(ResLSTM)structure and introducing skip connections between LSTM layers to establish residual learning pathways for the temporal features.A temporal attention mechanism was integrated into the traditional Squeeze-and-Excitation(SE)module to enhance channel-wise feature representation while capturing long-term temporal dependencies within ECG signals,thereby an efficient hierarchical feature extraction framework was constructed.The model was validated using the public CPSC2018 dataset and a private clinical dataset from the Seventh Affiliated Hospital of Southern Medical University.Results The experimental results demonstrated that the model achieved a classification accuracy of 99.70%on the CPSC2018 test set,with precision,recall,and F1-score values of 0.9966,0.9370,and 0.9653,respectively.On the private clinical dataset,it attained an accuracy of 82.77%,with precision,recall,and F1-score values of 0.6811,0.8961,and 0.7723.Ablation studies confirmed the significant contributions of both the residual connections and the temporal attention module to model performance.Conclusion The ResLSTM-TemporalSE model effectively integrates spatiotemporal features of the ECG signals and demonstrates superior classification performance on the CPSC2018 benchmark while maintaining strong generalization capabilities in real-world clinical settings.This framework provides a robust solution for automated ECG analysis and holds significant promise for clinical applications.