KDM-Net: a large-kernel and bi-mamba integrated network for 12-lead atrial arrhythmia classification

OBJECTIVE: Automatic recognition of atrial arrhythmias is important for early clinical screening and computer-aided diagnosis. Distinguishing atrial fibrillation from atrial flutter remains challenging because of their similar electrocardiographic manifestations and high risk of misclassification. This study aimed to develop an effective model for atrial arrhythmia classification with improved multi-lead feature utilization, long-range temporal modeling, and cross-patient generalization. APPROACH: We propose KDM-Net, which takes 12-lead electrocardiogram signals as input. The network employs a large-kernel K module to enhance the perception of waveform morphology and rhythm patterns, a D module to recalibrate and fuse multi-lead features, and a bidirectional Mamba-based M module to model long-range temporal dependencies. KDM-Net was evaluated on the combined CUSPH and PTB-XL dataset under an inter-patient protocol. MAIN RESULTS: In five-fold cross-validation, KDM-Net achieved an average accuracy and F1-score of 96.04% and 89.21%, respectively. On the independent patient-level test set, the accuracy and F1-score were 96.41% and 88.31%, respectively. On the CinC 2020 external test set, the model achieved an accuracy and F1-score of 91.14% and 80.41%, respectively. KDM-Net outperformed several representative models in comparative experiments. SIGNIFICANCE: KDM-Net effectively integrates multi-lead spatial information and long-range temporal dependencies, demonstrating strong recognition performance and promising cross-patient generalization. It provides an effective solution for the intelligent diagnosis of atrial arrhythmias.