MSCA-Mamba: A Compact Multi-Scale Mamba Network for Multi-Label ECG Classification

Objective: Multi-label electrocardiogram (ECG) classification requires modeling morphological patterns across different temporal scales and their sequential relationships while maintaining limited model complexity. This study proposes Multi-Scale Channel-Attentive Mamba (MSCA-Mamba), a compact architecture integrating multi-scale feature extraction, branch-wise recalibration, and state-space sequence modeling. Methods: MSCA-Mamba combines three parallel dilated Conv1D branches, branch-wise Squeeze-and-Excitation modules, and a unidirectional Mamba block. The model was evaluated on the PTB-XL diagnostic superclass task using the official patient-wise split and five random seeds. Component ablations, parameter-matched Conv1D, GRU, LSTM, and Transformer controls, unidirectional–bidirectional comparisons, and GPU benchmarks were performed. Results: MSCA-Mamba achieved a Macro ROC-AUC of 0.8998 ± 0.0022, Macro PR-AUC of 0.7651 ± 0.0036, and Macro F1-score of 0.7038 ± 0.0045 with 121,149 parameters. Multi-scale feature extraction provided the clearest benefit, particularly for macro-averaged metrics. Parameter-matched alternatives showed broadly comparable performance. Bidirectional Mamba achieved a modestly higher Macro ROC-AUC but increased the parameter count to 214,077 and inference latency. Conclusions: MSCA-Mamba provides a compact framework for multi-label ECG classification on PTB-XL. Its primary strength lies in compact architectural integration rather than universal superiority of individual components.