Lightweight Deep Learning for Atrial Fibrillation Detection from Single-Lead Wearable ECG: A Benchmark of Convolutional, Temporal, and Mamba-Inspired Architectures

Atrial fibrillation (AF) detection from short single-lead electrocardiogram (ECG) recordings requires a balance between rhythm discrimination and computational efficiency. This study compares a one-dimensional ResNet-34 (1D-ResNet34), a Temporal Convolutional Network (TCN), and MambaECG, a compact Mamba-inspired gated temporal model, under the same reported training protocol on the PhysioNet Computing in Cardiology Challenge 2017 corpus. The PhysioNet dataset contains 8528 recordings. For independent dataset evaluation, the Chapman–Shaoxing database contains 45,152 recordings, of which 35,339 were used: 1780 AF records and 33,559 selected sinus-rhythm comparator records after excluding 9813 other-arrhythmia records. Models trained on PhysioNet were evaluated on Chapman Lead I without target-domain fine-tuning, forming a zero-shot evaluation on an independent but simplified AF-versus-selected-sinus task. On PhysioNet, TCN achieved the highest AUROC (0.957), F1 (0.594), sensitivity (0.883), and specificity (0.914) with 0.19 M parameters. ResNet1D34 achieved AUROC 0.955 with 35.34 M parameters, while MambaECG achieved AUROC 0.928 with 0.65 M parameters and the lowest reported GPU inference latency of 0.04 ms per recording. Chapman selected-task AUROCs were 0.948, 0.963, and 0.961 for MambaECG, ResNet1D34, and TCN, respectively. Because the PhysioNet and Chapman non-AF comparator definitions differ, these AUROCs are not treated as evidence that performance improved across domains. Grad-CAM examples are used as qualitative interpretability illustrations rather than clinical validation. The results support a controlled comparison of accuracy, parameter efficiency, and measured latency while identifying limitations in label harmonisation, padding, partition documentation, and the Mamba-inspired proxy implementation.