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

Atrial fibrillation (AF) affects more than 37 million people worldwide and substantially raises the risk of stroke, heart failure, and premature death. Wearable single-lead ECG devices offer a natural platform for continuous AF surveillance, yet automated detection from short, noisy recordings remains an unsolved clinical engineering problem. This paper presents a sys tematic benchmark of three deep learning architectures—a one dimensional ResNet-34 (1D-ResNet34), a Temporal Convolutional Network (TCN), and a novel State-Space Model (MambaECG)— evaluated under identical training protocols on two indepen dent public datasets: the PhysioNet Computing in Cardiology Challenge 2017 corpus (8,528 recordings) and the Chapman Shaoxing 12-lead ECG database (45,152 recordings, Lead I used). All models were trained exclusively on single-lead PhysioNet recordings and tested on Chapman–Shaoxing Lead I without fine-tuning, constituting the first cross-institutional generalisation study for this architectural family. The TCN achieves the highest AUROC of 0.957 with only 0.19M parameters, outperforming 1D-ResNet34 (AUROC 0.955, 35.34M parameters) and MambaECG (AUROC 0.928, 0.65M parameters). Notably, all three architectures demonstrate positive zero-shot transfer to Chapman–Shaoxing, with AUROCs of 0.961, 0.963, and 0.948 respectively—without any target-domain exposure during training. MambaECG achieves the fastest in ference at 0.04 ms per recording, enabling real-time deployment on resource-constrained wearable hardware. Gradient-weighted Class Activation Mapping (Grad-CAM) analysis confirms that MambaECG correctly localises clinically meaningful ECG fea tures: QRS complexes and P-waves for normal rhythm, irregular RR intervals and fibrillatory baseline for AF, and premature ventricular complex morphology for Other rhythm. These results provide evidence-based architectural guidance for wearable AF screening systems and demonstrate that lightweight temporal models match or exceed deep residual networks at a fraction of the computational cost.