CSD-AFNet: Computationally Efficient Atrial Fibrillation Classification from ECGs using 2D Causal Strided Dilated Convolutions

Abstract

Automated analysis of electrocardiogram (ECG) signals using deep learning (DL) methods has shown substantial promise in atrial fibrillation (AFib) classification, particularly for detecting subtle indicators during normal sinus rhythm and for predicting new-onset AFib. However, many existing state-of-the-art models exhibit high computational demands, characterised by large parameter and floating-point operations (FLOPs) counts. This presents a high barrier to entry for training in budget-limited institutes and hinders the models’ deployment on medical edge devices. This paper introduces CSD-AFNet, a computationally efficient DL model specifically designed for AFib-related ECG classification tasks. CSD-AFNet achieves substantial reductions in both parameter and FLOPs counts by replacing expensive temporal convolutions with novel Feature-Preserving Pooled Convolutions (FPP-Convs). FPP-Convs enable the combination of striding and dilation without input feature loss, preserving temporal coverage while reducing the computational cost. The model further incorporates two-dimensional causal padding to prevent temporal leakage in downstream representations. Evaluation on the public CODE-15% and PTB-XL datasets demonstrates that CSD-AFNet matches the classification performance of leading benchmark models while reducing parameter count by a factor of 71 and FLOPs by a factor of 122 compared to the ResNet-10 inspired baseline. These findings support the suitability of CSD-AFNet for practical clinical scenarios, enabling training under resource constraints and efficient inference on medical edge devices, thereby facilitating scalable and cost-effective ECG-based AFib screening and monitoring.