Unveiling the secrets of neural network scaling for ECG classification

We present a new perspective on scaling neural networks for electrocardiograms (ECG). Although ResNet-based models are widely used in ECG classification, the potential benefits of network scaling remain unexplored. Our research investigates the impact of changes in the depth of layers, the number of channels, and the dimensions of the convolution kernels on performance. Contrary to computer vision practices, we found that shallower networks, with more channels and smaller kernels, lead to better performance for ECG classifications. Based on these findings, we provide insights that can guide the efficient development of models in practice. Finally, we explore why scaling hyperparameters affects ECG and computer vision differently. Our findings suggest that the inherent periodicity of the ECG signals plays a crucial role in this difference. • We introduce an approach to scale residual networks for ECGs, overlooked until now. • Performance trends in ECGs differ from those seen in other domains. • Scaling adjustments alone can significantly boost performance. • ECG trends may arise from the repetitive nature of ECG signal patterns.