Optimizing Neural Network Scale for ECG Classification
Byeong Tak Lee Yong-Yeon Jo Joon-Myoung Kwon
We study scaling convolutional neural networks (CNNs), specifically targeting
Residual neural networks (ResNet), for analyzing electrocardiograms (ECGs).
Although ECG signals are time-series data, CNN-based models have been shown to
outperform other neural networks with different architectures in ECG analysis.
However, most previous studies in ECG analysis have overlooked the importance
of network scaling optimization, which significantly improves performance. We
explored and demonstrated an efficient approach to scale ResNet by examining
the effects of crucial parameters, including layer depth, the number of
channels, and the convolution kernel size. Through extensive experiments, we
found that a shallower network, a larger number of channels, and smaller kernel
sizes result in better performance for ECG classifications. The optimal network
scale might differ depending on the target task, but our findings provide
insight into obtaining more efficient and accurate models with fewer computing
resources or less time. In practice, we demonstrate that a narrower search
space based on our findings leads to higher performance.