Benchmarks of ResNet Architecture for Atrial Fibrillation Classification

TL;DR

This paper evaluates various ResNet architectures for atrial fibrillation classification, revealing that model size significantly influences performance and that certain configuration parameters consistently yield better results.

Contribution

It systematically benchmarks different ResNet configurations for atrial fibrillation detection, highlighting the importance of specific layout parameters and model size.

Findings

Model size correlates strongly with performance.

Certain ResNet configurations outperform others.

Parameter layout choices consistently improve results.

Abstract

In this work we apply variations of ResNet architecture to the task of atrial fibrillation classification. Variations differ in number of filter after first convolution, ResNet block layout, number of filters in block convolutions and number of ResNet blocks between downsampling operations. We have found a range of model size in which models with quite different configurations show similar performance. It is likely that overall number of parameters plays dominant role in model performance. However, configuration parameters like layout have values that constantly lead to better results, which allows to suggest that these parameters should be defined and fixed in the first place, while others may be varied in a reasonable range to satisfy any existing constraints.