Our study focuses on the potential for modifications of Inception-like architecture within the electrocardiogram (ECG) domain. To this end, we introduce IncepSE, a novel network characterized by strategic architectural incorporation that leverages the strengths of both InceptionTime and channel attention mechanisms. Furthermore, we propose a training setup that employs stabilization techniques that are aimed at tackling the formidable challenges of severe imbalance dataset PTB-XL and gradient corruption. By this means, we manage to set a new height for deep learning model in a supervised learning manner across the majority of tasks. Our model consistently surpasses InceptionTime by substantial margins compared to other state-of-the-arts in this domain, noticeably 0.013 AUROC score improvement in the "all" task, while also mitigating the inherent dataset fluctuations during training.
@article{arxiv.2312.09445,
title = {IncepSE: Leveraging InceptionTime's performance with Squeeze and Excitation mechanism in ECG analysis},
author = {Tue Minh Cao and Nhat Hong Tran and Le Phi Nguyen and Hieu Huy Pham and Hung Thanh Nguyen},
journal= {arXiv preprint arXiv:2312.09445},
year = {2023}
}