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arXiv · 2312.09445

IncepSE: Leveraging InceptionTime's performance with Squeeze and Excitation mechanism in ECG analysis

Tue Minh Cao·Nhat Hong Tran·Le Phi Nguyen·Hieu Huy Pham·Hung Thanh Nguyen
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Abstract

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.

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Tue Minh Cao, Nhat Hong Tran, Le Phi Nguyen, Hieu Huy Pham, Hung Thanh Nguyen. 2023-11-16. IncepSE: Leveraging InceptionTime's performance with Squeeze and Excitation mechanism in ECG analysis. https://arxiv.org/abs/2312.09445

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