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
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.