  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.
