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Our study focuses on the potential for modifications of Inception-likearchitecture within the electrocardiogram (ECG) domain. To this end, weintroduce IncepSE, a novel network characterized by strategic architecturalincorporation that leverages the strengths of both InceptionTime and channelattention mechanisms. Furthermore, we propose a training setup that employsstabilization techniques that are aimed at tackling the formidable challengesof severe imbalance dataset PTB-XL and gradient corruption. By this means, wemanage to set a new height for deep learning model in a supervised learningmanner across the majority of tasks. Our model consistently surpassesInceptionTime by substantial margins compared to other state-of-the-arts inthis domain, noticeably 0.013 AUROC score improvement in the "all" task, whilealso mitigating the inherent dataset fluctuations during training.

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