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The field of deep-learning-based ECG analysis has been largely dominated byconvolutional architectures. This work explores the prospects of applying therecently introduced structured state space models (SSMs) as a particularlypromising approach due to its ability to capture long-term dependencies in timeseries. We demonstrate that this approach leads to significant improvementsover the current state-of-the-art for ECG classification, which we trace backto individual pathologies. Furthermore, the model's ability to capturelong-term dependencies allows to shed light on long-standing questions in theliterature such as the optimal sampling rate or window size to trainclassification models. Interestingly, we find no evidence for using datasampled at 500Hz as opposed to 100Hz and no advantages from extending themodel's input size beyond 3s. Based on this very promising first assessment,SSMs could develop into a new modeling paradigm for ECG analysis.

Advancing the State-of-the-Art for ECG Analysis through Structured State Space Models