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