Why the study?
Deep-learning-based ECG analysis has been dominated by convolutional architectures, leaving the potential of structured state space models to capture long-term dependencies in time series unexplored.
Structured state space models (SSMs) significantly improve deep-learning-based ECG classification performance compared to state-of-the-art convolutional architectures and can handle varying sampling rates without performance loss.
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.
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Mehari et al. (2022) studied this question.
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