Towards quantitative precision for ECG analysis: Leveraging state space
  models, self-supervision and patient metadata
Temesgen Mehari Nils Strodthoff
Deep learning has emerged as the preferred modeling approach for automatic
ECG analysis. In this study, we investigate three elements aimed at improving
the quantitative accuracy of such systems. These components consistently
enhance performance beyond the existing state-of-the-art, which is
predominantly based on convolutional models. Firstly, we explore more
expressive architectures by exploiting structured state space models (SSMs).
These models have shown promise in capturing long-term dependencies in time
series data. By incorporating SSMs into our approach, we not only achieve
better performance, but also gain insights into long-standing questions in the
field. Specifically, for standard diagnostic tasks, we find no advantage in
using higher sampling rates such as 500Hz compared to 100Hz. Similarly,
extending the input size of the model beyond 3 seconds does not lead to
significant improvements. Secondly, we demonstrate that self-supervised
learning using contrastive predictive coding can further improve the
performance of SSMs. By leveraging self-supervision, we enable the model to
learn more robust and representative features, leading to improved analysis
accuracy. Lastly, we depart from synthetic benchmarking scenarios and
incorporate basic demographic metadata alongside the ECG signal as input. This
inclusion of patient metadata departs from the conventional practice of relying
solely on the signal itself. Remarkably, this addition consistently yields
positive effects on predictive performance. We firmly believe that all three
components should be considered when developing next-generation ECG analysis
algorithms.