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.

翻译：深度学习已成为自动心电图分析的首选建模方法。本研究探讨了三个旨在提升此类系统定量精度的要素，这些组件持续地将性能提升至超越当前以卷积模型为主导的最先进水平。首先，我们通过利用结构化状态空间模型（SSMs）探索更具表达能力的架构。这类模型在捕捉时间序列数据中的长期依赖关系方面已展现出潜力。将SSMs融入我们的方法不仅提升了性能，还为领域内长期存在的问题提供了洞见：对于标准诊断任务，我们发现采用500Hz等高采样率相比100Hz并无优势；同样，将模型输入时长扩展至3秒以上也未带来显著改进。其次，我们证明利用对比预测编码的自监督学习可进一步提升SSMs的性能。通过自监督机制，模型能够学习更稳健且具代表性的特征，从而提升分析精度。最后，我们突破合成基准测试场景，将基础人口统计学元数据与心电图信号共同作为输入。这种纳入患者元数据的方式颠覆了传统仅依赖信号本身的实践。值得注意的是，这种补充对预测性能持续产生积极影响。我们坚信，下一代心电图分析算法的开发应全面考量这三个组件。