论文标题
通过结构化状态空间模型来推进ECG分析的最先进
Advancing the State-of-the-Art for ECG Analysis through Structured State Space Models
论文作者
论文摘要
基于深度学习的心电图分析的领域主要由卷积体系结构主导。这项工作探讨了应用最近引入的结构化状态空间模型（SSM）作为一种特别有前途的方法，因为它可以捕获时间序列中的长期依赖性。我们证明，这种方法可导致对当前的心电图分类的最新最新改进，我们追溯到个体病理。此外，该模型捕获长期依赖性的能力可以阐明文献中的长期问题，例如最佳采样率或窗口大小以训练分类模型。有趣的是，我们没有发现使用500Hz采样的数据，而不是100Hz，而不是将模型的输入大小扩展到3s以上而没有优势。基于这一非常有希望的首次评估，SSM可以发展为用于ECG分析的新建模范式。
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.
