用强化学习设计可解释的心电波形定位滤波器，提升边缘设备上噪声信号的检测精度。
Machine Intelligence on the Edge: Interpretable Cardiac Pattern Localisation Using Reinforcement Learning
- 通过强化学习生成多阶段可解释滤波序列，替代传统单滤波器。
- 在两个真实心电数据集上实现顶尖的R波检测与生理状态分类效果。
- 适合需要高可靠性与可解释性的医疗边缘计算场景。
匹配滤波器因高效且可解释而广泛用于信号模式定位，但在低信噪比（SNR）信号中性能下降，例如在边缘设备上记录的耳部心电图（ear-ECG），其心脏信号衰减严重并被显著伪影污染。为此，我们提出序列匹配滤波器（SMF），将传统单个匹配滤波器替换为由强化学习代理设计的滤波序列。通过将滤波器设计建模为序贯决策过程，SMF 能自适应生成针对特定信号的滤波序列，并保持完全可解释性，揭示驱动决策的关键模式。该框架在两个具有挑战性的现实世界心电图数据集上表现出色，实现了顶尖的R波检测与生理状态分类性能，具备可靠且可解释的临床决策支持潜力。该方法还可推广至其他需从噪声信号中精准定位模式的应用场景。
原文摘要 · Abstract (English)
Matched filters are widely used to localise signal patterns due to their high efficiency and interpretability. However, their effectiveness deteriorates for low signal-to-noise ratio (SNR) signals, such as those recorded on edge devices, where prominent noise patterns can closely resemble the target within the limited length of the filter. One example is the ear-electrocardiogram (ear-ECG), where the cardiac signal is attenuated and heavily corrupted by artefacts. To address this, we propose the Sequential Matched Filter (SMF), a paradigm that replaces the conventional single matched filter with a sequence of filters designed by a Reinforcement Learning agent. By formulating filter design as a sequential decision-making process, SMF adaptively design signal-specific filter sequences that remain fully interpretable by revealing key patterns driving the decision-making. The proposed SMF framework has strong potential for reliable and interpretable clinical decision support, as demonstrated by its state-of-the-art R-peak detection and physiological state classification performance on two challenging real-world ECG datasets. The proposed formulation can also be extended to a broad range of applications that require accurate pattern localisation from noise-corrupted signals.
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