Machine Intelligence on the Edge: Interpretable Cardiac Pattern Localisation Using Reinforcement Learning
This work addresses the need for reliable and interpretable clinical decision support on edge devices, though it is incremental as it builds upon matched filters with a novel sequential approach.
The paper tackled the problem of localizing signal patterns in low SNR environments, such as ear-ECG, by proposing the Sequential Matched Filter (SMF) using Reinforcement Learning, achieving state-of-the-art performance in R-peak detection and physiological state classification on real-world datasets.
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.