# Machine Intelligence on the Edge: Interpretable Cardiac Pattern Localisation Using Reinforcement Learning

## TL;DR

This paper introduces the Sequential Matched Filter (SMF), a reinforcement learning-based approach for interpretable, adaptive pattern localization in noisy signals like ear-ECG, achieving state-of-the-art results in cardiac signal analysis.

## Contribution

The paper proposes a novel reinforcement learning framework for designing sequential, interpretable matched filters that improve pattern localization in noisy, low-SNR signals.

## Key findings

- State-of-the-art R-peak detection accuracy
- Effective physiological state classification
- Robust performance on real-world ECG datasets

## Abstract

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.