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

arXiv: https://arxiv.org/abs/2508.21652 · HTML: https://mlsanity.com/paper/2508.21652/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 46 references that are in the map.
- First posted on arXiv 29 Aug 2025.

## Builds on (most cited first, 9 of 9)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [High-Dimensional Continuous Control Using Generalized Advantage Estimation](https://mlsanity.com/paper/1506.02438/) (2015)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Robust R-Peak Detection in Low-Quality Holter ECGs using 1D Convolutional Neural Network](https://mlsanity.com/paper/2101.01666/) (2020)
- [Target Search and Navigation in Heterogeneous Robot Systems with Deep Reinforcement Learning](https://mlsanity.com/paper/2308.00331/) (2023)

## Built on by (most cited first, 2 of 2)

- [The Deep-Match Framework for Event-Related Potential Detection in EEG](https://mlsanity.com/paper/2603.20258/) (2026)
- [Enabling Unsupervised Training of Deep EEG Denoisers With Intelligent Partitioning](https://mlsanity.com/paper/2605.06724/) (2026)

## Abstract (arXiv)

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.