MURFormer improves heart rate signal quality by reducing noise
MURFormer: Attention-Based Lightweight Multi-Scale Uncertainty-Guided Refinement Framework for PPG Denoising
Artificial IntelligenceMachine Learning
Summary
Photoplethysmography (PPG) measures heart signals easily but can get messy when people move, making the data hard to use. The authors propose MURFormer, a lightweight computer method that cleans noisy PPG signals by focusing on uncertain parts and refining them twice. They tested MURFormer on two datasets with real noisy and clean heart signal pairs and found it greatly improved signal clarity and accuracy in measuring heart rates. It also works well on different time lengths of data and keeps important heartbeat details intact.
What this means in practice
- •For wearable device engineers: Integrate MURFormer to improve heart rate accuracy in fitness and health monitors affected by user movement.
- •For healthcare monitoring teams: Use MURFormer to clean PPG signals collected during daily activities for better patient vital sign analysis in clinical and remote settings.
Authors
Haram Kwon, Chaeeun Hwang, Seayul Choi, Sungho Jo
Abstract
Photoplethysmography (PPG) is a widely used physiological signal for its convenience but is highly susceptible to motion artifacts that degrade waveform quality and limit downstream analysis. This paper proposes MURFormer, a lightweight multi-scale uncertainty-guided refinement framework for PPG denoising through noisy-to-clean PPG mapping. MURFormer employs a newly proposed Multi-Scale Attention-Conv Fusion (MACF) block, which is designed to efficiently capture multi-scale temporal dynamics of PPG, as the fundamental building block and performs uncertainty-guided two-pass refinement prioritizing unreliable regions. The method was evaluated on the publicly available CIME dataset and on a Daily Activities dataset collected in this work, comprising paired noisy-clean PPG recordings from 24 subjects. Experimental results show that MURFormer improves both signal quality and physiological fidelity over the raw noisy input, yielding 129\% and 105\% relative signal-to-noise ratio (SNR) improvements, and 66\% and 81\% relative reductions in heart-rate mean absolute error (MAE), on the Daily Activities and CIME datasets, respectively. MURFormer also outperforms an existing denoising baseline on both datasets with a relatively small parameter count while preserving beat-level timing and waveform morphology. In addition, the model maintains strong denoising performance across varying input window lengths, suggesting that MURFormer is an effective and robust framework for practical PPG denoising. The implementation of MURFormer is publicly available at https://github.com/k-hram/MURFormer.git