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Article

Feature–Shuffle and Multi–Head Attention–Based Autoencoder for Eliminating Electrode Motion Noise in ECG Applications

1
Department of Computer Science and Information Engineering, Chaoyang University of Technology, Taichung City, Wufeng 413310, Taiwan
2
Department of Electronic Engineering, National Yunlin University of Science and Technology, Yunlin County, Douliu 64002, Taiwan
3
Department of Automation Engineering and the Smart Machinery and Intelligent Manufacturing Research Center, National Formosa University, Yunlin County, Huwei 632301, Taiwan
4
Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, Yunlin County, Douliu 64002, Taiwan
*
Author to whom correspondence should be addressed.
Sensors 2025, 25(20), 6322; https://doi.org/10.3390/s25206322 (registering DOI)
Submission received: 20 August 2025 / Revised: 5 October 2025 / Accepted: 11 October 2025 / Published: 13 October 2025
(This article belongs to the Special Issue AI on Biomedical Signal Sensing and Processing for Health Monitoring)

Abstract

Electrocardiograms (ECGs) are critical for cardiovascular disease diagnosis, but their accuracy is often compromised by electrode motion (EM) artifacts—large, nonstationary distortions caused by patient movement and electrode–skin interface shifts. These artifacts overlap in frequency with genuine cardiac signals, rendering traditional filtering methods ineffective and increasing the risk of false alarms and misdiagnosis, particularly in wearable and ambulatory ECG applications. To address this, we propose the Feature–Shuffle Multi–Head Attention Autoencoder (FMHA–AE), a novel architecture integrating multi-head self–attention (MHSA) and a feature–shuffle mechanism to enhance ECG denoising. MHSA captures long–range temporal and spatial dependencies, while feature shuffling improves representation robustness and generalization. Experimental results show that FMHA–AE achieves an average signal–to–noise ratio (SNR) improvement of 25.34 dB and a percentage root mean square difference (PRD) of 10.29%, outperforming conventional wavelet–based and deep learning baselines. These results confirm the model’s ability to retain critical ECG morphology while effectively removing noise. FMHA–AE demonstrates strong potential for real–time ECG monitoring in mobile and clinical environments. This work contributes an efficient deep learning approach for noise–robust ECG analysis, supporting accurate cardiovascular assessment under motion–prone conditions.
Keywords: electrode motion artifacts; autoencoder; ECG denoising; multi–head self–attention; transformer electrode motion artifacts; autoencoder; ECG denoising; multi–head self–attention; transformer

Share and Cite

MDPI and ACS Style

Wang, S.-T.; Hsu, W.-Y.; Lai, S.-C.; Sheu, M.-H.; Chang, C.-Y.; Hsia, S.-C.; Wang, S.-H. Feature–Shuffle and Multi–Head Attention–Based Autoencoder for Eliminating Electrode Motion Noise in ECG Applications. Sensors 2025, 25, 6322. https://doi.org/10.3390/s25206322

AMA Style

Wang S-T, Hsu W-Y, Lai S-C, Sheu M-H, Chang C-Y, Hsia S-C, Wang S-H. Feature–Shuffle and Multi–Head Attention–Based Autoencoder for Eliminating Electrode Motion Noise in ECG Applications. Sensors. 2025; 25(20):6322. https://doi.org/10.3390/s25206322

Chicago/Turabian Style

Wang, Szu-Ting, Wen-Yen Hsu, Shin-Chi Lai, Ming-Hwa Sheu, Chuan-Yu Chang, Shih-Chang Hsia, and Szu-Hong Wang. 2025. "Feature–Shuffle and Multi–Head Attention–Based Autoencoder for Eliminating Electrode Motion Noise in ECG Applications" Sensors 25, no. 20: 6322. https://doi.org/10.3390/s25206322

APA Style

Wang, S.-T., Hsu, W.-Y., Lai, S.-C., Sheu, M.-H., Chang, C.-Y., Hsia, S.-C., & Wang, S.-H. (2025). Feature–Shuffle and Multi–Head Attention–Based Autoencoder for Eliminating Electrode Motion Noise in ECG Applications. Sensors, 25(20), 6322. https://doi.org/10.3390/s25206322

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