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Deep learning for suppressing EMG and motion artifacts in armband ECG R-peak detection.

Jaechan Lim1, Shirin Hajeb1, Youngsun Kong1

  • 1Department of Biomedical Engineering, University of Connecticut, 352 Mansfield Rd, Storrs, CT 06269 USA.

Biomedical Engineering Letters
|January 26, 2026
PubMed
Summary
This summary is machine-generated.

This study presents a new deep learning method to improve R-peak detection in wearable ECG devices, significantly reducing errors caused by motion and muscle noise for better heart rate monitoring.

Keywords:
Artifact suppressionCardiac monitoringDeep learningEncoder–decoder networksSignal processingWearable technology

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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Artificial Intelligence

Background:

  • Wearable ECG devices struggle with electromyogram (EMG) and motion artifacts.
  • Accurate R-peak identification is crucial for continuous cardiac monitoring.

Purpose of the Study:

  • Introduce a novel deep learning framework (CNNAED) for artifact-robust R-peak detection.
  • Enhance signal quality in armband ECG during daily activities.

Main Methods:

  • Developed a Convolutional Neural Network with an Encoder-Decoder architecture (CNNAED).
  • Utilized time-frequency spectrograms of ECG segments as input.
  • Trained the network to enhance R-peaks and suppress artifacts.
  • Validated using subject-independent testing on daytime recordings.

Main Results:

  • Achieved a 29.3% improvement in heart rate estimation accuracy (MAE reduced from 13.26 to 9.37 bpm).
  • Showed a 6.1% improvement in RMSSD, preserving heart rate variability metrics.
  • Demonstrated statistically significant performance gains (p < 0.001).

Conclusions:

  • The CNNAED framework offers a robust solution for EMG artifact mitigation in wearable cardiac monitoring.
  • Improved signal quality and data availability during ambulatory conditions.
  • Enables more reliable continuous cardiac monitoring.