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Two-stage ECG signal denoising based on deep convolutional network.

Lishen Qiu1,2, Wenqiang Cai3, Miao Zhang2

  • 1School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, People's Republic of China.

Physiological Measurement
|October 29, 2021
PubMed
Summary

A novel two-stage deep learning method effectively denoises electrocardiogram (ECG) signals, removing noise while preserving crucial waveform details for improved arrhythmia detection. This approach enhances ECG analysis accuracy in clinical settings.

Keywords:
DR-netECG denoisingU de -netconvolutiontwo-stage method

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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Artificial Intelligence

Background:

  • Electrocardiograms (ECGs) are vital for non-invasive arrhythmia detection.
  • ECG signal noise contamination can lead to misinterpretations.
  • Pretreatment of ECG signals is crucial for accurate analysis.

Purpose of the Study:

  • To develop and evaluate a two-stage deep learning model for denoising and reconstructing ECG signals.
  • To improve the accuracy of ECG analysis by removing noise and correcting waveform distortion.
  • To assess the proposed method's performance against existing schemes.

Main Methods:

  • Utilized CPSC2018 ECG data and MIT-BIH Noise Stress Test Database noise signals.
  • Employed a U-net model for initial ECG signal denoising.
  • Implemented a DR-net model for ECG signal reconstruction and waveform correction.
  • Constructed both models using convolution for end-to-end mapping.

Main Results:

  • The U-net + DR-net model outperformed five other schemes in SNR, RMSE, and P metrics.
  • Achieved significant improvements: SNR increased by 11.61-14.40 dB.
  • Reduced RMSE by 10.46-21.55 × 10⁻² across datasets.

Conclusions:

  • The proposed two-stage method effectively eliminates noise while preserving essential signal details.
  • Demonstrated strong performance in denoising and reconstructing ECG signals.
  • The method shows promising clinical application prospects for enhanced ECG analysis.