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A two-step pre-processing tool to remove Gaussian and ectopic noise for heart rate variability analysis.

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This study introduces a novel two-step denoising method to improve electrocardiogram (ECG) signal quality for heart rate variability (HRV) analysis. The approach effectively removes technical and physiological artifacts, enhancing HRV measure accuracy.

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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Cardiology

Background:

  • Electrocardiogram (ECG) signal quality is crucial for accurate heart rate variability (HRV) analysis.
  • Technical and physiological artifacts commonly degrade ECG recordings, impacting HRV measures.
  • Existing preprocessing methods often overlook technical artifacts, limiting their effectiveness.

Purpose of the Study:

  • To introduce a two-step preprocessing approach for denoising ECG signals for HRV analysis.
  • To investigate the impact of technical and physiological artifacts on commonly used HRV measures.
  • To evaluate the performance of the proposed denoising method.

Main Methods:

  • A two-step denoising strategy was developed, addressing technical and physiological artifacts separately.
  • Technical artifacts were removed using complete ensemble empirical mode decomposition with adaptive noise.
  • Physiological artifacts were eliminated using a combination filter of single dependent rank order mean and adaptive filtering.

Main Results:

  • The proposed method demonstrated high performance with a correlation coefficient of 0.846 and RMSE of 7.69 × 10-5 for added noise.
  • Most studied HRV measures were significantly affected by both technical and physiological noise.
  • Frequency domain measures (Total power, HF, LF power) and fragmentation measures (PAS, PIP, PSS) were most sensitive to noise.

Conclusions:

  • The developed two-step denoising approach effectively removes technical and physiological artifacts from ECG signals.
  • Artifacts significantly impact most HRV measures, particularly frequency and fragmentation metrics.
  • This method enhances ECG signal quality, leading to more reliable HRV analysis.