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Missing RRI Interpolation Algorithm based on Locally Weighted Partial Least Squares for Precise Heart Rate

Keisuke Kamata1, Koichi Fujiwara Takafumi Kinoshita2,3,2, Manabu Kano4

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Summary
This summary is machine-generated.

A novel method accurately interpolates missing heart rate variability (HRV) data caused by electrocardiogram (ECG) artifacts. This technique significantly improves HRV analysis accuracy for better health monitoring services.

Keywords:
Just-In-Time modelingR wave detectionheart rate variability analysislocally weighted partial least squares

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Area of Science:

  • Biomedical Engineering
  • Physiological Signal Processing
  • Data Science

Background:

  • Heart rate variability (HRV) analysis, derived from R-R interval (RRI) fluctuations in ECG, reflects autonomic nervous system (ANS) activity.
  • Accurate R wave detection is essential for reliable HRV analysis, but ECG artifacts frequently cause missing R waves, compromising data integrity.
  • Existing methods for handling missing RRI data can lead to inaccuracies in HRV assessment.

Purpose of the Study:

  • To introduce a new missing R-R interval (RRI) interpolation technique for electrocardiogram (ECG) data.
  • To enhance the accuracy of heart rate variability (HRV) analysis by addressing data gaps caused by artifacts.
  • To improve the reliability of autonomic nervous system (ANS) monitoring through precise HRV assessment.

Main Methods:

  • Development of a Just-In-Time (JIT) modeling approach for RRI interpolation.
  • Implementation of Locally Weighted Partial Least Squares (LWPLS) for local regression model construction within the JIT framework.
  • Evaluation using the MIT-BIH Normal Sinus Rhythm Database with artificially introduced missing RRIs, comparing LWPLS-based RRI interpolation (LWPLS-RI) against a mean imputation method.

Main Results:

  • The proposed LWPLS-RI method demonstrated a significant improvement in root mean squared error (RMSE) of RRI, reducing it by approximately 70% compared to the conventional MEAN method.
  • The LWPLS-RI technique effectively interpolated missing RRIs, preserving the integrity of HRV data.
  • The method facilitated precise HRV analysis, outperforming traditional imputation techniques.

Conclusions:

  • The proposed LWPLS-based RRI interpolation (LWPLS-RI) technique offers a robust solution for handling missing RRI data in ECG signals.
  • This method enhances the accuracy of HRV analysis, crucial for reliable health monitoring services.
  • LWPLS-RI contributes to the advancement of precise, artifact-resilient HRV-based health monitoring systems.