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An automated method for sleep apnoea detection using HRV.

Mohammad Karimi Moridani1

  • 1Department of Biomedical Engineering, Faculty of Health, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran.

Journal of Medical Engineering & Technology
|January 21, 2022
PubMed
Summary

This study predicts respiratory apnoea events using ECG-derived HRV analysis. Early detection of sleep apnoea through advanced signal processing can prevent severe health risks.

Keywords:
Heart rate variabilitySVMgenetic algorithmobstructive sleep apnoeatime and frequency features

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Area of Science:

  • Biomedical Engineering
  • Cardiology
  • Sleep Medicine

Background:

  • Sleep apnoea poses significant health risks, necessitating early and accurate diagnostic methods.
  • Electrocardiogram (ECG) signals offer a non-invasive source for analyzing physiological changes during sleep.
  • Heart Rate Variability (HRV) analysis derived from ECG can reveal autonomic nervous system dysregulation associated with apnoea events.

Purpose of the Study:

  • To develop and validate a method for predicting the occurrence of respiratory apnoea events during sleep.
  • To identify distinct HRV features indicative of impending apnoea episodes.
  • To enhance the diagnostic accuracy of sleep apnoea detection using ECG signal processing.

Main Methods:

  • Utilized ECG signals from 70 patients with sleep apnoea from the Physionet database.
  • Generated HRV signals from ECG data and extracted time and frequency domain features.
  • Employed statistical analysis, Principal Component Analysis (PCA), and a genetic algorithm for optimal feature selection.
  • Evaluated feature performance individually and in combination for distinguishing between apnoea and non-apnoea intervals.

Main Results:

  • Identified specific HRV features that effectively differentiate periods near apnoea events from those further away.
  • Demonstrated that combining selected features significantly enhances the ability to detect apnoea events.
  • Achieved high performance metrics: 99.77% specificity, 97.38% sensitivity, and 98.25% accuracy with the combined feature approach.

Conclusions:

  • ECG-derived HRV analysis is a viable method for early detection of sleep apnoea.
  • The proposed feature combination strategy offers superior diagnostic performance compared to previous studies.
  • Early and accurate diagnosis of sleep apnoea can lead to timely interventions, improving patient outcomes and potentially saving lives.