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Area of Science:

  • Cardiovascular Physiology
  • Biomedical Engineering
  • Sleep Medicine

Background:

  • The pre-ejection period (PEP) is a key indicator of myocardial contractility and cardiac autonomic function.
  • Accurate PEP measurement relies on precise identification of the B point on impedance cardiograms (ICG).
  • Motion artifacts and noise during sleep recordings frequently compromise ICG signal quality and B-point detection.

Purpose of the Study:

  • To develop and validate algorithms for detecting and excluding corrupted ICG cycles.
  • To enable accurate automatic estimation of PEP using artifact-free ICG data during sleep.
  • To assess the reliability of PEP measurement across different sleep stages and in-bed wakefulness.

Main Methods:

  • Developed novel algorithms to identify corrupted ICG cycles based on signal activity levels.
  • Excluded artifact-contaminated cycles before ensemble averaging.
  • Utilized a previously validated automatic B-point detection algorithm on clean ICG data from overnight recordings of 20 participants.

Main Results:

  • The artifact rejection algorithm achieved 87% accuracy in identifying expert-labeled corrupted ICG cycles, demonstrating robustness to various artifact types and levels.
  • The B-point detection algorithm showed excellent concurrent validity, with intraclass correlations exceeding 0.98 across sleep stages and wakefulness, indicating high agreement with expert scoring.
  • The combined algorithms successfully enabled reliable PEP estimation even with significant movement artifacts and signal variations common in overnight polysomnography.

Conclusions:

  • The developed artifact detection and rejection algorithms significantly enhance the reliability of ICG-based PEP measurements.
  • These algorithms are promising for automatic, accurate, and robust assessment of cardiac hemodynamic parameters during sleep studies.
  • This approach facilitates advanced sleep applications requiring precise cardiac monitoring.