Automatic analysis of pre‐ejection period during sleep using impedance cardiogram.

The pre‐ejection period (PEP) is a valid index of myocardial contractility and beta‐adrenergic sympathetic control of the heart defined as the time between electrical systole (ECG Q wave) to the initial opening of the aortic valve, estimated as the B point on the impedance cardiogram (ICG). B‐point detection accuracy can be severely impacted if ICG cardiac cycles corrupted by motion artifact, noise, or electrode displacement are included in the analyses. Here, we developed new algorithms to detect and exclude corrupted ICG cycles by analyzing their level of activity. PEP was then estimated and analyzed on ensemble‐averaged clean ICG cycles using an automatic algorithm previously developed by the authors for the detection of B point in awake individuals. We investigated the algorithms' performance relative to expert visual scoring on long‐duration data collected from 20 participants during overnight recordings, where the quality of ICG could be highly affected by movement artifacts and electrode displacements and the signal could also vary according to sleep stage and time of night. The artifact rejection algorithm achieved a high accuracy of 87% in detection of expert‐identified corrupted ICG cycles, including those with normal amplitude as well as out‐of‐range values, and was robust to different types and levels of artifact. Intraclass correlations for concurrent validity of the B‐point detection algorithm in different sleep stages and in‐bed wakefulness exceeded 0.98, indicating excellent agreement with the expert. The algorithms show promise toward sleep applications requiring accurate and reliable automatic measurement of cardiac hemodynamic parameters. The pre‐ejection period (PEP) is a valid index of sympathetic beta‐adrenergic influences upon the heart measured from the simultaneously recorded electrocardiogram and impedance cardiogram (ICG). PEP accuracy can be severely impacted if ICG cardiac cycles corrupted by motion artifact, noise, or electrode displacement are included in the analyses. Here, we developed new algorithms to automatically detect and exclude corrupted ICG cycles by analyzing their level of activity and evaluated the algorithms' performance on sleep data affected by noise and artifacts. The algorithms performed well and showed promise toward sleep applications requiring accurate and reliable automatic measurement of cardiac hemodynamic parameters. [ABSTRACT FROM AUTHOR]