Key result
An automatic algorithm for analyzing pre-ejection period during sleep achieved 87% accuracy in rejecting corrupted ICG cycles and an intraclass correlation >0.98 compared to expert visual scoring.
Why the study?
Pre-ejection period estimation via ICG B-point detection can be severely impaired by motion artifact, noise, or electrode displacement during overnight recordings across sleep stages.
Does an automatic algorithm accurately detect corrupted ICG cycles and estimate the pre-ejection period during sleep compared to expert visual scoring in participants undergoing overnight recordings?
Does an automatic algorithm accurately detect corrupted ICG cycles and estimate the pre-ejection period during sleep compared to expert visual scoring in participants undergoing overnight recordings?
The developed automatic algorithms for ICG artifact rejection and B-point detection show high accuracy and excellent agreement with expert scoring during overnight sleep recordings, enabling reliable measurement of cardiac hemodynamic parameters.
May enable scalable sleep hemodynamic monitoring; leaves open validation in diverse clinical cohorts before adoption.
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.
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Forouzanfar et al. (2019) studied Sleep (n=20). Automatic algorithm for artifact rejection and B-point detection vs. Expert visual scoring was evaluated on Accuracy of artifact rejection and intraclass correlation for B-point detection (ICC >0.98). An automatic algorithm for analyzing pre-ejection period during sleep achieved 87% accuracy in rejecting corrupted ICG cycles and an intraclass correlation >0.98 compared to expert visual scoring.
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