• Iris Cramer, Rik van Esch, Cindy Verstappen, Carla Kloeze, Bas van Bussel, Sander Stuijk, Jan Bergmans, Marcel van 't Veer, Svitlana Zinger, Leon Montenij, R Arthur Bouwman, and Lukas Dekker.
• Department of Electrical Engineering, Eindhoven University of Technology, Groene Loper 3, 5612 AZ, Eindhoven, the Netherlands. I.c.cramer@tue.nl.
• J Clin Monit Comput. 2025 Jan 29.

AbstractUnobtrusive pulse rate monitoring by continuous video recording, based on remote photoplethysmography (rPPG), might enable early detection of perioperative arrhythmias in general ward patients. However, the accuracy of an rPPG-based machine learning model to monitor the pulse rate during sinus rhythm and arrhythmias is unknown. We conducted a prospective, observational diagnostic study in a cohort with a high prevalence of arrhythmias (patients undergoing elective electrical cardioversion). Pulse rate was assessed with rPPG via a visible light camera and ECG as reference, before and after cardioversion. A cardiologist categorized ECGs into normal sinus rhythm or arrhythmias requiring further investigation. A supervised machine learning model (support vector machine with Gaussian kernel) was trained using rPPG signal features from 60-s intervals and validated via leave-one-subject-out. Pulse rate measurement performance was evaluated with Bland-Altman analysis. Of 72 patients screened, 51 patients were included in the analyses, including 444 60-s intervals with normal sinus rhythm and 1130 60-s intervals of clinically relevant arrhythmias. The model showed robust discrimination (AUC 0.95 [0.93-0.96]) and good calibration. For pulse rate measurement, the bias and limits of agreement for sinus rhythm were 1.21 [- 8.60 to 11.02], while for arrhythmia, they were - 7.45 [- 35.75 to 20.86]. The machine learning model accurately identified sinus rhythm and arrhythmias using rPPG in real-world conditions. Heart rate underestimation during arrhythmias highlights the need for optimization.© 2025. The Author(s).

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