Mahdi Momeni, Sophie Wuthe, Michaela Bitten Molmer, Emilie Lobner Svendsen, Mikkel Brabrand, Peter Biesenbach, Daniel Teichmann
{"title":"Facial Remote Photoplethysmography for Continuous Heart Rate Monitoring during Prolonged Cold Liquid Bolus Administration.","authors":"Mahdi Momeni, Sophie Wuthe, Michaela Bitten Molmer, Emilie Lobner Svendsen, Mikkel Brabrand, Peter Biesenbach, Daniel Teichmann","doi":"10.1109/EMBC53108.2024.10781709","DOIUrl":null,"url":null,"abstract":"<p><p>This study investigates non-contact heart rate (HR) monitoring through camera-based remote PPG during intravenous fluid bolus (FB) therapy. The experiment, at Odense University Hospital, Denmark, involved 4 volunteers and over 350 minutes of filming. We implemented a MATLAB-based HR extraction tool chain. The proposed method includes a two-stage process for dynamically determining regions of interest (ROIs), incorporating deep learning for facial landmarks detection and a subsequential consideration of subjects' facial dimensions. HR estimation uses chrominance-based (CHROM) and plane-orthogonal-to-skin (POS) PPG signal extraction methods, chosen for robustness against motion artifacts. Deviating from usual advice for other methods, omitting preprocessing minimizes signal processing still yielding a low error rate. The system achieved a mean error of fewer than 2 beats per minute (bpm), underscoring iPPG's alignment with ground truth. The results exemplify the feasibility of remote PPG monitoring in critical care and emergency settings.</p>","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2024 ","pages":"1-4"},"PeriodicalIF":0.0000,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EMBC53108.2024.10781709","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This study investigates non-contact heart rate (HR) monitoring through camera-based remote PPG during intravenous fluid bolus (FB) therapy. The experiment, at Odense University Hospital, Denmark, involved 4 volunteers and over 350 minutes of filming. We implemented a MATLAB-based HR extraction tool chain. The proposed method includes a two-stage process for dynamically determining regions of interest (ROIs), incorporating deep learning for facial landmarks detection and a subsequential consideration of subjects' facial dimensions. HR estimation uses chrominance-based (CHROM) and plane-orthogonal-to-skin (POS) PPG signal extraction methods, chosen for robustness against motion artifacts. Deviating from usual advice for other methods, omitting preprocessing minimizes signal processing still yielding a low error rate. The system achieved a mean error of fewer than 2 beats per minute (bpm), underscoring iPPG's alignment with ground truth. The results exemplify the feasibility of remote PPG monitoring in critical care and emergency settings.