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Abstract
There is no reliable automated non-invasive solution for monitoring circulation and guiding treatment in prehospital emergency medicine. Cardiac output (CO) monitoring might provide a solution, but CO monitors are not feasible/practical in the prehospital setting. Non-invasive ballistocardiography (BCG) measures heart contractility and tracks CO changes. This study analyzed the feasibility of estimating CO using morphological features extracted from BCG signals. In 20 healthy subjects ECG, carotid/abdominal BCG, and invasive arterial blood pressure based CO were recorded. BCG signals were adaptively processed to isolate the circulatory component from carotid (CCc) and abdominal (CCa) BCG. Then, 66 features were computed on a beat-to-beat basis to characterize amplitude/duration/area/length of the fluctuation in CCc and CCa. Subjects’ data were split into development set (75%) to select the best feature subset with which to build a machine learning model to estimate CO and validation set (25%) to evaluate model’s performance. The model showed a mean absolute error, percentage error and 95% limits of agreement of 0.83 L/min, 30.2% and − 2.18–1.89 L/min respectively in the validation set. BCG showed potential to reliably estimate/track CO. This method is a promising first step towards an automated, non-invasive and reliable CO estimator that may be tested in prehospital emergencies.
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1 Oslo University Hospital, Norwegian National Advisory Unit on Prehospital Emergency Medicine (NAKOS), Division of Prehospital Services, Oslo, Norway (GRID:grid.55325.34) (ISNI:0000 0004 0389 8485); University of Oslo, Institute of Clinical Medicine, Faculty of Medicine, Oslo, Norway (GRID:grid.5510.1) (ISNI:0000 0004 1936 8921)
2 University of the Basque Country (UPV/EHU), Department of Applied Mathematics, Bilbao, Spain (GRID:grid.11480.3c) (ISNI:0000 0001 2167 1098)
3 University of the Basque Country (UPV/EHU), Department of Electronic Technology, Eibar, Spain (GRID:grid.11480.3c) (ISNI:0000 0001 2167 1098)
4 Ullevål Hospital, Division of Internal Medicine, Department of Cardiology, Oslo, Norway (GRID:grid.55325.34) (ISNI:0000 0004 0389 8485)
5 University of Oslo, Institute of Clinical Medicine, Faculty of Medicine, Oslo, Norway (GRID:grid.5510.1) (ISNI:0000 0004 1936 8921); Ullevål Hospital, Division of Emergency Medicine, Department of Anestesiology, Oslo, Norway (GRID:grid.55325.34) (ISNI:0000 0004 0389 8485)
6 University of the Basque Country (UPV/EHU), Department of Communications Engineering, Bilbao, Spain (GRID:grid.11480.3c) (ISNI:0000 0001 2167 1098)
7 Oslo University Hospital, Norwegian National Advisory Unit on Prehospital Emergency Medicine (NAKOS), Division of Prehospital Services, Oslo, Norway (GRID:grid.55325.34) (ISNI:0000 0004 0389 8485); Ullevål Hospital, Division of Prehospital Services, Department of Air Ambulance, Oslo, Norway (GRID:grid.55325.34) (ISNI:0000 0004 0389 8485)