Using support vector machines on photoplethysmographic signals to discriminate between hypovolemia and euvolemia
Authors
Reljin, NatasaZimmer, Gary
Malyuta, Yelena
Shelley, Kirk
Mendelson, Yitzhak
Blehar, David J.
Darling, Chad E.
Chon, Ki H.
UMass Chan Affiliations
Department of Emergency MedicineDocument Type
Journal ArticlePublication Date
2018-03-29Keywords
BloodHemorrhage
Heart rate
Physicians
Blood pressure
Breathing
Artificial Intelligence and Robotics
Biomedical Devices and Instrumentation
Circulatory and Respiratory Physiology
Emergency Medicine
Equipment and Supplies
Trauma
Metadata
Show full item recordAbstract
Identifying trauma patients at risk of imminent hemorrhagic shock is a challenging task in intraoperative and battlefield settings given the variability of traditional vital signs, such as heart rate and blood pressure, and their inability to detect blood loss at an early stage. To this end, we acquired N = 58 photoplethysmographic (PPG) recordings from both trauma patients with suspected hemorrhage admitted to the hospital, and healthy volunteers subjected to blood withdrawal of 0.9 L. We propose four features to characterize each recording: goodness of fit (r2), the slope of the trend line, percentage change, and the absolute change between amplitude estimates in the heart rate frequency range at the first and last time points. Also, we propose a machine learning algorithm to distinguish between blood loss and no blood loss. The optimal overall accuracy of discriminating between hypovolemia and euvolemia was 88.38%, while sensitivity and specificity were 88.86% and 87.90%, respectively. In addition, the proposed features and algorithm performed well even when moderate blood volume was withdrawn. The results suggest that the proposed features and algorithm are suitable for the automatic discrimination between hypovolemia and euvolemia, and can be beneficial and applicable in both intraoperative/emergency and combat casualty care.Source
PLoS One. 2018 Mar 29;13(3):e0195087. doi: 10.1371/journal.pone.0195087. eCollection 2018. Link to article on publisher's site
DOI
10.1371/journal.pone.0195087Permanent Link to this Item
http://hdl.handle.net/20.500.14038/40541PubMed ID
29596477Related Resources
Rights
Copyright: © 2018 Reljin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Distribution License
http://creativecommons.org/licenses/by/4.0/ae974a485f413a2113503eed53cd6c53
10.1371/journal.pone.0195087
Scopus Count
Except where otherwise noted, this item's license is described as Copyright: © 2018 Reljin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.