Department of Emergency Medicine
Artificial Intelligence and Robotics | Biomedical Devices and Instrumentation | Circulatory and Respiratory Physiology | Emergency Medicine | Equipment and Supplies | Trauma
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 In addition, the proposed features and algorithm performed well even when moderate blood volume was withdrawn.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.
Blood, Hemorrhage, Heart rate, Physicians, Blood pressure, Breathing
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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.
DOI of Published Version
PLoS One. 2018 Mar 29;13(3):e0195087. doi: 10.1371/journal.pone.0195087. eCollection 2018. Link to article on publisher's site
Reljin N, Zimmer G, Malyuta Y, Shelley K, Mendelson Y, Blehar DJ, Darling CE, Chon KH. (2018). Using support vector machines on photoplethysmographic signals to discriminate between hypovolemia and euvolemia. Open Access Publications by UMass Chan Authors. https://doi.org/10.1371/journal.pone.0195087. Retrieved from https://escholarship.umassmed.edu/oapubs/3347
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Artificial Intelligence and Robotics Commons, Biomedical Devices and Instrumentation Commons, Circulatory and Respiratory Physiology Commons, Emergency Medicine Commons, Equipment and Supplies Commons, Trauma Commons