Open AccessTowards a Lightweight Classifier to Detect Hypovolemic Shock
Author(s) -
Leena Pramanik,
Christopher L. Felton,
Robert W. Techentin,
David R. Holmes,
Timothy B. Curry,
Michael J. Joyner,
Victor A. Convertino,
Clifton R. Haider
Publication year - 2023
Publication title -
2023 45th annual international conference of the ieee engineering in medicine and biology society (embc)
Language(s) - English
Resource type - Conference proceedings
eISSN - 2694-0604
ISBN - 979-8-3503-2447-1
DOI - 10.1109/embc40787.2023.10340949
Subject(s) - bioengineering , engineering profession , general topics for engineers
Predicting the ability of an individual to compensate for blood loss during hemorrhage and detect the likely onset of hypovolemic shock is necessary to permit early clinical intervention. Towards this end, the compensatory reserve metric (CRM) has been demonstrated to directly correlate with an individual’s ability to maintain compensatory mechanisms during loss of blood volume from onset (one-hundred percent health) to exsanguination (zero percent health). This effort describes a lightweight, three-class predictor (good, fair, poor) of an individual’s compensatory reserve using a linear support-vector machine (SVM) classifier. A moving mean filter of the predictions demonstrates a feasible model for implementation of real-time hypovolemia monitoring on a wearable device, requiring only 408 bytes to store the models’ coefficients and minimal processor cycles to complete the computations.