Using support vector machines on photoplethysmographic signals to discriminate between hypovolemia and euvolemia

Abstract

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 In addition, the proposed features and algorithm performed well even when moderate blood volume was withdrawn.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.

Current progress of photoplethysmography and SPO2 for health monitoring

“…In rPPG, a color filter array (CFA) is used to demosaicize and denoise the signal [118,119]. The algorithm utilized signal decomposition and the support vector machine (SVM) model to remove motion artefacts from the PPG signal [110,[120][121][122][123][124][125]. Generally, the PPG signal and acceleration-derived features are extracted and classified using an SVM classifier.…”

Evidence-based Clinical Decision Support Systems for the prediction and detection of three disease states in critical care: A systematic literature review

“…Across all studies, three had sample sizes ≤100 29, 30, 36 ; three had sample sizes of 101–1000 28, 31, 32 ; four studies had sample sizes of 1001–10,000 19, 33, 34, 37, 42 ; and another five studies, four retrospective single-center studies and one multi-center, had sample sizes larger than 10,000 35, 38– 41 . The three largest studies included patients admitted to various wards of a specified hospital.…”

“…Of the 15 published studies, five were conducted by research groups outside the USA 28– 32 . Ten studies were conducted in the US 19, 33– 41 , Thirteen studies were retrospective 19, 28– 33, 35, 37– 41 and only two were prospective 34, 36 . Nine studies were single-center 28, 30, 31, 33, 37– 41 and six studies were multi-center 19, 29, 32, 34– 36 .…”

“…Ten studies were conducted in the US 19, 33– 41 , Thirteen studies were retrospective 19, 28– 33, 35, 37– 41 and only two were prospective 34, 36 . Nine studies were single-center 28, 30, 31, 33, 37– 41 and six studies were multi-center 19, 29, 32, 34– 36 . Five studies were time-series 28, 30– 32, 40 and nine were case-series 19, 29, 33– 35, 37– 39, 41 .…”

Applications of machine learning in acute care research

“…18 The accuracy of support vector machine can continue to improve over time, if continually trained, and can often be implemented in short order with limited training sets. 39,40 Ideally, however, investigators may select a panel of machine learning models to ensure consistency with their results much the same as a sensitivity analysis can confirm other findings in a study.…”