Due to limited remote triage monitoring capabilities, combat medics cannot currently distinguish bleeding soldiers from those engaged in combat unless they have physical access to them. PURPOSE: To test the hypothesis that low-level physiological signals can be used to develop a machine-learning algorithm for tracking changes in central blood volume that will subsequently distinguish central hypovolemia from physical activity. METHODS: 24 subjects underwent central hypovolemia via lower body negative pressure (LBNP), and a supine-cycle exercise protocol. Exercise workloads were determined by matching heart rate (HR) responses from each LBNP level. HR and stroke volume (SV) were measured via Finometer. ECG, heat flux, skin temperature, galvanic skin response, and 2-axis acceleration were obtained from an armband (SenseWear® Pro2) and used to develop a machine-learning algorithm to predict changes in SV as an index of central blood volume under both conditions. The algorithm SV was retrospectively compared against Finometer SV. A model was developed to determine if unknown data points could be correctly classified into these two conditions using leave-one-out cross validation. RESULTS: Algorithm versus Finometer SV values were strongly correlated for LBNP in individual subjects (mean r=0.92; range 0.75-0.98), but only moderately correlated for exercise (mean r=0.50; range -0.23-0.87). From the first level of LBNP/exercise, the machine learning algorithm was able to distinguish between LBNP and exercise with high accuracy, sensitivity, and specificity (all ≥90%). CONCLUSIONS: A machine-learning algorithm developed from low-level physiological signals could reliably distinguish central hypovolemia from exercise, indicating that this device could provide battlefield remote triage capabilities.
- triage algorithm
- lower body negative pressure
- central hypovolemia
- Copyright © 2013, Journal of Applied Physiology