A Real-Time, Non-Invasive Monitoring System of Combat Casualties for Early Detection of Hemorrhagic Shock During Transport and Higher Echelon Medical

The proposed research has two aims: 1) to develop a real-time algorithm that uses non-invasive physiological signals to quickly and accurately detect severity of acute blood loss; and 2) to develop a complimentary algorithm that uses all or a subset of these signals (depending on their availability and situation specific appropriateness) to forewarn providers of an injured soldier’s predicted risk for hemodynamic decompensation. The algorithms will be developed for use during transport and higher echelon medical care. These research efforts will be based on human subject Lower Body Negative Pressure (LBNP) experiments done in collaboration with the US Army Institute for Surgical Research, with proposed real-time testing of the developed algorithms. The research effort utilizes standard and novel machine learning, statistical and signal processing algorithms, in an aim to identify the most robust algorithms for prediction of hypovolemia, given noisy physiological data.