Development and Evaluation of Hemodynamic Monitoring Algorithms
Abstract
The ultimate objective of this project is to enable context-aware and safety-preserving closed-loop control of critical care resuscitation. Toward the long-term goal, the objective of this project is to develop and evaluate hemodynamic monitoring algorithms capable of estimating blood volume (BV) and cardiac output (CO) based on (i) machine learning analysis of physiological signals and (ii) mathematical model-based recursive inference. Our technical approach is to (i) continue to develop, optimize, and validate machine learning-based digital biomarkers of BV using wearable-compatible physiological signals, as well as to (ii) continue to develop, optimize, and validate recursive inference-based hemodynamic monitoring algorithms capable of estimating CO in real time. To achieve our goal, we will (i) continue to develop novel machine learning-based digital biomarkers of BV and its compensation status; (ii) develop various recursive inference-based hemodynamic monitoring-algorithms; and (iii) validate the digital biomarkers and hemodynamic monitoring algorithms in vivo. If-successful, this project will furnish innovative digital biomarkers and computational algorithms that can evolve into medical device systems for more convenient and accurate hemodynamic monitoring. This research aims at developing and validating novel technologies to support the automated critical care system. Consequently, it is highly relevant to the technological needs of the Navy. Especially, this research work may contribute to the Navy in the following aspects. First, it may furnish innovative methods for hemodynamic monitoring in combat casualties receiving resuscitation, which may ultimately revolutionize the quality of combat casualty care.-Second, it may facilitate the development of novel closed-loop controlled resuscitation algorithms and systems that can preserve holistic hemodynamic safety.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Oct 13, 2023
- Source ID
- N000142312828
Entities
People
- Jin-Oh Hahn
Organizations
- Office of Naval Research
- United States Navy
- University of Maryland