Biomedical Engineering Approaches for Non-Invasive Monitoring in Hypovolemia and Hypotension

Dear Colleagues,

Monitoring vital signs is crucial for assessing a patient’s condition at every stage of medical care. However, monitoring critical vital signs, such as continuous blood pressure, often requires invasive sensors, which are impractical in pre-hospital or combat casualty care. Furthermore, traditional vital signs can sometimes only provide a partial view of a patient’s condition, as compensatory physiological mechanisms can mask underlying issues. Thus, improved methods are needed to capture continuous non-invasive data when treating traumatic medical conditions.

This Special Issue, titled “Biomedical Engineering Approaches for Non-Invasive Monitoring in Hypovolemia and Hypotension”, features original research papers and comprehensive reviews, highlighting novel methodologies and experimental validation of approaches to non-invasively track a patient’s clinical status. Topics of interest for this Special Issue include, but are not limited to, the following research areas:  

• Continuous non-invasive vital sign measurement techniques during progressive hypotension, such as those associated with traumatic hemorrhage;
• Machine learning models for improved physiological tracking of a patient’s clinical condition using non-invasive vital sign measurements;
• Deep learning artificial intelligence techniques for estimating compensatory status or casualty triage state;
• Experimental verification and validation of non-invasive blood pressure measurement techniques;
• In vivo animal or clinical study analysis of advanced monitoring metrics for estimating physiological status.

Any methods or technologies may be considered relevant if they rely on non-invasive monitoring to provide continuous estimations of physiological status and show potential for application in hypotensive and/or hypovolemic patient management.

Dr. Eric J. Snider
Dr. Victor A. Convertino
Guest Editors

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• physiological monitoring
• non-invasive monitoring
• blood pressure estimation
• advanced monitoring
• machine learning
• deep learning
• artificial intelligence