Adaptive Data Analysis for Quantitative Monitoring of Anesthesia
Abstract
This proposed research focuses on obtaining critical understanding of patient conditions and on developing quantitative indices for depth of anesthesia from a variety of physiological data. The condition of a patient under anesthesia is currently based primarily on the experience of anesthesiologists. One major technical challenge for developing depth of anesthesia indices via the analysis of bio-signals is that these signals are typically nonstationary and nonlinear, whereas the analysis tools being applied, such as Fourier and wavelet analysis, are based on the assumption that the signals are linear and stationary. The PIs from NCU Taiwan have done high impact research work on dynamical biomarkers and modeling human health conditions using an adaptive data analysis approach. The proposed research aims to provide mathematical models and computational tools that can be used to quantitatively assess the degree of anesthesia through the state of hypnosis, analgesia, and muscle relaxation conditions based on physiological data.b. Quantitative indices to characterize different physiological aspects of a patient in an anesthetic state will have tremendous value to the care of warfighters wounded in combat. A reliable quantitative assessment using physiological signals (EEG, respiration, blood pressure, etc) to characterize anesthesia is essential for the future Navy autonomous critical care system (ACCS) development
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Feb 03, 2017
- Source ID
- N62909151N128
Entities
People
- Norden Huang
Organizations
- Office of Naval Research
- United States Navy