Smart Anesthesia Monitor

Case ID:

Technology Summary:  WSU researchers have developed a technology to assist anesthesiologists using real-time monitoring, outcome prediction and anesthesia decision support in operating rooms. This smart anesthesia monitoring system allows the physician to look into the near-future and make decisions that are more objective, timely, and accurate. The core of the technology is a novel information processing methodology that uses measured drug rates, physiological signals and real-time data analysis to establish and update individual patient models.

Background: General anesthesia is an integral part of most surgical operations. Anesthesia decisions are very challenging, in which anesthetic requirements and agent dosages depend critically on patient medical conditions, surgical procedures, drug interactions, and coordinated levels of anesthesia depth and physiological variables such as blood pressures and heart rates. As a result, drug impact is very difficult to predict subjectively and manually. Satisfactory anesthesia decisions require extensive clinical experience and highly sharpened vigilance. Errors in anesthesia decisions occur even with experienced personnel, and the resulting impact ranges from minor consequences to serious morbidity and mortality.


•  Drug impact predictions
•  Optimal drug dosage
•  Real-time recommendations
•  Increased decision accuracy
•  Reduced clinical workload
•  Critical condition warning

Stage of Development — Pre-clinical

Initial prototype developed and tested with commercial anesthesia monitoring systems (i.e. Covidan Bispectral index (BIS), GE Entropy Monitoring). Clinical data collected from human patients to verify the utility of the system performance, response time, model accuracy. Fully automated drug control systems can possibly be integrated into future systems.

Patent Pending:
US10/561,074 “System for Identifying Patient response to Anesthesia Infusion”

•  “Decision-Oriented Multi-Outcome Modeling for Anesthesia Patients” The Open Biomedical Engineering Journal, 4, pp. 113-122, 2010
•  “Anesthesia Outcome Prediction” Middle East Journal of Anesthesiology Vol. 20, No. 3, pp. 363-368, 2009.


Patent Information:
For Information, Contact:
Nicole Grynaviski
Commercialization Principal
Wayne State University
Le Yi Wang
Hong Wang
Gang Yin
Drug Delivery
Patient Monitoring