Decision support system for anesthesiologists through monitoring of level of consciousness, neuromuscular block and nociception using data fusion and artificial intelligence

• Grant number: 17/22815-7
• Support Opportunities: Research Grants - Innovative Research in Small Business - PIPE
• Start date: May 01, 2019
• End date: January 31, 2020
• Field of knowledge: Engineering - Electrical Engineering - Electrical, Magnetic and Electronic Measurements, Instrumentation
• Principal Investigator: Bruno Bestle Turrin
• Grantee: Bruno Bestle Turrin
• Company: Serenity Now Pesquisa e Desenvolvimento Ltda
• CNAE: Fabricação de aparelhos e equipamentos de medida, teste e controle ; Fabricação de equipamentos e aparelhos elétricos não especificados anteriormente
• City: São Paulo
• Associated scholarship(s): 19/10634-3 - Decision support system for anesthesiologists through monitoring of level of consciousness, neuromuscular block and nociception using data fusion and artificial intelligence, BP.PIPE

Abstract

The balanced anesthesia process contains three main parts: the control of hypnosis, analgesia, and neuromuscular blockade. For the induction phase, the anesthesiologist performs protocols based on prior planning specific to each patient and usually performs these controls by monitoring the classic vital signs and other clinical signs for the maintenance phase. In a way, this professional is the controller in a control system that acts on the plant (the patient) through the infusion of hypnotic drugs, analgesics and neuromuscular blockers. In addition, the anesthesiologist estimates the state of consciousness, the level of analgesia and the level of neuromuscular blockage through other indirect measures, as well as a state observer. There are different techniques for direct monitoring of these three anesthesia variables (DoA, NMB and NoL), such as BIS and Narcotrend, but all have some disadvantages, especially when the anesthesia process combines different drugs. This work proposes a new way of evaluating DoA, NMB and NoL using data fusion techniques to combine classical clinical signs with advanced EEG monitoring techniques to provide a decision support system for the anesthesiologist. (AU)