Machine learning for the study of EEG data recorded during general anesthesia

Project Summary

General Anesthesia (GA) is a drug-induced, reversible condition with three commonly accepted goals: lack of experience of surgery, nociceptive blockade and immobility for the needs of surgery. In 2010, 11.3 millions of anesthesia procedures were performed in France. However, despite numerous progresses in the understanding of GA mechanisms, some questions remain unanswered like the precise mechanisms of awakening after a GA, the long term effects of anesthesia or the common pathway of all anesthetics. The aim of this project is to use electroencephalogram (EEG) signatures and machine learning tools in order to better understand the phenomena involved during awakening from GA. In particular, the goal of our study is to precisely quantify GA recovery in order to further investigate its nature and time course, which remains debated. GA recovery is often considered as a passive process: as the anesthetics are eliminated, the reactivation of the neuronal circuits affected by GA would simply mirror their deactivation. But is it true? Could we find some traces of GA in the EEG of patients, even several hours after they woke up?

Laurent Oudre