Real-time spectral prediction of the brain during General Anesthesia

Objective

A general methodology based on modeling, data processing and classification is still lacking to predict the brain response to sedation during anesthesia. In this project, we propose to develop a mathematical approach of the brain signal revealed by EEG behavior. We will use modeling to identify features present in frequency bands, to predict the brain response. Using spectral analysis, we will study the time-frequency of the dominant frequency band of the EEG signal. We propose to develop algorithms dedicated to study the brain dynamics based combining transient motifs of the EEG signal. Finally, the present approach will be tested in different hospitals for adults and children. We will specifically focus on predicting the depth of anesthesia. To conclude, the present method could provide new tools to practitioners to prevent deep sedation and possible post-anesthetic complications.