Kavli Affiliate: Jeanne Paz
| Authors: Virginie Loison, Yuliya Voskobiynyk, Britta Lindquist, Deanna Necula, Dan Longrois, Jeanne T. Paz and David Holcman
| Summary:
Cortical electro-encephalography (EEG) has become the clinical reference for monitoring unconsciousness during general anesthesia. The current EEG-based monitors classify general anesthesia states simply as underdosed, adequate, or overdosed, with no transition phases among these states, and therefore no predictive power. To address the issue of transition phases, we analyzed EEG signal of isoflurane-induced general anesthesia in mice. We adopted a data-driven approach and utilized signal processing to track theta- and delta- band dynamics as well as iso-electric suppressions. By combining this approach with machine learning, we developed a fully-automated algorithm. We found that the dampening of the delta-band occurred several minutes before significant iso-electric suppression episodes. Additionally, we observed a distinct gamma-frequency oscillation that persisted for several minutes during the recovery phase following isoflurane-induced overdose. Finally, we constructed a map summarizing multiple states and their transitions which can be utilized to predict and prevent overdose during general anesthesia. The transition phases we identified and algorithm we developed may allow clinicians to prevent inadequate anesthesia, and thus individually tailor anesthetic regimens.