PT  - JOURNAL ARTICLE
AU  - Sunil B. Nagaraj
AU  - Pegah Kahali
AU  - Patrick L. Purdon
AU  - Fred E. Shapiro
AU  - M. Brandon Westover
TI  - Electroencephalogram Monitoring of Depth of Anesthesia during Office-Based Anesthesia
AID  - 10.1101/2020.10.27.356592
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.10.27.356592
4099  - http://biorxiv.org/content/early/2020/10/27/2020.10.27.356592.short
4100  - http://biorxiv.org/content/early/2020/10/27/2020.10.27.356592.full
AB  - Objective Electroencephalogram (EEG) monitors are often used to monitor depth of general anesthesia. EEG monitoring is less well developed for lighter levels of anesthesia. Here we present an automated method to monitor the depth of anesthesia for office based procedures using EEG spectral features.Methods We analyze EEG recordings from 30 patients undergoing sedation using a multimodal anesthesia strategy. Level of sedation during the procedure is coded using the Richmond Agitation and Sedation Scale (RASS). The power spectrum from the frontal EEG is used to infer the level of sedation, by training a logistic regression model with elastic net regularization. Area under the receiver operator characteristic curve (AUC) is used to evaluate how well the automated system distinguishes awake from sedated EEG epochs.Results EEG power spectral characteristics vary systematically and consistently across patients with the levels of light anesthesia and relatively healthy patients encountered during office-based anesthesia procedures. The logistic regression model using spectral EEG features distinguishes awake and sedated states with an AUC of 0.85 (± 0.14).Conclusions Our results demonstrate that frontal EEG spectral features can reliably monitor sedation levels during office based anesthesia.Competing Interest StatementDuring this research, Dr. Westover was supported by the Glenn Foundation for Medical Research and the American Federation for Aging Research through a Breakthroughs in Gerontology Grant; through the American Academy of Sleep Medicine through an AASM Foundation Strategic Research Award; and by grants from the NIH (1R01NS102190, 1R01NS102574, 1R01NS107291, 1RF1AG064312). Others authors have no conflicts to declare.