Distinguishing Anesthetized from Awake State in Patients: A New Approach Using One Second Segments of Raw EEG - Juel, Bjørn E.; Romundstad, Luis; Kolstad, Frode; Storm, Johan F.; Larsson, Pål G. (Frontiers in Human Neuroscience, 2018)
Objective: The objective of this study was to test whether properties of one second segments of spontaneous scalp EEG activity can be used to automatically distinguish the awake state from the anesthetized state in patients undergoing general propofol anesthesia. Methods: 25 channel EEG was recorded from 10 patients undergoing general intravenous propofol anesthesia with remifentanil during anterior cervical discectomy and fusion. From this, we extracted properties of the EEG by applying the Directed Transfer Function (DTF) directly to every one-second segment of the raw, signal. The extracted properties were used to develop a data-driven classification algorithm to categorize patients as ‘anesthetized’ or ‘awake’ for every one-second segment of raw EEG. Results: The properties of the EEG signal were significantly different in the awake and anesthetized states for at least 8 of the 25 channels (p<0.05, Bonferroni corrected Wilcoxon rank-sum tests). Using these differences, our algorithms achieved classification accuracies of 95.9%. Conclusion: Properties of the DTF calculated from one-second segments of raw EEG can be used to reliably classify whether the patients undergoing general anesthesia with propofol and remifentanil were awake or anesthetized. Significance: This method may be useful for developing automatic real-time monitors of anesthesia.