Monitoring the Level of Anesthesia by Automatic Analysis of Spontaneous EEG Activity

Recent advances in the fields of automatic EEG analysis and pattern recognition provide a valuable new perspective for reconsidering the question of whether or not the level of anesthesia can be reliably estimated by analyzing spontaneous EEG activity. The feasibility of developing a computer-based EEG pattern recognition system capable of continuously estimating the level of anesthesia of patients during surgical operations is investigated in this paper. Anesthetists were asked to define five clinically significant levels of anesthesia for a commonly used anesthetic in terms of meaningful non-EEG criteria. The subsequent development of various EEG pattern recognition systems in an attempt to reliably estimate the levels of anesthesia as determined by the non-EEG criteria is described. All such systems employ Bayes decision rule under the assumption that pattern features are statistically independent. System performance is evaluated in terms of the estimated probability of misclassification error. Systems based on the recognition of spectral or frequency-domain EEG patterns are compared to those based on the recognition of time-domain EEG patterns.