title: Classifying depth of anesthesia using EEG features, a comparison.
abstract: Various EEG features have been used in depth of anesthesia (DOA) studies. The objective of this study was to find the excellent features or combination of them than can discriminate between different anesthesia states. Conducting a clinical study on 22 patients we could define 4 distinct anesthetic states: awake, moderate, general anesthesia, and isoelectric. We examined features that have been used in earlier studies using single-channel EEG signal processing method. The maximum accuracy (99.02%) achieved using approximate entropy as the feature. Some other features could well discriminate a particular state of anesthesia. We could completely classify the patterns by means of 3 features and Bayesian classifier.
authors.affiliation: Biomedical Signal and Image Processing Laboratory (BiSIPL), School of Electrical Engineering, Sharif University of Technology, Iran. vesmaeili@gmail.com
authors.forename: Vahid
authors.initials: V
authors.lastname: Esmaeili
authors.forename: Mohammad Bagher
authors.initials: MB
authors.lastname: Shamsollahi
authors.forename: Noor Mohammad
authors.initials: NM
authors.lastname: Arefian
authors.forename: Amin
authors.initials: A
authors.lastname: Assareh
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000769:Anesthesia, Inhalation / Q000145:classification; D000771:Anesthesia, Intravenous / Q000145:classification; D004569:Electroencephalography / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D016343:Monitoring, Intraoperative / Q000379:methods*; D013520:Urologic Surgical Procedures