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Classifying depth of anesthesia using EEG features, a comparison

Esmaeili, V ; Sharif University of Technology | 2007

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  1. Type of Document: Article
  2. DOI: 10.1109/IEMBS.2007.4353239
  3. Publisher: 2007
  4. Abstract:
  5. Various EEG features have been used in depth of anesthesia (DOA) studies. The objective of this study was to And 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. © 2007 IEEE
  6. Keywords:
  7. Bayesian networks ; Electroencephalography (EEG) ; Signal processing ; Approximate entropy ; Bayesian classifier ; Depth of anesthesia (DOA) ; Anesthesiology ; Classification ; Clinical trial ; Comparative study ; Human ; Inhalation anesthesia ; Intravenous anesthesia ; Male ; Methodology ; Middle aged ; Patient monitoring ; Urologic surgery ; Adolescent ; Adult ; Aged ; Female ; Humans ; Monitoring, intraoperative ; Urologic surgical procedures
  8. Source: 29th Annual International Conference of IEEE-EMBS, Engineering in Medicine and Biology Society, EMBC'07, Lyon, 23 August 2007 through 26 August 2007 ; 2007 , Pages 4106-4109 ; 05891019 (ISSN) ; 1424407885 (ISBN); 9781424407880 (ISBN)
  9. URL: https://ieeexplore.ieee.org/document/4353239