Automatic Classification Algorithms of the EEG Monitor Narcotrend for Routinely Recorded EEG Data from General Anaesthesia: a Validation Study

• Published in: Biomedizinische Technik Vol. 47; no. 1-2; pp. 9 - 13
• Main Authors: Schultz, B., Grouven, U., Schultz, A.
• Format: Journal Article
• Language: English
• Published: Berlin, New York Walter de Gruyter, Berlin / New York 2002
• Subjects: Algorithms; Anesthesia, General; Arousal - drug effects; Beta Rhythm; Delta Rhythm; Electroencephalography - classification; Electroencephalography - drug effects; Humans; Monitoring, Intraoperative; Reproducibility of Results; Signal Processing, Computer-Assisted; Theta Rhythm
• ISSN: 0013-5585; 1862-278X
• DOI: 10.1515/bmte.2002.47.1-2.9

Impacts of hypnotic drugs on brain function are reflected in the EEG. The EEG monitor Narcotrend performs an automatic classification of the EEG using a scale which was proposed by Kugler for visual evaluation of the EEG. In this article the results of a validation study of the automatic classification algorithms implemented in the EEG monitor Narcotrend are presented. Visual and automatic classification of EEG data recorded in routine clinical practice were compared. The correlation between visual and automatic assessment was high (Spearman rank correlation r = 0.90, prediction probability Pk = 0.90) and a sufficient agreement between visual and automatic assessment was achieved for 92% of the analysed EEG epochs. The results of the study suggest that the automatic classification algorithms implemented in the EEG monitor Narcotrend yield a reliable assessment of the depth of hypnosis.