title: EEG signal processing in anaesthesia. Use of a neural network technique for monitoring depth of anaesthesia.
abstract: BACKGROUND
  The Bispectral Index (BIS) is a proprietary index of anaesthesia depth, which is correlated with the level of consciousness and probability of intraoperative recall. The present study investigates the use of a neural network technique to obtain a non-proprietary index of the depth of anaesthesia from the processed EEG data.
  
  
  METHODS
  Two hundred patients, who underwent general abdominal surgery, were recruited for our trial. For anaesthesia we used a total i.v. technique, tracheal intubation, and artificial ventilation. Fourteen EEG variables, including the BIS, were extracted from the EEG, monitored with an EEG computerized monitor, and then stored on a computer. Data from 150 patients were used to train the neural network. All the variables, excluding the BIS, were used as input data in the neural network. The output targets of the network were provided by anaesthesia scores ranging from 10 to 100 assigned by the anaesthesiologist according to the observer's assessment of alertness and sedation (OAA/S) and other clinical means of assessing depth of anaesthesia. Data from the other 50 patients were used to test the model and for statistical analysis.
  
  
  RESULTS
  The artificial neural network was successfully trained to predict an anaesthesia depth index, the NED (neural network evaluated depth), ranging from 0 to 100. The correlation coefficient between the NED and the BIS over the test set was 0.94 (P<0.0001).
  
  
  CONCLUSIONS
  We have developed a neural network model, which evaluates 13 processed EEG parameters to produce an index of anaesthesia depth, which correlates very well with the BIS during total i.v. anaesthesia with propofol.
authors.affiliation: Dipartimento Area Critica Medico Chirurgica, Università di Firenze, Italy.
authors.forename: O
authors.initials: O
authors.lastname: Ortolani
authors.forename: A
authors.initials: A
authors.lastname: Conti
authors.forename: A
authors.initials: A
authors.lastname: Di Filippo
authors.forename: C
authors.initials: C
authors.lastname: Adembri
authors.forename: E
authors.initials: E
authors.lastname: Moraldi
authors.forename: A
authors.initials: A
authors.lastname: Evangelisti
authors.forename: M
authors.initials: M
authors.lastname: Maggini
authors.forename: S J
authors.initials: SJ
authors.lastname: Roberts
mesh_terms: D000005:Abdomen / Q000601:surgery; D000328:Adult; D000771:Anesthesia, Intravenous; D018686:Anesthetics, Intravenous; D004569:Electroencephalography / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D016343:Monitoring, Intraoperative / Q000379:methods*; D016571:Neural Networks, Computer; D015742:Propofol; D012815:Signal Processing, Computer-Assisted
chemical_list: D018686:Anesthetics, Intravenous; D015742:Propofol