title: Accuracy of combined EEG parameters in prediction the depth of anesthesia.
abstract: BACKGROUND
  The importance of proper qualitative evaluation of EEG parameters during surgery has been recognized since many years. Although none of the characteristics based on the frequency, entropy, and Bi spectral characteristics have been regarded as a good predictor for detection of the depth of anesthesia alone. So it seems necessary to study multiple characteristics together.
  
  
  OBJECTIVE
  In this study we tried to introduce the best combination of the mentioned characteristics.
  
  
  METHODS
  EEG data of 64 patients undergoing general anesthesia with the same anesthesia protocol (total intravenous anesthesia) were recorded in all anesthetic stages in Shohada Tajrish Hospital. Quantitative EEG characteristics are classified into 4 categories: time, frequency, bi spectral and entropy based characteristics. Their sensitivity, specificity and accuracy in determination of the depth of anesthesia are yielded by comparison with recorded reference signal in awake, light anesthesia, deep anesthesia and brain death patients. Then, with combining 2, 3, 4 and 5 of characteristics and using coded algorithm we determined the error degree and introduced the combination yielding the least error.
  
  
  RESULTS
  Fifteen vectors (of dimension two to five) which yielded the best results were introduced. Vectors combined of entropy based characteristics obtained 100% specificity and sensitivity during all 4 stages.
  
  
  CONCLUSIONS
  The combination entropy based characteristics had high accuracy in predicting the depth of anesthesia. Reevaluation of classic indices cortical status index and BIS seems necessary. The next step is to find a system to simplify the evaluation of this information for technicians.
authors.affiliation: Shohada Tajrish Hospital, Shahid Beheshti University of Medical Sciences, Tehran, IR Iran.
authors.forename: Nourmohammad
authors.initials: N
authors.lastname: Arefian
authors.forename: Amir Saied
authors.initials: AS
authors.lastname: Seddighi
authors.forename: Afsoun
authors.initials: A
authors.lastname: Seddighi
authors.forename: Ali Reza
authors.initials: AR
authors.lastname: Zali
keywords: Anesthesia; Depth; Electroencephalography