Record number :
2422202
Title of article :
Accuracy of Combined EEG Parameters in Prediction the Depth of Anesthesia
Author/Authors :
Arefian, Nourmohammad Shohada Tajrish Hospital - Shahid Beheshti University of Medical Sciences, Tehran, IR Iran , Seddighi , Amir Saied Shohada Tajrish Hospital - Functional Neurosurgery Research Center of Shohada Tajrish Hospital - Shahid Beheshti University of Medical Sciences, Tehran, IR Iran , Afsoun, Seddighi Shohada Tajrish Hospital - Functional Neurosurgery Research Center of Shohada Tajrish Hospital - Shahid Beheshti University of Medical Sciences, Tehran, IR Iran , Zali, Ali Reza Shohada Tajrish Hospital - Functional Neurosurgery Research Center of Shohada Tajrish Hospital - Shahid Beheshti University of Medical Sciences, Tehran, IR Iran
Pages :
5
From page :
833
To page :
837
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. Objectives: In this study we tried to introduce the best combination of the mentioned characteristics. Materials and 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.
Keywords :
Electroencephalograph , Anesthesia , Depth
Journal title :
Astroparticle Physics
Serial Year :
2012
Link To Document :
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