Department of Anesthesiology,
Department of Neurosurgery, Shohada-e-Tajrish Hospital,
Tracheal Disease Research Center
Lung Transplantation Research Center, NRITLD, Shahid Beheshti University M.C., TEHRAN-IRAN.
Background: Evaluation of depth of anesthesia is especially important in adequate and efficient management of patients. Clinical assessment of EEG in the operating room is one of the major difficulties in this field. This study aims to find the most valuable EEG parameters in prediction of the depth of anesthesia in different stages. Materials and Methods: EEG data of 30 patients with same anesthesia protocol (total intravenous anesthesia) were recorded in all anesthetic stages in Shohada-e-Tajrish Hospital. Quantitative EEG characteristics are classified into 4 categories of time, frequency, bispectral and entropy-based characteristics. Their sensitivity, specificity and accuracy in determination of depth of anesthesia were yielded by comparing them with the recorded reference signals in awake, light anesthesia, deep anesthesia and brain dead patients. Results: Time parameters had low accuracy in prediction of the depth of anesthesia. The accuracy rate was 75% for burst suppression response. This value was higher for frequency- based characteristics and the best results were obtained in ß spectral power (accuracy: 88.9%). The accuracy rate was 89.9% for synch fast slow bispectral characteristics. The best results were obtained from entropy-based characteristics with the accuracy of 99.8%. Conclusion: Analysis of the entropy-based characteristics had a great value in predicting the depth of anesthesia. Generally, due to the low accuracy of each single parameter in prediction of the depth of anesthesia, we recommend multiple characteristics analysis with greater focus on entropy-based characteristics. (Tanaffos 2009; 8(2): 46-53)