01995nas a2200229   4500000000100000000000100001008004100002260001200043653001200055653001900067653001000086653001900096653001300115100002400128700002700152245011500179856009800294300001000392490000600402520134300408022001401751       2016                            d  c12/201610aWavelet10aClassification10aFuzzy10aNeural Network10aMedicine1 aMansour Esmaeilpour1 aAli Reis Ali Mohammadi00aAnalyzing the EEG Signals in Order to Estimate the Depth of Anesthesia using Wavelet and Fuzzy Neural Networks  uhttp://www.ijimai.org/journal/sites/default/files/files/2016/11/ijimai20164_2_2_pdf_16114.pdf  a12-150 v43 aEstimating depth of Anesthesia in patients with the objective to administer the right dosage of drug has always attracted the attention of specialists. To study Anesthesia, researchers analyze brain waves since this is the place which is directly affected by the drug. This study aimed to estimate the depth of Anesthesia using electroencephalogram (EGG) signals, wavelet transform, and adaptive Neuro Fuzzy inference system (ANFIS). ANFIS can estimate the depth of Anesthesia with high accuracy. A set of EEG signals regarding consciousness, moderate Anesthesia, deep Anesthesia, and iso-electric point were collected from the American Society of Anesthesiologists (ASA) and PhysioNet. First, the extracted features were combined using wavelet and spectral analysis after which the target features were selected. Later, the features were classified into four categories. The results obtained revealed that the accuracy of the proposed method was 98.45%. Since the visual analysis of EEG signals is difficult, the proposed method can significantly help anesthesiologists estimate the depth of Anesthesia. Further, the results showed that ANFIS could significantly increase the accuracy of Anesthesia depth estimation. Finally, the system was deemed to be advantageous since it was also capable of updating in real-time situations as well.  a1989-1660