Isfahan University of Medical Sciences

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Estimating the Depth of Anesthesia by Applying Sub Parameters to an Artificial Neural Network During General Anesthesia Publisher



Ghanatbari M1 ; Mehri Dehnavi AR1 ; Rabbani H1 ; Mahoori AR2
Authors

Source: Final Program and Abstract Book - 9th International Conference on Information Technology and Applications in Biomedicine, ITAB 2009 Published:2009


Abstract

This paper presents two artificial neural network (ANN) structures to estimate the depth of anesthesia (DOA). First, a clinical study involved on 33 patients is proposed to construct reference data and also to compare the results with DIS monitor (Aspect Medical, Vista), which represents satisfactory correlation with clinical assessments. Secondly, to extract features from electroencephalogram (EEG) signals, we extract some features in frequency and time domain as well as in wavelet (Daubechies) domain. Finally, to integrate EEG features to estimate DOA, ANNs based on back propagation (DP) algorithm are proposed. Since each of the proposed features may has good performance only for a specific range of DOA, this model proved to have good prediction properties, and the output of the proposed ANN has a high correlation with the output of the BIS index. ©2009 IEEE.