Authors: Rüştü Güntürkün
Publish Date: 2009/02/12
Volume: 34, Issue: 4, Pages: 479-484
Abstract
In this study Elman recurrent neural networks have been defined by using conjugate gradient algorithm in order to determine the depth of anesthesia in the continuation stage of the anesthesia and to estimate the amount of medicine to be applied at that moment The feed forward neural networks are also used for comparison The conjugate gradient algorithm is compared with back propagation BP for training of the neural Networks The applied artificial neural network is composed of three layers namely the input layer the hidden layer and the output layer The nonlinear activation function sigmoid sigmoid function has been used in the hidden layer and the output layer EEG data has been recorded with Nihon Kohden 9200 brand 22channel EEG device The international 8channel bipolar 10–20 montage system 8 TBb system has been used in assembling the recording electrodes EEG data have been recorded by being sampled once in every 2 milliseconds The artificial neural network has been designed so as to have 60 neurons in the input layer 30 neurons in the hidden layer and 1 neuron in the output layer The values of the power spectral density PSD of 10second EEG segments which correspond to the 1–50 Hz frequency range the ratio of the total power of PSD values of the EEG segment at that moment in the same range to the total of PSD values of EEG segment taken prior to the anesthesia
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