Estimation of medicine amount used anesthesia by an artificial neural network

In this study, Elman's recurrent neural networks using Resilient Back Propagation (RP) algorithm and feed-forward neural networks using adaptive learning rate algorithm (gdx) have been compared in order to determine the depth of anesthesia in the continuation stage of anesthesia and to estimate the amount of medicine to be applied at that moment. EEG data have been recorded by being sampled once in every 2 ms. From 30 patients, 57 distinct EEG recordings have been collected prior to during anaesthesia of different levels. The applied artificial neural network is composed of three layers, namely the input layer, the middle layer and the output layer. The nonlinear activation function sigmoid (sigmoid function) has been used in the hidden layer and the output layer. Prediction has been made by means of ANN. Training and testing the ANN have been used previous anaesthesia amount, total power/normal power and total power/previous. When Elman Resilient BP and feed-forward network are compared, it is observed that resilient back propagation algorithm has generated values which are quite close to the applied anesthesia amount compared to gdx which is an adaptive learning algorithm. The system has been able to correctly purposeful responses in average accuracy of 95% of the cases. This method is also computationally fast and acceptable real-time clinical performance has been obtained.