Microcontroller Based Fuzzy Logic Sevofluorane Anesthesia Control System
Proceedings of the International Conference, 7th Fuzzy Days on Computational Intelligence, Theory and Applications
Fuzzy detection of EEG alpha without amplitude thresholding
Artificial Intelligence in Medicine
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In this study, power spectrum of the EEG data and the heartbeat data obtained from 25 patients has been applied to the designed neuro-fuzzy system. The designed system has composed of two parts; one is an artificial neural network and the other is a fuzzy system. A back-propagation artificial neural network has been developed which contains 53 nodes in the input layer, 27 nodes in the hidden and 1 node in the output layer. In the artificial neural network inputs, the power spectral density values corresponding 1-50Hz frequency interval of the EEG slices which has 10s of time interval, the ratio of the total of the PSD values of current EEG slice to the total PSD values of EEG slice of pre-anesthesia, the ratio of the total PSD values of the EEG data to the total PSD values of the previous EEG data, and the previous anesthetic gas ratio values have been applied and the network has been educated. At the end of the education total error has been found as 10^-^1^7. In the fuzzy system block, the ratio of current heartbeat to the previous one, the ratio of the current heartbeat to the pre-operation heartbeat, the ratio of the output of the artificial neural network to the previous applied anesthetic gas have been applied as variables and in the system output gas ratio prediction has been obtained as percentage. The designed neuro-fuzzy system has been tested by using 10 data set obtained from four different patients. In the anesthetic gas prediction according to the anesthesia level, successful results have been obtained with the designed system.