Automatic EEG interpretation adaptable to individual electroencephalographer using artificial neural network

In view of the fact that the visual EEG interpretation could be a subjective task and may vary among the electroencephalographers, the main objective of this study was to develop an automatic EEG interpretation system which is adaptable to each electroencephalographer. The system adapted to each electroencephalographer would bring a close automatic EEG interpretation to that done by the electroencephalographer's visual interpretation. The adaptable automatic EEG interpretation was accomplished by using the constructive neural network with forgetting factor. The artificial neural network was constructed so as to give the integrative interpretation of the EEG based on the intermediate judgment of 13 items that characterized the visual interpretation. The developed method was evaluated based on the visually inspected EEG data of 37 patients by electroencephalographer-A and the data of 20 patients by electroencephalographer-B. The adapted ANN showed good agreement with each electroencephalographer's visual inspection. The proposed automatic EEG interpretation by use of the ANN can be a powerful assistant tool for individual electroencephalographers for their EEG interpretation. Copyright (C) 2001 John Wiley Sons, Ltd.