Abstract
Computer assisted processing of long-term EEG recordings is gaining a growing importance. To simplify the work of a physician, that must visually evaluate long recordings, we present a method for automatic processing of EEG based on learning classifier. This method supports the automatic search of long-term EEG recording and detection of graphoelements – signal parts with characteristic shape and defined diagnostic value. Traditional methods of detection show great percent of error caused by the great variety of non-stationary EEG. The idea of this method is to break down the signal into stationary sections called segments using adaptive segmentation and create a set of normalized discriminative features representing segments. The groups of similar patterns of graphoelements form classes used for the learning of a classifier. Weighted features are used for classification performed by modified learning classifier fuzzy k – Nearest Neighbours. Results of classification describe classes of unknown segments. The implementation of this method was experimentally verified on a real EEG with the diagnosis of epilepsy.
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Kosar, K., Lhotska, L., Krajca, V. (2004). Classification of Long-Term EEG Recordings. In: Barreiro, J.M., Martín-Sánchez, F., Maojo, V., Sanz, F. (eds) Biological and Medical Data Analysis. ISBMDA 2004. Lecture Notes in Computer Science, vol 3337. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30547-7_33
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DOI: https://doi.org/10.1007/978-3-540-30547-7_33
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