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Electroencephalogram Signals Classification by Ordered Fuzzy Decision Tree

A new algorithm for Electroencephalogram (EEG) signals classification is proposed in this paper. This classification is used for automatic detection of patients with epilepsy in a medical system for decision support. The classification algorithm is based on Ordered Fuzzy Decision Tree (OFDT) for EEG signals. The application of OFDT requires special transformation of EEG signal that is named as preliminary data transformation. This transformation extracts fundamental properties/features of EEG signals from every sample and reduces dimension of the samples. The accuracy of the proposed algorithm was evaluated and compared with other known algorithms used for EEG signal classification. This comparison showed that the algorithm proposed in this paper is comparable with existing ones and can produce better results than others.