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 Please use this identifier to cite or link to this item: http://hdl.handle.net/1807/25750

 Title: Brain State Classification in Epilepsy and Anaesthesia Authors: Lee, Angela Advisor: Bardakjian, Berj Department: Electrical and Computer Engineering Keywords: EEGsignal processingepilepsyanaesthesiaanesthesianeurosciencefuzzy logicentropyfuzzy c-means clusteringwaveletclassificationbrain stateseizuremembership function Issue Date: 7-Jan-2011 Abstract: Transitions between normal and pathological brain states are manifested differently in the electroencephalogram (EEG). Traditional discrimination of these states is often subject to bias and strict definitions. A fuzzy logic-based analysis can permit the classification and tracking of brain states in a non-subjective and unsupervised manner. In this thesis, the combination of fuzzy c-means (FCM) clustering, wavelet, and information theory has revealed notable frequency features in epilepsy and anaesthetic-induced unconsciousness. It was shown that entropy changes in membership functions correlate to specific epileptiform activity and changes in anaesthetic dosages. Seizure episodes appeared in the 31-39 Hz band, suggesting changes in cortical functional organization. The induction of anaesthetics appeared in the 64-72 Hz band, while the return to consciousness appeared in the 32-40 Hz band. Changes in FCM activity were associated with the concentration of anaesthetics. These results can help with the treatment of epilepsy and the safe administration of anaesthesia. URI: http://hdl.handle.net/1807/25750 Appears in Collections: MasterThe Edward S. Rogers Sr. Department of Electrical & Computer Engineering - Master theses

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