The purpose of this paper is to identify a suitable fuzzy classification model for epilepsy risk level from EEG signals. The parametric values such as energy, positive and negative peaks, sharp spikes and events are derived in each epoch of the EEG signal channels. Fuzzy techniques are used to classify the epilepsy risk levels in each epoch for all the channels, the risk level patterns obtained are found to have low values of sensitivity, performance and quality value. In order to increase the classification rate, an optimization technique is used and a quality value of 11.9 is achieved when compared to the value 6.25 achieved in the previous case. A comparison of fuzzy techniques without and with optimization is studied. The focal epilepsy problem in normal fuzzy classification is solved using this new approach. A group of six patients with known epilepsy findings are used in this study. An analytic study is undertaken for classification based on fuzzy aggregation operators. The results obtained from fuzzy classification with optimization method and aggregation operators are almost at the same level. The number of samples may be increased to improve the classification rate.
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Ra Harikumar, Sukanesh, Ra, and Sabarish Narayanan B., “Fuzzy techniques and fuzzy aggregation operators for classification of epilepsy risk level using EEG signal parameters”, Modelling, Measurement and Control C, vol. 66, pp. 43-62, 2005.