Detecting Epileptic Seizures Using Electroencephalogram: A Novel Frequency Domain Feature Extraction Technique for Seizure Classification using Fast ANFIS

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dc.categoryConference Proceedings
dc.contributor.authorGeethalakshmi, S N
dc.date.accessioned2017-03-28T21:56:02Z
dc.date.available2017-03-28T21:56:02Z
dc.date.issued2012
dc.departmentComputer Scienceen_US
dc.description.abstractEpileptic seizures usually results in a mixture of temporal alterations in perception and behavior. Epilepsy is considered to be one of the highly frequent neurological disorders. A considerable manner for detecting and examining epileptic seizure behavior in humans is Electroencephalogram (EEC) signal examination. EEC classification is a significant process in Brain Computer Interface (BCl) that offers a new dimension in human computer interface, directly linking a computer with human thinking. Identification of the epileptic EEC signal has been performed manually. Recently, automated epileptic seizure identification with the help of EEC signals has become an active of research. This paper presents an implementation of automated epileptic EEG detection system. In this paper, frequency domain feature extraction is carried out through Fast Fourier Transform to the process of classifying EEG signals. For classification this paper uses Fast Adaptive Ncuro-Fuzzy Inference System (ANFIS) which utilize the modified Levenberg-Marquardt algorithm for learning. Experimental results show that the proposed system results in higher accuracy of classification at lesser time.en_US
dc.identifier.urihttps://ir.avinuty.ac.in/handle/avu/2237
dc.langEnglishen_US
dc.publisher.nameInternational Conference on Advances In Computing, Communications And Informaticsen_US
dc.publisher.typeInternationalen_US
dc.titleDetecting Epileptic Seizures Using Electroencephalogram: A Novel Frequency Domain Feature Extraction Technique for Seizure Classification using Fast ANFISen_US
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