G. Geetha
ABSTRACT
Epileptic 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 (EEG) signal examination. EEG classification is a significant process in Brain Computer Interface (BCI) that offers a new dimension in human computer interface, directly linking a computer with human thinking. Identification of the epileptic EEG signal has been performed manually. Recently, automated epileptic seizure identification with the help of EEG 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 Neuro-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.
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- Detecting epileptic seizures using electroencephalogram: a novel frequency domain feature extraction technique for seizure classification using fast ANFIS
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