Detection Of Epileptic Seizure From EEG Signal Using Wavelet Transform And Artificial Neural Network

Apply

Detection Of Epileptic Seizure From EEG Signal Using Wavelet Transform And Artificial Neural Network

Journal Title: International Journal of engineering Research and Applications - Year 2018, Vol 8, Issue 10

Abstract

Epilepsy is the fourth most common neurological problem in which brain activities becomes abnormal, causing seizure or unusual behavior and sometimes loss of awareness. An electroencephalogram (EEG) signal is used to detect problems in electrical activity of the brain that is associated with certain brain disorders. In this paper, electroencephalogram signals were decomposed into the frequency sub-bands using DWT and set of statistical features were extracted from the sub-bands to represent the distribution of wavelet coefficients. Later, extracted features are given as an input to the neural network for classification of normal and epileptic signal. EEG signal is collected from the publically available Bonn dataset. Experimental results show that 99.8% accuracy is achieved in the classification using the proposed method.

Authors and Affiliations

M. Sornam, E. Panneer Selvam

Keywords

Design analysis of CNTFETs based Full Adder using CVSL

In this paper, the design analysis, implementation and comparison of different types of full adders is done. The adders being a very important basic logic functional block are required in many complex and high end circui...

Selective approach to efficient ethanol production using adaptation of producer Saccharomyces cerevisiae to hyperosmotic medium

The resistance to ethanol of 18 strains of alcoholic yeast Saccharomyces cerevisiae, including production strains, was studied. It is shown that yeast growth was inhibited by increasing the concentration of ethanol in th...

EP ID EP401961
DOI 10.9790/9622-0810010611.
Views 152
Downloads 0