Classification and Detection of Epilepsy using Reduced Set of Extracted Features

The Electroencephalogram (EEG) signal is the non-invasive technique to examine the electrical activity of the brain and epilepsy is the chronological disorder or abnormality symptoms obtained from EEG data. The detection of this abnormality requires large number of features for the classification of...

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Bibliographic Details
Published in:	2018 5th International Conference on Signal Processing and Integrated Networks (SPIN) pp. 560 - 564
Main Authors:	Choubey, Hemant, Pandey, Alpana
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01-02-2018
Subjects:	Discrete wavelet transforms Electroencephalogram (EEG) Signal Electroencephalography Epilepsy Epileptic Seizure Detection Expected Activity Measurement (EAM) Feature extraction Fractals Higuchi Fractal Dimension (HFD) k-Nearest Neighbor (k-NN) Classification Petrosian Fractal Dimension (PFD) Signal processing Training
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Summary:	The Electroencephalogram (EEG) signal is the non-invasive technique to examine the electrical activity of the brain and epilepsy is the chronological disorder or abnormality symptoms obtained from EEG data. The detection of this abnormality requires large number of features for the classification of healthy, inter-ictal and ictal signal from the EEG signal. Epileptic seizure detection using reduced set of features is the main idea behind in this paper. Expected Activity Measurement coefficient and Hurst Exponent with Higuchi Fractal Dimension is the small set of features sufficient for the detection of epileptic seizure from EEG signal using k-NN classifier with performance parameter like Accuracy, Precision and Jaccard Coefficient.
DOI:	10.1109/SPIN.2018.8474100