Research Area:  Machine Learning

Abstract:

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.

Keywords:  
Classification
Detection Of Epilepsy
Extracted Features
Machine Learning
Deep Learning

Author(s) Name:  Hemant Choubey; Alpana Pandey

Publisher name:  IEEE

DOI:  10.1109/SPIN.2018.8474100

Volume Information:  

Paper Link:   https://ieeexplore.ieee.org/abstract/document/8474100