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2020 | OriginalPaper | Chapter

An Automated System for Epileptic Seizure Detection Using EEG

Authors : Bilal Alam Khan, Anam Hashmi, Omar Farooq

Published in: Advances in Data and Information Sciences

Publisher: Springer Singapore

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Abstract

Epileptic seizures are usually investigated using EEG. The dynamic and statistical properties of brain waves of an individual with seizure are different from a normal person’s brain waves. This paper exploits these underlying properties of EEG using Lyapunov exponent and approximate entropy and proposes a novel statistical feature namely Gini’s coefficient. In this paper, we propose an automated system for detecting seizure using statistical and machine learning algorithm. The data used was publicly available with five different classes (normal to seizure). Linear discriminant analysis (LDA) was used to classify the extracted features. The proposed method gives the best accuracy of 100% in detecting seizure from the EEG.

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Metadata
Title
An Automated System for Epileptic Seizure Detection Using EEG
Authors
Bilal Alam Khan
Anam Hashmi
Omar Farooq
Copyright Year
2020
Publisher
Springer Singapore
Book
Advances in Data and Information Sciences

Print ISBN: 978-981-15-0693-2

Electronic ISBN: 978-981-15-0694-9

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-981-15-0694-9_15