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Research Journal of Pharmacy and Technology
Year : 2017, Volume : 10, Issue : 10
First page : ( 3443) Last page : ( 3448)
Print ISSN : 0974-3618. Online ISSN : 0974-360X.
Article DOI : 10.5958/0974-360X.2017.00613.8

Detection of seizure using EEG Signals by Supervised Learning Algorithms

Prince P. Grace Kanmani1,*, Hemamalini Rani2, Anitha U.1, Premalatha J.1, Sudheera K.1

1Sathyabama University, Rajiv Gandhi Road, Chennai, 600118

2St. Peters College of Engineering and Technology, Avadi, Chennai, 600054

*Corresponding Author E-mail: coggrace05@gmail.com

Online published on 26 March, 2018.


Epileptic seizure can be detected by many ways but EEG signal prove to be the most important marker. Since EEG signal requires a strenuous effort to go through pages of recorded signal. Automatic seizure detection can be done by extracting features from the EEG signals and then feeding them to the supervised learning algorithms for classification and prediction. In this paper the features that are chosen are mean, standard deviation, skewness, kurtosis, interquartile range and mean absolute deviation. A comparative study of SVM and GRNN are done in this work and GRNN proves to be accurate for seizure detection applications.



Epileptic seizure, supervised learning, features, feature, Support vector machine(SVM), Generalized Regression Neural Network (GRNN).


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