P. Grace Kanmani Prince, Rani Hemamalini, U. Anitha, J. Premalatha, K. Sudheera
P. Grace Kanmani Prince1, Rani Hemamalini2, U. Anitha1, J. Premalatha1, K. Sudheera1
1Sathyabama University, Rajiv Gandhi Road, Chennai 600118.
2St. Peters College of Engineering and Technology, Avadi, Chennai 600054
Volume - 10,
Issue - 10,
Year - 2017
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.
Cite this article:
P. Grace Kanmani Prince, Rani Hemamalini, U. Anitha, J. Premalatha, K. Sudheera. Detection of seizure using EEG Signals by Supervised Learning Algorithms. Research J. Pharm. and Tech 2017; 10(10):3443-3448. doi: 10.5958/0974-360X.2017.00613.8