Seizure detection using median based feature

Author

Paulose, Anju ; Bedeeuzzaman, Mohamed

Author_Institution

Dept. of Electron. & Commun. Eng., MES Coll. of Eng., Kuttippuram, India

fYear

2014

fDate

17-18 Dec. 2014

Firstpage

328

Lastpage

332

Abstract

Epilepsy is a common neurological disorder which is difficult to treat because of its unpredictable and recurrent nature. The electroencephalogram (EEG) is a valuable tool for detecting epileptic seizures. With the aim of reducing the input feature dimensionality, a single median based feature called interquartile range (IQR) was used in this paper for the classification of normal and seizure EEG signals. Classification was done using a linear classifier and a support vector machine (SVM) classifier. Normal and seizure signals were classified with an accuracy of 71.62% and 96.57% using linear and SVM classifier respectively.

Keywords

electroencephalography; feature extraction; medical disorders; medical signal processing; neurophysiology; signal classification; support vector machines; electroencephalogram; epilepsy; epileptic seizure detection; input feature dimensionality; interquartile range; linear classifier; neurological disorder; normal EEG signal classification; recurrent nature; seizure EEG signal classification; single median based feature; support vector machine classifier; unpredictable nature; Accuracy; Electroencephalography; Entropy; Epilepsy; Feature extraction; Support vector machines; Wavelet transforms; Classification; Electroencephalogram; Epilepsy; Inter quartile range; Support Vector Machine;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Systems and Communications (ICCSC), 2014 First International Conference on

Conference_Location

Trivandrum

Print_ISBN

978-1-4799-6012-5

Type

conf

DOI

10.1109/COMPSC.2014.7032672

Filename

7032672