Feature extraction and classification of EEG for automatic seizure detection

Author

Rafiuddin, Nidal ; Khan, Yusuf Uzzaman ; Farooq, Omar

Author_Institution

Dept. of Electr. Eng., Aligarh Muslim Univ., Aligarh, India

fYear

2011

fDate

17-19 Dec. 2011

Firstpage

184

Lastpage

187

Abstract

One of the many challenges in the automated detection of epileptic seizures is to draw a line of demarcation between seizure activity and non-seizure activity. To accomplish this task, identification of related features and there extraction from the EEG plays a key role. The work presented in this paper is a part of an overall effort going on to develop a new method for automated detection of seizures. A wavelet based feature extraction technique has been adopted. Statistical features, Inter-quartile range (IQR) and Median Absolute Deviation (MAD) also form part of the feature vector. The algorithm was evaluated on 23 subjects with 195 seizures. The results gave an average detection accuracy of 96.5%. The database used is the CHB-MIT scalp EEG database. All the calculations were performed on Matlab.

Keywords

electroencephalography; feature extraction; medical signal processing; CHB-MIT scalp EEG database; EEG classification; Interquartile range; Matlab; automatic seizure detection; detection accuracy; epileptic seizure; median absolute deviation; statistical feature; wavelet based feature extraction technique; Accuracy; Electroencephalography; Feature extraction; Signal processing; Support vector machine classification; Wavelet transforms;

fLanguage

English

Publisher

ieee

Conference_Titel

Multimedia, Signal Processing and Communication Technologies (IMPACT), 2011 International Conference on

Conference_Location

Aligarh

Print_ISBN

978-1-4577-1105-3

Type

conf

DOI

10.1109/MSPCT.2011.6150470

Filename

6150470