Dispersion measures and entropy for seizure detection

Author

Bedeeuzzaman, M. ; Farooq, Omar ; Khan, Yusuf U.

Author_Institution

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

fYear

2011

fDate

22-27 May 2011

Firstpage

673

Lastpage

676

Abstract

Electroencephalogram (EEG) is an important technique for detecting epileptic seizures. In this paper a method of classification of EEG signal into normal, interictal and ictal classes is presented. Statistical measures such as median absolute deviation (MAD), variance and entropy showing the dispersion and rhythmicity, were calculated for each frame of EEG signals. The classification was done using a linear classifier. The direct time domain approach adopted without resorting into any kind of transformations yields an accuracy of 100%.

Keywords

diseases; electroencephalography; entropy; medical signal processing; neurophysiology; signal classification; statistical analysis; time-domain analysis; EEG signal classification; EEG signal dispersion; EEG signal rhythmicity; MAD; direct time domain approach; dispersion measures; electroencephalogram; entropy; epileptic seizure detection; interictal EEG signal class; median absolute deviation; normal EEG signal class; statistical measures; variance; Accuracy; Artificial neural networks; Dispersion; Electroencephalography; Entropy; Epilepsy; Feature extraction; Classification; Electroencephalogram; Feature Extraction; Median Absolute Deviation;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on

Conference_Location

Prague

ISSN

1520-6149

Print_ISBN

978-1-4577-0538-0

Electronic_ISBN

1520-6149

Type

conf

DOI

10.1109/ICASSP.2011.5946493

Filename

5946493