Ayman M. Mansour, Mohammad A Obeidat, Murad Al-Aqtash, "Intelligent Classifiers of EEG Signals for Epilepsy Detection," WSEAS Transactions on Signal Processing, vol. 15, pp. 106-113, 2019, DOI:
Ayman M. Mansour, Mohammad A Obeidat, Murad Al-Aqtash. Intelligent Classifiers of EEG Signals for Epilepsy Detection.
WSEAS Transactions on Signal Processing. 2019;15:106-113.
WSEAS Transactions on Signal Processing
Print ISSN: 1790-5052, E-ISSN: 2224-3488
Volume 15, 2019
Intelligent Classifiers of EEG Signals for Epilepsy Detection
Authors: , ,
Abstract: In this paper a novel methodology is developed to classify the time series EEG signals, and to apply that methodology to the analysis of physiological signals recorded from epileptic patients for seizure analysis depending on EEG signal. The developed system uses data mining techniques as classification tool based on standard deviation, entropy, and power spectral density resulted of EEG signal components resulted from discrete wavelet transform. The work has been tested on real patient EEG signals using Weka software. This approach offers a good treatment of seizure detection.
Keywords:
Epilepsy, Standard Deviation, Entropy, Power Spectrum density, Discrete Time Wavelet Transform.
Pages: 106-113 WSEAS Transactions on Signal Processing, ISSN / E-ISSN: 1790-5052 / 2224-3488, Volume 15, 2019, Art. #13 DOI: *As the DOI is a unique identifier, it is already available in the pdf version.
**The DOI link will be activated in the first midst of January 2026.
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