A signal processing based analysis and prediction of seizure onset in patients with epilepsy
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Hamidreza Namazi1, Vladimir V. Kulish1, Jamal Hussaini2,*, Jalal Hussaini3,*, Ali Delaviz4,*, Fatemeh Delaviz5,*, Shaghayegh Habibi6,*, Sara Ramezanpoor6,*
1School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore
2Faculty of Medicine, Universiti Teknologi MARA (UiTM), Selangor, Malaysia
3Department of Otolaryngology, Faculty of Medicine, Shiraz University, Shiraz, Iran
4Faculty of Mechanical Engineering, Iran University of Science and Technology, Tehran, Iran
5Faculty of Chemical Engineering, Islamic Azad University (Fars Science and Research Branch), Shiraz, Iran
6School of Medicine, Fasa University of Medical Sciences, Fasa, Iran
*These authors have contributed equally to this work
Hamidreza Namazi, e-mail: [email protected]
Keywords: epileptic seizure, prediction, EEG signals, the Hurst exponent, fractal dimension
Received: October 16, 2015 Accepted: November 09, 2015 Published: November 17, 2015
One of the main areas of behavioural neuroscience is forecasting the human behaviour. Epilepsy is a central nervous system disorder in which nerve cell activity in the brain becomes disrupted, causing seizures or periods of unusual behaviour, sensations and sometimes loss of consciousness. An estimated 5% of the world population has epileptic seizure but there is not any method to cure it. More than 30% of people with epilepsy cannot control seizure. Epileptic seizure prediction, refers to forecasting the occurrence of epileptic seizures, is one of the most important but challenging problems in biomedical sciences, across the world. In this research we propose a new methodology which is based on studying the EEG signals using two measures, the Hurst exponent and fractal dimension. In order to validate the proposed method, it is applied to epileptic EEG signals of patients by computing the Hurst exponent and fractal dimension, and then the results are validated versus the reference data. The results of these analyses show that we are able to forecast the onset of a seizure on average of 25.76 seconds before the time of occurrence.
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