A signal processing based analysis and prediction of seizure onset in patients with epilepsy

• Main Authors: Delaviz, A. (Author), Delaviz, F. (Author), Habibi, S. (Author), Hussaini, J. (Author), Kulish, V.V (Author), Namazi, H. (Author), Ramezanpoor, S. (Author)
• Format: Article
• Language: English
• Published: Impact Journals LLC 2016
• Subjects: adult; Adult; algorithm; Algorithms; analysis; Article; controlled study; EEG signals; electroencephalogram; electroencephalography; Electroencephalography; epilepsy; Epilepsy; Epileptic seizure; female; Female; forecasting; fractal analysis; Fractal dimension; Fractals; human; Humans; Hurst exponent; major clinical study; male; Male; mathematical parameters; methodology; pathophysiology; prediction; Prediction; procedures; prognosis; Prognosis; reproducibility; Reproducibility of Results; seizure; Seizures; sensitivity and specificity; Sensitivity and Specificity; signal processing; signal processing based analysis; Signal Processing, Computer-Assisted; The Hurst exponent; time factor; Time Factors; validation study; young adult; Young Adult
• Online Access: View Fulltext in Publisher; View in Scopus

 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. © 2015. Oncotarget.