bivariate features, obtained from multichannel electroencephalogram (eeg) recordings, quantify the relation between different brain regions. studies based on bivariate features have shown optimistic results for tackling epileptic seizure prediction problem in patients suffering from refractory epilepsy. a new bivariate approach using univariate features is proposed here. differences and ratios ...

The epileptic seizure occurs at random by impaired brain functions. An epileptic seizure can be characterized in terms of paroxysmal occurrences of synchronous oscillations. In this paper, we suggest several approaches to evaluate the hidden characteristic of the data which can form the basis for diagnosis and prediction of epileptic seizures. These methodologies are applied on electroencephalo...

Epileptic seizure prediction is a difficult problem in clinical applications, and it has the potential to significantly improve the patients' daily lives whose seizures cannot be controlled by either drugs or surgery. However, most current studies of epileptic seizure prediction focus on high sensitivity and low false-positive rate only and lack the flexibility for a variety of epileptic seizur...

During the past century, most neuroscientists have believed that epileptic seizures began abruptly in a matter of a few seconds before clinical onset. Since the late 80’s, there has been an explosion of interests in neuroscience research to predict epileptic seizures based on quantitative analyses of brain electrical activity captured by electroencephalograms (EEGs). Many research groups have d...

A statistical method for finding the optimal preictal period to be used in epileptic seizure prediction algorithms is presented. As supervised machine learning methods need labeled training samples, the adequate selection of preictal period plays a key role in the training of an efficient classifier employed in seizure prediction. The proposed method uses amplitude distribution histograms of a ...

Epilepsy is a neurological disorder marked by sudden recurrent episodes of sensory disturbance, loss of consciousness, or convulsions, associated with abnormal electrical activity in the brain. The sudden and seemingly unpredictable nature of seizures is one of the most compromising aspects of the disease epilepsy. Most epilepsy patients only spend a marginal part of their time actually having ...

Drug-resistant, multifocal, non-resectable epilepsies are among the most difficult epileptic disorders to manage. An approach to control previously uncontrollable seizures in epilepsy patients would consist of identifying seizure precursors in critical brain areas combined with delivering a counteracting influence to prevent seizure generation. Predictability of seizures with acceptable levels ...

Electroencephalographic signal is a representative signal that contains information about brain activity, which is used for the detection of epilepsy since epileptic seizures are caused by a disturbance in the electrophysiological activity of the brain. The prediction of epileptic seizure usually requires a detailed and experienced analysis of EEG. In this paper, we have introduced a statistica...

background: serum prolactin level has been used as an alternative determinant to help differentiate epileptic from non-epileptic seizures. we aimed to analyze the association between serum prolactin concentration and epileptic seizure versus febrile seizure as well as seizure duration, single versus multiple seizure attacks and time lapse between seizure onset and blood sampling.   methods: sev...