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...

Research in automatic analysis of EEG for supporting the diagnosis of epileptic seizures took pace in the 1970s. Prior et al [45] suggested the use of a device called cerebral function monitor to demarcate generalized tonic-clonic seizures. These could be identified as a large increase in EEG amplitude followed by an observable decrease and by large EMG activity. Method described by Ives et al ...

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...

OBJECTIVE Retrospective evaluation and comparison of performances of a multivariate method for seizure detection and prediction on simultaneous long-term EEG recordings from scalp and intracranial electrodes. METHODS Two multivariate techniques based on simulated leaky integrate-and-fire neurons were investigated in order to detect and predict seizures. Both methods were applied and assessed ...

BACKGROUND Genetic rat models for childhood absence epilepsy have become instrumental in developing theories on the origin of absence epilepsy, the evaluation of new and experimental treatments, as well as in developing new methods for automatic seizure detection, prediction, and/or interference of seizures. METHOD Various methods for automated off and on-line analyses of ECoG in rodent model...

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 ...

Great effort has been made toward defining and characterizing the pre-ictal state. Many studies have pursued the idea that there are recognizable electrographic (EEG-based) features which occur before overt clinical seizure activity. However, development of reliable EEG-based seizure detection and prediction algorithms has been difficult. In this review, we discuss the concepts of seizure detec...

Epilepsy is a common neurological disorder affecting more than 1.5 percent worldwide. Twenty percent of epilepsies are drug resistant. Therefore, early detection of prediction of epileptic seizures is of prime significance to decrease the burdens of the disease. There is strong evidence indicating seizures develop minutes to hours before clinical onset. This change is based on quantitative stud...

When dealing with seizure detection/prediction problems, there are three main performance metrics that must be optimized: false positive rate, false negative rate, detection delay or, if the problem is seizure prediction, it is desirable to obtain the greatest prediction time achievable. Tuning specific extracted features to individual patients can lead to improved results. The processing windo...

OBJECTIVE Research in seizure prediction from intracranial EEG has highlighted the usefulness of bivariate measures of brainwave synchronization. Spatio-temporal bivariate features are very high-dimensional and cannot be analyzed with conventional statistical methods. Hence, we propose state-of-the-art machine learning methods that handle high-dimensional inputs. METHODS We computed bivariate...