The seizure prediction problem has been addressed by many researchers from very different fields for more than three decades. The vision of an implantable seizure prediction device may become reality now: the first clinical study of such a device has been realized very recently and other realizations are not far behind. Despite promising first results, however, up to now there is no method allo...

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 new approach to recognize and predict succedent epileptic seizure by using single channel electroencephalogram (EEG) analysis is proposed. Spectral analysis of a brain time series of the left frontal FP1-F7 (LF) scalp location signal is devoted for seizure prediction and analysis. Important findings showing the presence of preictal spectral changes in studied brain signal are described. Spect...

Nonlinear time series analysis techniques have been proposed to detect changes in the electroencephalography dynamics prior to epileptic seizures. Their applicability in practice to predict seizure onsets is hampered by the present lack of generally accepted standards to assess their performance. We propose an analytic approach to judge the prediction performance of multivariate seizure predict...

Responsive, implantable stimulation devices to treat epilepsy are now in clinical trials. New evidence suggests that these devices may be more effective when they deliver therapy before seizure onset. Despite years of effort, prospective seizure prediction, which could improve device performance, remains elusive. In large part, this is explained by lack of agreement on a statistical framework f...

Background There is currently no formal method for predicting the range expected in an individual's seizure counts. Having access to such a prediction would be of benefit for developing more efficient clinical trials, but also for improving clinical care in the outpatient setting. Methods Using three independently collected patient diary datasets, we explored the predictability of seizure fre...

Nonlinear time series analysis techniques have been proposed to detect changes in the electroencephalography dynamics prior to epileptic seizures. Their applicability in practice to predict seizure onsets is hampered by the present lack of generally accepted standards to assess their performance. We propose an analytic approach to judge the prediction performance of multivariate seizure predict...

Commentary Efforts to predict impending seizures before they actually occur have been the focus of extensive lines of research over decades. Successful seizure prediction would offer the possibility of immediate treatment to preempt the seizure, as well as to provide an opportunity for the patient to take precautionary measures and reduce risk. There is even evidence that simply reducing the un...

BACKGROUND Seizure prediction can increase independence and allow preventative treatment for patients with epilepsy. We present a proof-of-concept for a seizure prediction system that is accurate, fully automated, patient-specific, and tunable to an individual's needs. METHODS Intracranial electroencephalography (iEEG) data of ten patients obtained from a seizure advisory system were analyzed...