Objective: This study proposes a new approach for offline seizure detection in intracranial (subdural) electroencephalogram recordings using nonlinear decision functions. It implements well-established features that are designed to deal with complex signals, such as brain recordings, and proposes a two-dimensional (2D) domain of analysis that overcomes the dilemma faced with the selection of em...

Wong S, Gardner AB, Krieger AM, Litt B. A stochastic framework for evaluating seizure prediction algorithms using hidden Markov models. J Neurophysiol 97: 2525–2532, 2007. First published October 4, 2006; doi:10.1152/jn.00190.2006. Responsive, implantable stimulation devices to treat epilepsy are now in clinical trials. New evidence suggests that these devices may be more effective when they de...

One of epileptology's fundamental aims is the formulation of a universal, internally consistent seizure definition. To assess this aim's feasibility three signal analysis methods were applied to a seizure time series and performance comparisons were undertaken among them and with respect to a validated algorithm. One of the methods uses a Fisher's matrix weighted measure of the rate of paramete...

In this paper it is shown that Reservoir Computing can be successfully applied to perform real-time detection of epileptic seizures in Electroencephalograms (EEGs). Absence and tonic-clonic seizures are detected on intracranial EEG coming from rats. This resulted in an area under the Receiver Operating Characteristics (ROC) curve of more than 0.99 on the data that was used. For absences an aver...

Seizures are commonly encountered in the setting of brain injury in neurologic critical care. Though seizure prophylaxis with the use of antiepileptic drugs is frequently utilized in variety of brain injury paradigms, it is often not based on evidence and is controversial. Significant difficulties arise from interpretation of supporting literature due to lack of definitions for early-vs.-late-s...

Traditional seizure detection algorithms act on single channels ignoring the synchronously recorded, inherently interdependent multichannel nature of EEG. However, the spatial distribution and evolution of the ictal pattern is a crucial characteristic of the seizure. Two different approaches aiming at including such structural information into the data representation are presented in this paper...

The electroencephalogram (EEG), which has been in clinical use for over 70 years, is still an essential tool for diagnosis of neural functioning (Kennett, 2012). Well-known applications of EEGs include identification of epilepsy and epileptic seizures, anoxic and hypoxic damage to the brain, and identification of neural disorders such as hemorrhagic stroke, ischemia and toxic metabolic encephal...

we propose a novel approach for detecting precursors to epileptic seizures in intracranial electroencephalograms (ieeg), which is based on the analysis of system dynamics. in the proposed scheme, the largest lyapunov exponent of the discrete wavelet packet transform (dwpt) of the segmented eeg signals is considered as the discriminating features. such features are processed by a support vector ...

Acquired epilepsy (i.e., after an insult to the brain) is often considered to be a progressive disorder, and the nature of this hypothetical progression remains controversial. Antiepileptic drug treatment necessarily confounds analyses of progressive changes in human patients with acquired epilepsy. Here, we describe experiments testing the hypothesis that development of acquired epilepsy begin...