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Query: UMLS:C0036572 (seizures)
 80,221 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

The theory of phase resetting can reveal important information about the dynamic behavior of a periodic system when a single brief stimulus is applied to that system at the appropriate time. Phase resetting studies have revealed the existence in some biological systems of a vulnerable stimulus window generating desynchronization and suppression of the activity. The objective of this study was to test the hypothesis that a "singular" stimulus could annihilate this activity. Perfusion with the high-K solution produced synchronous, quasi-periodic population bursts with inter-burst interval of ~0.8-1.5 seconds. A single 0.1 ms duration anodic pulse of programmable delay and magnitude was applied to the somatic layer of the CA3 pyramidal cells. Three types of phase-resetting behavior were observed: (1) Weak resetting with little or no effect on the timing of the subsequent burst, (2) Strong resetting where the applied current pulse delayed the next event by one time period, (3) Singular behavior where the applied pulse partially or completely suppressed the subsequent bursting. The singular stimulus parameter window, however, was very narrow making it difficult to generate the singular behavior reliably. Nevertheless, the results indicate that singularities exist for high potassium neural activity and that a well timed pulse applied with the right amplitude can suppress this activity. This study suggests that phase resetting of a population of neurons is possible for quasi-periodic interictal activity and similar techniques could be applied to the control of epileptic seizures.
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PMID:Phase resetting analysis of high potassium epileptiform activity in CA3 region of the rat hippocampus. 2144 76

High-density electrocorticogram (ECoG) electrodes are capable of recording neurophysiological data with high temporal resolution with wide spatial coverage. These recordings are a window to understanding how the human brain processes information and subsequently behaves in healthy and pathologic states. Here, we describe and implement delay differential analysis (DDA) for the characterization of ECoG data obtained from human patients with intractable epilepsy. DDA is a time-domain analysis framework based on embedding theory in nonlinear dynamics that reveals the nonlinear invariant properties of an unknown dynamical system. The DDA embedding serves as a low-dimensional nonlinear dynamical basis onto which the data are mapped. This greatly reduces the risk of overfitting and improves the method's ability to fit classes of data. Since the basis is built on the dynamical structure of the data, preprocessing of the data (e.g., filtering) is not necessary. We performed a large-scale search for a DDA model that best fit ECoG recordings using a genetic algorithm to qualitatively discriminate between different cortical states and epileptic events for a set of 13 patients. A single DDA model with only three polynomial terms was identified. Singular value decomposition across the feature space of the model revealed both global and local dynamics that could differentiate electrographic and electroclinical seizures and provided insights into highly localized seizure onsets and diffuse seizure terminations. Other common ECoG features such as interictal periods, artifacts, and exogenous stimuli were also analyzed with DDA. This novel framework for signal processing of seizure information demonstrates an ability to reveal unique characteristics of the underlying dynamics of the seizure and may be useful in better understanding, detecting, and maybe even predicting seizures.
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PMID:Delay Differential Analysis of Seizures in Multichannel Electrocorticography Data. 2877 20

In this paper a nonlinear filtering algorithm for count time series is developed that takes the non-negativity of the data into account and preserves positive definiteness of the covariance matrices of the model. For this purpose, a recently proposed variant of Kalman Filtering based on Singular Value Decomposition is incorporated into Iterative Extended Kalman Filtering, in order to estimate the states of a nonlinear state space model. The resulting algorithm is applied to the evaluation and design of therapies for patients suffering from Myoclonic Astatic Epilepsy, employing time series of daily seizure rate. The analysis provides a decision whether for a specific patient a particular anti-epileptic drug is increasing or reducing the seizure rate. Through a simulation study the proposed algorithm is validated. Additionally, for clinical data results obtained by the proposed algorithm are compared with the results from a Cox-Stuart trend test as well as with the visual assessment of experienced pediatric epileptologists.
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PMID:SVD Square-root Iterated Extended Kalman Filter for Modeling of Epileptic Seizure Count Time Series with External Inputs. 3194 73