Wavelet-based Gaussian-mixture hidden Markov model for the detection of multistage seizure dynamics: a proof-of-concept study.

Background: Epilepsy is a common neurological disorder characterized by recurrent electrophysiological activities, known as seizures. Without the appropriate detection strategies, these seizure episodes can dramatically affect the quality of life for those afflicted. The rationale of this study is to develop an unsupervised algorithm for the detection of seizure states so that it may be implemented along with potential intervention strategies.

Mechanisms of morphine enhancement of spontaneous seizure activity.

Background: High-dose opioid therapy can precipitate seizures; however, the mechanism of such a dangerous adverse effect remains poorly understood. The aim of our study was to determine whether the neuroexcitatory activity of high-dose morphine is mediated by selective stimulation of opioid receptors.

Methods: Mice hippocampi were resected intact and bathed in low magnesium artificial cerebrospinal fluid to induce spontaneous seizure-like events recorded from CA1 neurons.

Towards real-time in-implant epileptic seizure prediction.

We present an architecture of an epileptic seizure prediction system suitable for an implantable implementation. The microsystem comprises a neural interface, a spectral analysis processor and an artificial neural network (ANN). The neural interface and the spectral analysis processor have been prototyped in a 0.

Prevention of Spontaneous Seizure-like Events in Both in-silico and in-vitro Epilepsy Models.

In this paper, we apply the small perturbation control strategy for the prevention of seizure-like events (SLEs) characterized as lower dimensional possibly rhythmic (LPR) activities in both the coupled oscillators in-silico model and the in-vitro low magnesium rat hippocampal slice model. Utilizing the wavelet artificial neural network (WANN), state transitions towards SLEs can be predicted. Successful suppression of SLEs was achieved when brief control perturbations were applied to the field coupling portals of the coupled oscillators model and to the mossy fibers via extracellular field stimulating electrode, respectively.

Lag synchrony measures dynamical processes underlying progression of seizure states.

We investigate the dynamics of bursting behavior in an intact hippocampal preparation using causal entropy, an adaptive measure of lag synchrony. This analysis, together with a heuristic model of coupled bursting networks, separates experimentally observed bursting dynamics into two dynamical regimes, when bursting is driven by (1) the intranetwork dynamics of a single region, or (2) internetwork feedback between spatially disjoint neural populations. Our results suggest that the abrupt transition between these two states heralds the gradual desynchronization of bursting activity.

Dynamics of epileptic phenomena determined from statistics of ictal transitions.

In this paper, we investigate the dynamical scenarios of transitions between normal and paroxysmal state in epilepsy. We assume that some epileptic neural network are bistable i.e.

Online prediction of onsets of seizure-like events in hippocampal neural networks using wavelet artificial neural networks.

It has been previously shown that wavelet artificial neural networks (WANNs) are able to classify the different states of epileptiform activity and predict the onsets of seizure-like events (SLEs) by offline processing (Ann. Biomed. Eng.