Time dependencies in the occurrences of epileptic seizures

A new method of analysis, developed within the framework of nonlinear dynamics, is applied to patient recorded time series of the occurrence of epileptic seizures. These data exhibit broad band spectra and generally have no obvious structure. The goal is to detect hidden internal dependencies in the data without making any restrictive assumptions, such as linearity, about the structure of the underlying system. The basis of our approach is a conditional probabilistic analysis in a phase space reconstructed from the original data. The data, recorded from patients with intractable epilepsy over a period of 1-3 years, consist of the times of occurrences of hundreds of partial complex seizures. Although the epileptic events appear to occur independently, we show that the epileptic process is not consistent with the rules of a homogeneous Poisson process or generally with a random (IID) process. More specifically, our analysis reveals dependencies of the occurrence of seizures on the occurrence of preceding seizures. These dependencies can be detected in the interseizure interval data sets as well as in the rate of seizures per time period. We modeled patient's inaccuracy in recording seizure events by the addition of uniform white noise and found that the detected dependencies are persistent after addition of noise with standard deviation as great as 1/3 of the standard deviation of the original data set. A linear autoregressive analysis fails to capture these dependencies or produces spurious ones in most of the cases.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/31879/1/0000830.pd

Quantification of depth of anesthesia by nonlinear time series analysis of brain electrical activity

We investigate several quantifiers of the electroencephalogram (EEG) signal with respect to their ability to indicate depth of anesthesia. For 17 patients anesthetized with Sevoflurane, three established measures (two spectral and one based on the bispectrum), as well as a phase space based nonlinear correlation index were computed from consecutive EEG epochs. In absence of an independent way to determine anesthesia depth, the standard was derived from measured blood plasma concentrations of the anesthetic via a pharmacokinetic/pharmacodynamic model for the estimated effective brain concentration of Sevoflurane. In most patients, the highest correlation is observed for the nonlinear correlation index D*. In contrast to spectral measures, D* is found to decrease monotonically with increasing (estimated) depth of anesthesia, even when a "burst-suppression" pattern occurs in the EEG. The findings show the potential for applications of concepts derived from the theory of nonlinear dynamics, even if little can be assumed about the process under investigation.Comment: 7 pages, 5 figure

Quantitative analysis by renormalized entropy of invasive electroencephalograph recordings in focal epilepsy

Invasive electroencephalograph (EEG) recordings of ten patients suffering from focal epilepsy were analyzed using the method of renormalized entropy. Introduced as a complexity measure for the different regimes of a dynamical system, the feature was tested here for its spatio-temporal behavior in epileptic seizures. In all patients a decrease of renormalized entropy within the ictal phase of seizure was found. Furthermore, the strength of this decrease is monotonically related to the distance of the recording location to the focus. The results suggest that the method of renormalized entropy is a useful procedure for clinical applications like seizure detection and localization of epileptic foci.Comment: 10 pages, 5 figure

Seizure states identification in experimental epilepsy using gabor atom analysis

Background: Epileptic seizures evolve through several states, and in the process the brain signals may change dramatically. Signals from different states share similar features, making it difficult to distinguish them from a time series; the goal of this work is to build a classifier capable of identifying seizure statesbased on time frequency features taken from short signal segments.Methods: There are different amounts of frequency components within each Time Frequency window foreach seizure state, referred to as the Gabor atom density. Taking short signal segments from the differentstates and decomposing them into their atoms, the present paper suggests that is possible to identifyeach seizure state based on the Gabor atom density. The brain signals used in this work were taken for a database of intracranial recorded seizures from the Kindling model.Results: The findings suggest that short signal segments have enough information to be used to derivea classifier able to identify the seizure states with reasonable confidence, particularly when used withseizures from the same subject. Achieving average sensitivity values between 0.82 and 0.97, and areaunder the curve values between 0.5 and 0.9. Conclusions: The experimental results suggest that seizure states can be revealed by the Gabor atom density; and combining this feature with the epoch s energy produces an improved classifier. These results are comparable with the recently published on state identification. In addition, considering that the order of seizure states is unlikely to change, these results are promising for automatic seizure state classification.Thanks are extended to the Hospital Universitario de Valencia, for sharing their signal database and to Francisco Sancho for his support. Finally, thanks are given to Dr. Luis N. Coria for his support and the proofreading support. Partial funding for this work was provided by CONACYT Basic Science Research Project No. 178323, DGEST (Mexico) Research Projects No. 5149.13-P, 5414.14-P and TIJ-ING-2012-110, and IRSES project ACoBSEC financed by the European Commission.Sotelo Orozco, A.; Guijarro Estelles, ED.; Trujillo, L. (2015). Seizure states identification in experimental epilepsy using gabor atom analysis. Journal of Neuroscience Methods. 241:121-131. https://doi.org/10.1016/j.jneumeth.2014.12.001S12113124

Modeling Brain Resonance Phenomena Using a Neural Mass Model

Stimulation with rhythmic light flicker (photic driving) plays an important role in the diagnosis of schizophrenia, mood disorder, migraine, and epilepsy. In particular, the adjustment of spontaneous brain rhythms to the stimulus frequency (entrainment) is used to assess the functional flexibility of the brain. We aim to gain deeper understanding of the mechanisms underlying this technique and to predict the effects of stimulus frequency and intensity. For this purpose, a modified Jansen and Rit neural mass model (NMM) of a cortical circuit is used. This mean field model has been designed to strike a balance between mathematical simplicity and biological plausibility. We reproduced the entrainment phenomenon observed in EEG during a photic driving experiment. More generally, we demonstrate that such a single area model can already yield very complex dynamics, including chaos, for biologically plausible parameter ranges. We chart the entire parameter space by means of characteristic Lyapunov spectra and Kaplan-Yorke dimension as well as time series and power spectra. Rhythmic and chaotic brain states were found virtually next to each other, such that small parameter changes can give rise to switching from one to another. Strikingly, this characteristic pattern of unpredictability generated by the model was matched to the experimental data with reasonable accuracy. These findings confirm that the NMM is a useful model of brain dynamics during photic driving. In this context, it can be used to study the mechanisms of, for example, perception and epileptic seizure generation. In particular, it enabled us to make predictions regarding the stimulus amplitude in further experiments for improving the entrainment effect