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Klaus Lehnertz

Publications and source records attributed to Klaus Lehnertz.

At least 19 recordsLinked to original sources
PubMed2006

Assessing seizure dynamics by analysing the correlation structure of multichannel intracranial EEG.

Epileptic seizures are commonly characterized as 'hypersynchronous states'. This habit is doubly misleading, because seizures are not necessarily synchronous and are not unchanging 'states' but dynamic processes. Here the temporal evolution of the correlation structure in the course of 100 focal onset seizures of 60 patients recorded by intracranial multichannel EEG was assessed. To this end a multivariate method was applied that at its core consists of computing the eigenvalue spectrum of the zero-lag correlation matrix of a short sliding window. Our results show that there are clearly observable and statistically significant changes of the correlation structure of focal onset seizures. Specifically, these changes indicate that the zero-lag correlation of multi-channel EEG either remains approximately unchanged or-especially in the case of secondary generalization-decreases during the first half of the seizures. Then correlation gradually increases again before the seizures terminate. This development was qualitatively independent of the anatomical location of the seizure onset zone and therefore seems to be a generic property of focal onset seizures. We suggest that the decorrelation of EEG activity is due to the different propagation times of locally synchronous ictal discharges from the seizure onset zone to other brain areas. Furthermore we speculate that the increase of correlation during the second half of the seizures may be causally related to seizure termination.

Brain↗
PubMed2006

Seizure prediction: the long and winding road.

The sudden and apparently unpredictable nature of seizures is one of the most disabling aspects of the disease epilepsy. A method capable of predicting the occurrence of seizures from the electroencephalogram (EEG) of epilepsy patients would open new therapeutic possibilities. Since the 1970s investigations on the predictability of seizures have advanced from preliminary descriptions of seizure precursors to controlled studies applying prediction algorithms to continuous multi-day EEG recordings. While most of the studies published in the 1990s and around the turn of the millennium yielded rather promising results, more recent evaluations could not reproduce these optimistic findings, thus raising a debate about the validity and reliability of previous investigations. In this review, we will critically discuss the literature on seizure prediction and address some of the problems and pitfalls involved in the designing and testing of seizure-prediction algorithms. We will give an account of the current state of this research field, point towards possible future developments and propose methodological guidelines for future studies on seizure prediction.

Algorithms↗
PubMed2006

Improved spatial characterization of the epileptic brain by focusing on nonlinearity.

An advanced characterization of the complicated dynamical system brain is one of science's biggest challenges. Nonlinear time series analysis allows characterizing nonlinear dynamical systems in which low-dimensional nonlinearity gives rise to complex and irregular behavior. While several studies indicate that nonlinear methods can extract valuable information from neuronal dynamics, others doubt their necessity and conjecture that the same information can be obtained using classical linear techniques. To address this issue, we compared these two concepts, but included furthermore a combination of nonlinear measures with surrogates, an approach that has been designed to specifically focus on nonlinearity. As a benchmark we used the discriminative power to detect the seizure-generating hemisphere in medically intractable mesial temporal lobe epilepsy. We analyzed intracranial electroencephalographic recordings from the seizure-free interval of 29 patients. While the performance of both linear and nonlinear measures was weak, if not insignificant, a very high performance was obtained by the use of surrogate-corrected measures. Focusing on nonlinearity by using a combination of nonlinear measures with surrogates appears as the key to a successful characterization of the spatial distribution of the epileptic process.

Electroencephalography↗
PubMed2006

Seizure anticipation: from algorithms to clinical practice.

PURPOSE OF REVIEW: Our understanding of the mechanisms that lead to the occurrence of epileptic seizures is rather incomplete. If it were possible to identify preictal precursors from the EEG of epilepsy patients, therapeutic possibilities could improve dramatically. Studies on seizure prediction have advanced from preliminary descriptions of preictal phenomena via proof of principle studies and controlled studies to studies on continuous multi-day recordings. RECENT FINDINGS: Following mostly promising early reports, recent years have witnessed a debate over the reproducibility of results and suitability of approaches. The current literature is inconclusive as to whether seizures are predictable by prospective algorithms. Prospective out-of-sample studies including a statistical validation are missing. Nevertheless, there are indications of a superior performance for approaches characterizing relations between different brain regions. SUMMARY: Prediction algorithms must be proven to perform better than a random predictor before prospective clinical trials involving seizure intervention techniques in patients can be justified.

Algorithms↗
PubMed2005

A distributed computing system for multivariate time series analyses of multichannel neurophysiological data.

We present a client-server application for the distributed multivariate analysis of time series using standard PCs. We here concentrate on analyses of multichannel EEG/MEG data, but our method can easily be adapted to other time series. Due to the rapid development of new analysis techniques, the focus in the design of our application was not only on computational performance, but also on high flexibility and expandability of both the client and the server programs. For this purpose, the communication between the server and the clients as well as the building of the computational tasks has been realized via the Extensible Markup Language (XML). Running our newly developed method in an asynchronous distributed environment with random availability of remote and heterogeneous resources, we tested the system's performance for a number of different univariate and bivariate analysis techniques. Results indicate that for most of the currently available analysis techniques, calculations can be performed in real time, which, in principle, allows on-line analyses at relatively low cost.

Algorithms↗
PubMed2005

Estimating phase synchronization in dynamical systems using cellular nonlinear networks.

We propose a method for estimating phase synchronization between time series using the parallel computing architecture of cellular nonlinear networks (CNN's). Applying this method to time series of coupled nonlinear model systems and to electroencephalographic time series from epilepsy patients, we show that an accurate approximation of the mean phase coherence R--a bivariate measure for phase synchronization--can be achieved with CNN's using polynomial-type templates.

Action Potentials↗
PubMed2005

On the predictability of epileptic seizures.

OBJECTIVE: An important issue in epileptology is the question whether information extracted from the EEG of epilepsy patients can be used for the prediction of seizures. Several studies have claimed evidence for the existence of a pre-seizure state that can be detected using different characterizing measures. In this paper, we evaluate the predictability of seizures by comparing the predictive performance of a variety of univariate and bivariate measures comprising both linear and non-linear approaches. METHODS: We compared 30 measures in terms of their ability to distinguish between the interictal period and the pre-seizure period. After completely analyzing continuous inctracranial multi-channel recordings from five patients lasting over days, we used ROC curves to distinguish between the amplitude distributions of interictal and preictal time profiles calculated for the respective measures. We compared different evaluation schemes including channelwise and seizurewise analysis plus constant and adaptive reference levels. Particular emphasis was placed on statistical validity and significance. RESULTS: Univariate measures showed statistically significant performance only in a channelwise, seizurewise analysis using an adaptive baseline. Preictal changes for these measures occurred 5-30 min before seizures. Bivariate measures exhibited high performance values reaching statistical significance for a channelwise analysis using a constant baseline. Preictal changes were found at least 240 min before seizures. Linear measures were found to perform similar or better than non-linear measures. CONCLUSIONS: Results provide statistically significant evidence for the existence of a preictal state. Based on our findings, the most promising approach for prospective seizure anticipation could be a combination of bivariate and univariate measures. SIGNIFICANCE: Many measures reported capable of seizure prediction in earlier studies are found to be insignificant in performance, which underlines the need for statistical validation in this field.

Algorithms↗
PubMed2005

The First International Collaborative Workshop on Seizure Prediction: summary and data description.

The First International Collaborative Workshop on Seizure Prediction was held at the Department of Epileptology, University of Bonn, in Bonn, Germany on April 24-28, 2002. Organized by the Universities of Pennsylvania and Bonn, and funded by grants from the American Epilepsy Society, the German Section of the International League against Epilepsy, and the German Section of the International Federation of Clinical Neurophysiology, the workshop was attended by 51 researchers from 16 centers in seven countries. There were four major goals for the workshop: (1) to host a one-day didactic session on the science of seizure prediction, with lectures by leaders in the field; (2) to assess the current state of the field by applying current methods used to predict seizures to a shared set of continuous intracranial EEG data and discussing the strengths and weaknesses of each approach; (3) to establish a consensus on minimal data requirements, a common nomenclature, and objective methods for comparing system performance across platforms and laboratories for seizure prediction research; and most importantly (4) to establish a multi-laboratory, international working group dedicated to understanding seizure generation and making on-line, prospective seizure prediction a reality. Following the didactic course, each participating group presented their results, after applying their seizure prediction methods to five common data sets agreed upon in advance and distributed before the meeting. What follows is a description of the shared data set used for analysis, a summary of the major discussion points from the workshop, and points of consensus among the group. The brief discussion serves as a common introduction to the research papers that follow in this issue, and the description of the shared data is referenced in each of these papers. Participants in the workshop are listed at the end of the Conclusions section, in alphabetical order.

Consensus↗
PubMed2005

Phase/amplitude reset and theta-gamma interaction in the human medial temporal lobe during a continuous word recognition memory task.

We analyzed intracranial electroencephalographic (EEG) recordings from the medial temporal lobes of 12 epilepsy patients during a continuous word recognition paradigm, contrasting trials of correctly recognized repeated words (hits) and correctly identified new words (correct rejections). Using a wavelet-based analysis, we investigated how power changes and phase clustering in different frequency bands contribute to the averaged event-related potentials (ERPs). In addition, we analyzed the actual mean phases of the different oscillations. Our analyses yielded the following results: (1) power changes contributed significantly only to the late components of the ERPs (>400 ms) (2) earlier ERP components were produced by a stimulus-related broad-band phase and amplitude reset of ongoing oscillatory activity about 190 ms after stimulus onset that involved not only the theta band, but also covered alpha and lower beta band frequencies (3) phase and amplitude reset occurred during an epoch of increased phase entrainment over time that lasted for about two oscillation periods for all involved frequencies and was more pronounced for correct rejections than for hits. The broad-band phase and amplitude reset was observed for both hits and correct rejections, and therefore, did not appear to support a specific cognitive function, but rather to act as a general facilitating factor for the processes involved in this memory task. Further analyses of synchronization between oscillations and power changes in different frequency bands revealed a task-dependent modulation of gamma activity by the entrained theta cycle, a mechanism potentially related to memory encoding and retrieval in the rhinal cortex and hippocampus, respectively.

Adult↗
PubMed2005

EEG analysis with nonlinear excitable media.

The detection of patterns embedded within a complex, nonstationary, and noisy background activity is a crucial and important task in EEG analysis. The authors present a biologically inspired, analog approach to EEG analysis that is conceptually different from a variety of statistical approaches currently used. A nonlinear, excitable, spatially extended medium that is composed of diffusively coupled model neurons is considered. When EEG recordings are applied as local perturbations to such an excitable neural tissue, the induced transient changes in the dynamics of the perturbed system can be regarded as an instantaneous characterization of transient processes in the brain reflected by the EEG, e.g., in the form of a sequence of correlated dynamical events (patterns). Nonlinear excitable media can be implemented in form of an array of locally coupled integrated analog nonlinear electrical circuits called cellular neural networks, which represent a next evolutionary step in the parallel analog computer architecture. Using cellular neural networks, the authors show that the concept of signal-induced pattern generation allows an almost instantaneous and unsupervised detection of seizure onsets in EEG recordings. In addition, they show that a cellular neural network can be trained in a supervised way to approximate the degree of synchronization in EEG recordings. The resulting pattern-recognition device may be suitable for the prediction of epileptic seizures.

Animals↗
PubMed2004

Measure profile surrogates: a method to validate the performance of epileptic seizure prediction algorithms.

In a growing number of publications it is claimed that epileptic seizures can be predicted by analyzing the electroencephalogram (EEG) with different characterizing measures. However, many of these studies suffer from a severe lack of statistical validation. Only rarely are results passed to a statistical test and verified against some null hypothesis H0 in order to quantify their significance. In this paper we propose a method to statistically validate the performance of measures used to predict epileptic seizures. From measure profiles rendered by applying a moving-window technique to the electroencephalogram we first generate an ensemble of surrogates by a constrained randomization using simulated annealing. Subsequently the seizure prediction algorithm is applied to the original measure profile and to the surrogates. If detectable changes before seizure onset exist, highest performance values should be obtained for the original measure profiles and the null hypothesis. "The measure is not suited for seizure prediction" can be rejected. We demonstrate our method by applying two measures of synchronization to a quasicontinuous EEG recording and by evaluating their predictive performance using a straightforward seizure prediction statistics. We would like to stress that the proposed method is rather universal and can be applied to many other prediction and detection problems.

Algorithms↗
PubMed2004

Improved statistical test for nonstationarity using recurrence time statistics.

We have recently introduced a measure for nonstationarity using a recurrence time statistic to assess stationarity. In this paper we propose an extension of this method based on a detailed study of the statistics for the case of stationary systems. We derive a simple scheme that allows us to estimate the effective number of degrees of freedom relevant for this statistic. This substantially improves the statistical significance of the method and can be used to improve the significance of various other nonlinear statistics.

Journal Article↗
PubMed2003

Heterotopias, cortical dysplasias and glioneural tumors participate in cognitive processing in patients with temporal lobe epilepsy.

Focal brain lesions such as cortical dysplasia and glioneural tumors generate epileptic activity and thus may be synaptically connected with normal cortex. To test this hypothesis, we compared event-related potentials recorded directly from the medial temporal lobe (MTL) and a dysplastic lesion in eight patients with intractable temporal lobe epilepsy. The P3 component, related to visual target detection, showed different peak latencies in four patients and a larger intralesional amplitude compared to established anterior-MTL-generators in two patients. Semantic processing was identified by the N400 component and showed a different latency in four patients and larger intralesional amplitudes in two patients. These results are compatible with the hypothesis that cortical lesions interact with synaptic pathways related to cognitive functions such as visual target detection, and verbal processing.

Adult↗
PubMed2003

Automated detection of a preseizure state based on a decrease in synchronization in intracranial electroencephalogram recordings from epilepsy patients.

The question whether information extracted from the electroencephalogram (EEG) of epilepsy patients can be used for the prediction of seizures has recently attracted much attention. Several studies have reported evidence for the existence of a preseizure state that can be detected using different measures derived from the theory of dynamical systems. Most of these studies, however, have neglected to sufficiently investigate the specificity of the observed effects or suffer from other methodological shortcomings. In this paper we present an automated technique for the detection of a preseizure state from EEG recordings using two different measures for synchronization between recording sites, namely, the mean phase coherence as a measure for phase synchronization and the maximum linear cross correlation as a measure for lag synchronization. Based on the observation of characteristic drops in synchronization prior to seizure onset, we used this phenomenon for the characterization of a preseizure state and its distinction from the remaining seizure-free interval. After optimizing our technique on a group of 10 patients with temporal lobe epilepsy we obtained a successful detection of a preseizure state prior to 12 out of 14 analyzed seizures for both measures at a very high specificity as tested on recordings from the seizure-free interval. After checking for in-sample overtraining via cross validation, we applied a surrogate test to validate the observed predictability. Based on our results, we discuss the differences of the two synchronization measures in terms of the dynamics underlying seizure generation in focal epilepsies.

Journal Article↗
PubMed2003

Testing the null hypothesis of the nonexistence of a preseizure state.

A rapidly growing number of studies deals with the prediction of epileptic seizures. For this purpose, various techniques derived from linear and nonlinear time series analysis have been applied to the electroencephalogram of epilepsy patients. In none of these works, however, the performance of the seizure prediction statistics is tested against a null hypothesis, an otherwise ubiquitous concept in science. In consequence, the evaluation of the reported performance values is problematic. Here, we propose the technique of seizure time surrogates based on a Monte Carlo simulation to remedy this deficit.

Biophysical Phenomena↗
PubMed2003

Epileptic seizures are preceded by a decrease in synchronization.

The exact mechanisms leading to the occurrence of epileptic seizures in humans are still poorly understood. It is widely accepted, however, that the process of seizure generation is closely associated with an abnormal synchronization of neurons. In order to investigate this process, we here measure phase synchronization between different regions of the brain using intracranial EEG recordings. Based on our preliminary finding of a preictal drop in synchronization, we investigate whether this phenomenon can be used as a sensitive and specific criterion to characterize a preseizure state and to distinguish this state from the interictal interval. Applying an automated technique for detecting decreased synchronization to EEG recordings from a group of 18 patients with focal epilepsy comprising a total of 117 h, we observe a characteristic decrease in synchronization prior to 26 out of 32 analyzed seizures at a very high specificity as tested on interictal recordings. The duration of this preictal state is found to range from several minutes up to a few hours. Investigation of the spatial distribution of preictal desynchronization indicates that the process of seizure generation in focal epilepsy is not necessarily confined to the focus itself but may instead involve more distant, even contralateral areas of the brain. Finally, we demonstrate an intrahemispheric asymmetry in the spatial dynamics of preictal desynchronization that is found in the majority of seizures and appears to be an immanent part of the mechanisms underlying the initiation of seizures in humans.

Adult↗