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For patients with focal epilepsy, a promising treatment to resolve recurrent seizure activity is resection of the brain tissue responsible for seizure generation (epileptogenic focus). Because of the associated risks with this invasive procedure of removing eloquent tissue, correct localization of the focus is of paramount importance. Computational analysis of recorded intracranial electroencephalographic (iEEG) signals from multiple brain regions can be effective in identifying the epileptogenic focus. Such multi-channel signals ideally require multivariate analysis, in the time or frequency domain, that can also help us understand how different brain regions interact with the focus. We employed the measure of Generalized Partial Directed Coherence (GPDC) in the frequency domain to measure the strength of directional interactions between brain sites from long-term iEEG recordings in 4 patients with focal temporal lobe epilepsy. In 3 out of 4 patients, the maximum directed interactions into brain sites (inflow of information) was found to be statistically significant more frequent (p Keywords: GPDC, frequency analysis, focus localization, focal epilepsy, INTRODUCTION Epilepsy afflicts approximately 1% of the global population, making it one of the most common neurological disorders [1]. Epilepsy is characterized by intermittent abnormal events (seizures), which result from [...]

The relationship of epileptic spikes to seizures has been a matter of debate with researchers reporting higher or lower spike rates before and after seizures. To elucidate a possible role of spikes in ictogenesis (seizure generation) we developed a mathematical framework to investigate the spatiotemporal dynamics of spikes in the epileptic brain. Long-term (5-10 days) intracranial electroencephalograms (iEEGs) from 2 patients with temporal lobe epilepsy who underwent presurgical evaluation for localization of candidate epileptic foci were used in the study. First, spikes were detected using an in-house spike detection algorithm. To characterize the spatiotemporal changes in spikes a novel spatial synchronization measure (SSM) of spikes was developed. Application of SSM to iEEG revealed monotonically increasing long-term preictal synchronization between spike trains at brain sites that included the epileptogenic zone. The presence of preictal synchronization of spikes hours to minutes prior to the onset of seizures indicates a possible predictive value of spike synchronization for seizure occurrence. Furthermore, across seizures and patients, we observed that desynchronization of spikes at critical brain sites mostly occurred in postictal epochs. We characterized these observed changes between preictal and postical spike synchronization by a measure of resetting based on the fraction of pairs that synchronize preictally and desynchronize postictally. On the basis of this resetting measure, we showed that resetting of spike synchronization occurs more commonly at seizures than interictally (p Keywords: Epilepsy, Spikes, Seizures, Spatiotemporal Analysis, Ictogenesis, INTRODUCTION Epilepsy is a widely prevalent neurological disorder affecting around 50 million people worldwide and has been classified as the third most common neurological disorder next to stroke and Alzheimer's. [...]

In this study we test the hypothesis that four sleep disorders, the periodic limb movement behavioral disorder (PLMBD), rapid eye movement behavior disorder (REMBD), insomnia, and nocturnal frontal lobe epilepsy (NFLE), can be differentiated from one another and from the healthy condition through analysis of the sleep electroencephalogram (EEG). Frequency (median frequency and spectral entropy) and complexity (Higuchi fractal dimension) measures of the EEG signal were estimated over time and used to extract features related to the different sleep stages and the sleep-specific activity of the Cyclic Alternating Pattern (CAP) (EEG data from the Physionet database). Analysis of Variance (ANOVA) of the extracted features was used to compare the five groups: PLMBD (10 subjects), REMBD (22 subjects), NFLE (37 subjects), insomnia (9 subjects) and healthy (11 subjects). ANOVA revealed significant differences (α = 0.05) between some but not all of the groups. Subsequent multivariate analysis of variance (MANOVA) revealed that all five groups could be differentiated (p Keywords: EEG, sleep stages, CAP, differential diagnosis PLMBD, REMBD, NFLE, insomnia., INTRODUCTION Sleep is a quotidian cycle of repeatable sequence of sleep stages over several hours [1, 2]. A healthy sleep cycle is associated with good performance of normal activities during [...]

About 1% of the global population is afflicted with epilepsy and, of all adult epilepsy patients, 60% have focal epilepsy. It has been observed that, during the course of secondarily generalized focal seizures, epileptic activity propagates from focal to other brain sites. This could reflect changes in connectivity mappings. Computer assisted analysis of recorded electrographic activity of the brain (EEG) can allow the characterization and quantification and of brain connectivity patterns during seizures. We developed a method that uses the measure of Generalized Partial Directed Coherence (GPDC) to estimate the effective connections between brain sites, and by the use of surrogate data analysis determines the statistically significant ones. Assuming that the brain functions as a network of directionally interconnected nodes, we then use these derived connections to estimate the inward and outward density of connections for each node over time during seizures. The method was applied to EEG recordings of seizures from three patients with focal temporal lobe epilepsy. In 16 of 19 recorded clinical seizures across these patients, at the start of seizures, a relatively consistent and distinct increase of density of inward connections at focal brain sites was observed, accompanied by an increase of density of outward connections at non-focal brain sites. For some seizures and patients reversal of this pattern was observed as seizures were progressing. Subclinical seizures did not exhibit such a consistent or distinct pattern of connectivity. These findings shed further light on the underlying mechanisms governing seizure spatiotemporal dynamics and could be useful in the elucidation of seizure generation, accurate focus localization and seizure control. Keywords: Network Connectivity, GPDC, Graph Theory, Epilepsy, Seizure Dynamics, INTRODUCTION Epilepsy is one of the most common neurological disorders afflicting approximately 1% of the global population [1]. The disorder is characterized by intermittent abnormal events (seizures) which, if not [...]

The epileptic brain typically transits into seizures (ictal states) with preceding (preictal) and following (postictal) states, while it behaves 'normally' between seizures (interictal states). We set to automatically identify these brain states from the electroencephalogram (EEG). The ability to differentiate between epileptic brain states can find many valuable applications in the management of epilepsy. A time-frequency analysis via multivariate matching pursuit (MMP), which efficiently decomposes a multichannel signal into basic waveforms (e.g., Gabor functions), was performed peri-ictally and interictally to long-term intracranial multi-channel EEG recordings from 4 patients with intractable epilepsy. Novel features capturing EEG's complexity, frequency content and energy were estimated from the resulted data. We used these features together with their time lags in a 4-class support vector machine (SVM) classification scheme per patient and tested it under a leave-one-out cross-validation (LOOCV) strategy. Synthetic Minority Oversampling Technique (SMOTE) was also used to treat the highly imbalanced datasets due to considerably longer span of the interictal state compared to the span of the other states. Our analytical scheme achieved high sensitivity in detecting each state in all 4 patients (e.g., 83%, 95%, 100% and 100% for the preictal state per patient respectively). These results suggest potential employment of the devised scheme to a wide range of applications, from seizure prediction and in-time intervention to abolish an upcoming seizure, to accurate and objective monitoring of seizure susceptibility over time, to fast and robust detection of different epileptic brain states hidden in days-long inpatient EEG recordings. Keywords: Multivariate Matching Pursuit, EEG, Brain States, Seizures, Epilepsy, INTRODUCTION Epilepsy is characterized by recurrent seizures and is among the most common disorders of the nervous system, second only to stroke and Alzheimer disease, and affects 1% to 2% [...]

Single‐photon emission computed tomography (SPECT) during seizures and magnetoencephalography (MEG) during the interictal state are noninvasive modalities employed in the localization of the epileptogenic zone in patients with drug‐resistant focal epilepsy (DRFE). The present study aims to investigate whether there exists a preferentially high MEG functional connectivity (FC) among those regions of the brain that exhibit hyperperfusion or hypoperfusion during seizures. We studied MEG and SPECT data in 30 consecutive DRFE patients who had resective epilepsy surgery. We parcellated each ictal perfusion map into 200 regions of interest (ROIs) and generated ROI time series using source modeling of MEG data. FC between ROIs was quantified using coherence and phase‐locking value. We defined a generalized linear model to relate the connectivity of each ROI, ictal perfusion z score, and distance between ROIs. We compared the coefficients relating perfusion z score to FC of each ROI and estimated the connectivity within and between resected and unresected ROIs. We found that perfusion z scores were strongly correlated with the FC of hyper‐, and separately, hypoperfused ROIs across patients. High interictal connectivity was observed between hyperperfused brain regions inside and outside the resected area. High connectivity was also observed between regions of ictal hypoperfusion. Importantly, the ictally hypoperfused regions had a low interictal connectivity to regions that became hyperperfused during seizures. We conclude that brain regions exhibiting hyperperfusion during seizures highlight a preferentially connected interictal network, whereas regions of ictal hypoperfusion highlight a separate, discrete and interconnected, interictal network. [ABSTRACT FROM AUTHOR]

This article gives an overview of a methodology for building dynamical electronic systems. As an example a part of a system for epileptic seizure prediction is used, which monitors EEG signals and continuously calculates the largest short-term Lyapunov exponents. In dynamical electronic systems, the cost of exploitation, for instance energy consumption, may vary substantially with the values of input signals. In addition, the functions describing the variations are not known at the time the system is designed. As a result, the architecture of the system must accommodate for the worst case exploitation costs, which rapidly exceed the available resources (for instance battery life) when accumulated over time. The presented system scenario methodology solves these challenges by identifying at design time groups of possible exploitation costs, called system scenarios, and implementing a mechanism to detect system scenarios at run time and re-configure the system to cost-efficiently accommodate them. During reconfiguration, the optimized system architecture settings for the active system scenario are selected and the total exploitation cost is reduced. When the dynamic behavior is due to input data variables with a large number of possible values, current techniques for bottom-up scenario identification and detection becomes too complex. A new top-down technique, based on polygonal regions, is presented in this paper. The results for the example system indicate that with 10 system scenarios the average energy consumption of the system can be reduced by 28% and brought within 5% of the theoretically best solution.