Predictors Of Epileptic Seizures Research Articles

АНАЛИЗ ПРЕДИКТОРОВ ЭПИЛЕПТИЧЕСКИХ ПРИСТУПОВ

The statistical data on epilepsy, etiology and pathogenesis of the disease are given in the article. The main causes that affect the occurrence of epileptic seizures, and possible predictors that are sensitive to these causes (metabolic rate, blood flow velocity, change in electrical activity of the brain) are highlighted. The blood flow dynamics in the common carotid artery, which supplies the brain with oxygen and nutrients is proposed to be considered as one of the possible internal physiological parameters that are believed to be predictors of epileptic seizures.

Predictors of Epileptic Seizures and Ability to Work in Supratentorial Cavernous Angioma Located Within Eloquent Brain Areas.

The postoperative outcomes and the predictors of seizure control are poorly studied for supratentorial cavernous angiomas (CA) within or close to the eloquent brain area. To assess the predictors of preoperative seizure control, postoperative seizure control, and postoperative ability to work, and the safety of the surgery. Multicenter international retrospective cohort analysis of adult patients benefitting from a functional-based surgical resection with intraoperative functional brain mapping for a supratentorial CA within or close to eloquent brain areas. A total of 109 patients (66.1% women; mean age 38.4±12.5 yr), were studied. Age >38 yr (odds ratio [OR], 7.33; 95% confidence interval [CI], 1.53-35.19; P=.013) and time to surgery > 12 mo (OR, 18.21; 95% CI, 1.11-296.55; P=.042) are independent predictors of uncontrolled seizures at the time of surgery. Focal deficit (OR, 10.25; 95% CI, 3.16-33.28; P<.001) is an independent predictor of inability to work at the time of surgery. History of epileptic seizures at the time of surgery (OR, 7.61; 95% CI, 1.67-85.42; P=.003) and partial resection of the CA and/or of the hemosiderin rim (OR, 12.02; 95% CI, 3.01-48.13; P<.001) are independent predictors of uncontrolled seizures postoperatively. Inability to work at the time of surgery (OR, 19.54; 95% CI, 1.90-425.48; P=.050), Karnofsky Performance Status≤70 (OR, 51.20; 95% CI, 1.20-2175.37; P=.039), uncontrolled seizures postoperatively (OR, 105.33; 95% CI, 4.32-2566.27; P=.004), and worsening of cognitive functions postoperatively (OR, 13.71; 95% CI, 1.06-176.66; P=.045) are independent predictors of inability to work postoperatively. The functional-based resection using intraoperative functional brain mapping allows safe resection of CA and the peripheral hemosiderin rim located within or close to eloquent brain areas.

Towards Improved Design and Evaluation of Epileptic Seizure Predictors.

Key issues in the epilepsy seizure prediction research are (1) the reproducibility of results (2) the inability to compare multiple approaches directly. To overcome these problems, the seizure prediction challenge was organized on Kaggle.com. It aimed at establishing benchmarks on a dataset with predefined train, validation, and test sets. Our main objective is to analyze the competition format, and to propose improvements, which would facilitate a better comparison of algorithms. The second objective is to present a novel deep learning approach to seizure prediction and compare it to other commonly used methods using patient centered metrics. We used the competition's datasets to illustrate the effects of data contamination. Having better data partitions, we compared three types of models in terms of different objectives. We found that correct selection of test samples is crucial when evaluating the performance of seizure forecasting models. Moreover, we showed that models, which achieve state-of-the-art performance with respect to commonly used AUC, sensitivity, and specificity metrics, may not yet be suitable for practical usage because of low precision scores. Correlation between validation and test datasets used in the competition limited its scientific value. Our findings provide guidelines which allow for a more objective evaluation of seizure prediction models.

Epileptic seizures in anaplastic gangliogliomas

Aim: Prevalence and predictors of epileptic seizures are unknown in the malignant variant of ganglioglioma.Methods: In a retrospective exploratory dataset of 18 supratentorial anaplastic World Health Organization grade III gangliogliomas, we studied: (i) the prevalence and predictors of epileptic seizures at diagnosis; (ii) the evolution of seizures during tumor evolution; (iii) seizure control rates and predictors of epilepsy control after oncological treatments.Results: Epileptic seizures prevalence progresses throughout the natural course of anaplastic gangliogliomas: 44% at imaging discovery, 67% at histopathological diagnosis, 69% following oncological treatment, 86% at tumor progression, and 100% at the end-of-life phase. The medical control of seizures and their refractory status worsened during the tumor’s natural course: 25% of uncontrolled seizures at histopathological diagnosis, 40% following oncological treatment, 45.5% at tumor progression, and 45.5% at the end-of-life phase. Predictors of seizures at diagnosis appeared related to the tumor location (i.e. temporal and/or cortical involvement). Prognostic parameters of seizure control after first-line oncological treatment were temporal tumor location, eosinophilic granular bodies, TP53 mutation, and extent of resection. Prognostic parameters of seizure control at tumor progression were a history of epileptic seizures at diagnosis, seizure control after first-line oncological treatment, eosinophilic granular bodies, and TP53 mutation.Conclusion: Epileptic seizures are frequently observed in anaplastic gangliogliomas and both prevalence and medically refractory status worsen during the tumor’s natural course. Both oncological and antiepileptic treatments should be employed to improve the control of epileptic seizures and the quality of life of patients harboring an anaplastic ganglioglioma.

Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients

The ability of computational intelligence methods to predict epileptic seizures is evaluated in long-term EEG recordings of 278 patients suffering from pharmaco-resistant partial epilepsy, also known as refractory epilepsy. This extensive study in seizure prediction considers the 278 patients from the European Epilepsy Database, collected in three epilepsy centres: Hôpital Pitié-là-Salpêtrière, Paris, France; Universitätsklinikum Freiburg, Germany; Centro Hospitalar e Universitário de Coimbra, Portugal.For a considerable number of patients it was possible to find a patient specific predictor with an acceptable performance, as for example predictors that anticipate at least half of the seizures with a rate of false alarms of no more than 1 in 6h (0.15h−1). We observed that the epileptic focus localization, data sampling frequency, testing duration, number of seizures in testing, type of machine learning, and preictal time influence significantly the prediction performance.The results allow to face optimistically the feasibility of a patient specific prospective alarming system, based on machine learning techniques by considering the combination of several univariate (single-channel) electroencephalogram features. We envisage that this work will serve as benchmark data that will be of valuable importance for future studies based on the European Epilepsy Database.

Predictors of epileptic seizures - retrospective analysis

The article surveys the data from domestic and international studies regarding the evolution of thoughts and contemporary point of view at epileptic seizure predictors, considering data from experimental and laboratory studies as well as from clinical studies. The provisional epileptic seizure predictors classification is proposed. The characteristics of the included predictors is given, the need for direct comparison considering results of laboratory studies on biological models as well as clinical trials on biologic objects is underlined.

Is it reasonable to forbid the use of guide dogs for the blind as predictors of epileptic seizures? A case report

Is it reasonable to forbid the use of guide dogs for the blind as predictors of epileptic seizures? A case report

Is there chaos in the brain? II. Experimental evidence and related models

The search for chaotic patterns has occupied numerous investigators in neuroscience, as in many other fields of science. Their results and main conclusions are reviewed in the light of the most recent criteria that need to be satisfied since the first descriptions of the surrogate strategy. The methods used in each of these studies have almost invariably combined the analysis of experimental data with simulations using formal models, often based on modified Huxley and Hodgkin equations and/or of the Hindmarsh and Rose models of bursting neurons. Due to technical limitations, the results of these simulations have prevailed over experimental ones in studies on the nonlinear properties of large cortical networks and higher brain functions. Yet, and although a convincing proof of chaos (as defined mathematically) has only been obtained at the level of axons, of single and coupled cells, convergent results can be interpreted as compatible with the notion that signals in the brain are distributed according to chaotic patterns at all levels of its various forms of hierarchy. This chronological account of the main landmarks of nonlinear neurosciences follows an earlier publication [Faure, Korn, C. R. Acad. Sci. Paris, Ser. III 324 (2001) 773–793] that was focused on the basic concepts of nonlinear dynamics and methods of investigations which allow chaotic processes to be distinguished from stochastic ones and on the rationale for envisioning their control using external perturbations. Here we present the data and main arguments that support the existence of chaos at all levels from the simplest to the most complex forms of organization of the nervous system. We first provide a short mathematical description of the models of excitable cells and of the different modes of firing of bursting neurons (Section 1). The deterministic behavior reported in giant axons (principally squid), in pacemaker cells, in isolated or in paired neurons of Invertebrates acting as coupled oscillators is then described (Section 2). We also consider chaotic processes exhibited by coupled Vertebrate neurons and of several components of Central Pattern Generators (Section 3). It is then shown that as indicated by studies of synaptic noise, deterministic patterns of firing in presynaptic interneurons are reliably transmitted, to their postsynaptic targets, via probabilistic synapses (Section 4). This raises the more general issue of chaos as a possible neuronal code and of the emerging concept of stochastic resonance Considerations on cortical dynamics and of EEGs are divided in two parts. The first concerns the early attempts by several pioneer authors to demonstrate chaos in experimental material such as the olfactory system or in human recordings during various forms of epilepsies, and the belief in ‘dynamical diseases’ (Section 5). The second part explores the more recent period during which surrogate-testing, definition of unstable periodic orbits and period-doubling bifurcations have been used to establish more firmly the nonlinear features of retinal and cortical activities and to define predictors of epileptic seizures (Section 6). Finally studies of multidimensional systems have founded radical hypothesis on the role of neuronal attractors in information processing, perception and memory and two elaborate models of the internal states of the brain (i.e. ‘winnerless competition’ and ‘chaotic itinerancy’). Their modifications during cognitive functions are given special attention due to their functional and adaptive capabilities (Section 7) and despite the difficulties that still exist in the practical use of topological profiles in a state space to identify the physical underlying correlates. The reality of ‘neurochaos’ and its relations with information theory are discussed in the conclusion (Section 8) where are also emphasized the similarities between the theory of chaos and that of dynamical systems. Both theories strongly challenge computationalism and suggest that new models are needed to describe how the external world is represented in the brain. To cite this article: H. Korn, P. Faure, C. R. Biologies 326 (2003).