Influencia del ingreso en una Unidad de Monitorización vídeo-EEG prolongada en función del tiempo de evolución de epilèpsia

Se desconoce si existe un tiempo de evolución límite a partir del cual ingresar en una UMVEEG* no suponga una mejoría del pronóstico del paciente epiléptico. El estudio analiza el efecto del ingreso en la UMVEEG sobre una serie de variables pronósticas (FC**, NFAE***, CVP****) en función del tiempo de evolución desde el diagnóstico. Analizamos epilépticos diagnosticados con certeza y pacientes con crisis psicógenas. Se estudiaron 135 pacientes(Edad:39+13,5años,Sexo(55,6%mujeres).Se obtuvo una mejoría significativa de FC**(p<0,001)y CVP****(p<0,005)en los grupos estudiados independientemente del tiempo de evolución.El tiempo de evolución determinó una respuesta diferencial sobre la reducción del NFAE***excepto para crisis psicógenas,en que hubo una reducción significativa(p=0,004)independientemente del tiempo de evolución.

Monitorização vídeo-EEG prolongada em crises não epilépticas: semiologia clínica

O objetivo deste estudo foi mostrar a contribuição da monitorização vídeo-EEG prolongada (MVEP) no diagnóstico de crises não epilépticas (CNE) e estimar sua prevalência em um centro terciário de atendimento à Epilepsia (EP). Foram observados 47 pacientes com diagnóstico de CNE com crises espontâneas ou provocadas. Foram instituídos protocolos direcionados à história clínica e à semiologia das crises. A análise estatística baseou-se no teste de Fisher e na análise de cluster. Os resultados evidenciaram prevalência de 10% de CNE. Houve predominância do sexo feminino (63,8%); em 57% dos pacientes as crises foram espontâneas. A média de idade foi 32,5 ± 11anos. O sinal semiológico mais freqüente foi o sono aparente (87,2%). em 9% dos pacientes observaram-se tanto EP como CNE. Três agrupamentos resultaram da análise de cluster: CNE hipermotora das extremidades com alteração de tônus; CNE com automatismos e CNE axial com movimentos oculares. em conclusão, o estudo da semiologia clínica das CNE durante a MVEP contribui para o diagnóstico desta entidade nosológica e para o diagnóstico diferencial com EP; o teste provocativo auxilia na obtenção das crises.

Avaliação de foco epileptogênico do lobo temporal: correlação entre SPECT ictal, ressonância magnética e ressonância magnética com espectroscopia de prótons

O objetivo deste trabalho foi determinar a existência de concordância entre os métodos radioisotópico e radiológico e, em caso positivo, avaliar a utilidade do SPECT ictal na determinação do foco epileptogênico. Foram realizados SPECT ictal, ressonância magnética (RM) e ressonância magnética com espectroscopia de prótons (RME) em seis pacientes com epilepsia de lobo temporal refratária. O SPECT ictal foi realizado após a retirada das drogas antiepilépticas durante monitoramento por vídeo-EEG, utilizando-se o 99mTc-ECD, administrado aos pacientes no início da crise. As imagens de RM foram obtidas em T1, T2 e FLAIR, com cortes de 3 e 5 mm de espessura, e a RME foi realizada com técnica PRESS, com voxel único posicionado no hipocampo, bilateralmente. A análise estatística incluiu os valores de Kappa (k), erro-padrão (ep) e o nível de significância (p) para a lateralização do foco. Os achados foram analisados com base na localização por EEG da descarga ictal, no tempo de duração da crise (109-280 s; média: 152 s) e no tempo de administração do traçador (30-262 s; média: 96 s). Obtivemos dados correlatos em quatro pacientes (67%), com valores de k = 0,67, ep = 0,38 e p = 0,041. Concluímos que existe concordância entre SPECT ictal, RM e RME, e a utilidade do procedimento radioisotópico está relacionada aos casos em que o EEG não é diagnóstico e quando há discordância ou indefinição diagnóstica na análise comparativa entre EEG, RM e RME.

Estudo vídeo-polissonográfico em recém-nascidos de termo pequenos para a idade gestacional

Video-polygraphic-EEG studies were performed in the first 24 life-hours of 26 healthy full-term newborns without perinatal injuries. The neurological examination and cranial ultrasonography were normal. The newborns were divided into two groups: one, with full-term appropriate - birth weight 11 newborns (control group ) and the other with full-term low-birth weight 15 newborns. Thirteen newborns of the second group had video-polygraphic-EEG study abnormalities. The most frequent abnormalities were found in 11 cases, as far as sleep architecture is concerned. Also, when compared with the control group, 8 cases of an excessive amount of startles and 2 cases of low behavior activities were found. The results demonstrate the usefulness of video-polygraphic-EEG study in the full-term newborns with intra-uterine growth retard. This examination was sensitive to detect behavior, sleep architecture and EEG standard differences in the low birth-weight newborns as to the control group.

Sistema de neurónios espelho no síndrome de down: estudo das variações dos ritmos Mu No Eeg

A capacidade de compreensão das acções dos outros e de imitação tem sido descrita como fundamental para a cognição social do ser humano. Recentemente tem sido atribuída a responsabilidade desta capacidade a um sistema neuronal denominado de Sistema de Neurónios Espelho, que se tem demonstrado estar afectado em perturbações mentais que se caracterizam por alterações severas da teoria da mente e da empatia, como é o caso do autismo. No caso do Síndrome de Down, verifica-se a coexistência de boas competências sociais e de capacidades práxicas e de imitação intactas, com dificuldades de interpretação de situações sociais e de reconhecimento de emoções, que nos levam a questionar acerca da actividade do seu Sistema de Neurónios Espelho. As oscilações do ritmo de frequências um (8-13 Hz) no córtex sensório-motor perante a observação de acções são consideradas um reflexo da actividade dos neurónios espelho, estando estabelecido que em pessoas saudáveis ocorre uma supressão mu na realização de movimentos com o membro superior e na sua observação quando realizados por outras pessoas. Neste estudo registou-se electroencefalograficamente a supressão dos ritmos mu em 11 pessoas com SD e em 20 pessoas sem SD nas seguintes condições: observação de um vídeo com duas bolas em movimento, observação de um vídeo com um movimento repetido de uma mão e realização movimentos com a mão. A baseline foi registada através da observação de um ponto estático. Constatamos que existe supressão dos ritmos mu na observação das acções dos outros em pessoas com Síndrome Down da mesma forma que ocorre na realização do próprio movimento, sugerindo uma relativa preservação do funcionamento dos neurónios espelho e dos mecanismos básicos de cognição social. Estes resultados vão de encontro aos estudos que apontam para a integridade das capacidades de imitação no Síndrome Down. Verificamos também que não se encontram diferenças significativas na supressão dos ritmos mu entre os grupos de pessoas com Síndrome Down e de Controlo em relação às condições usadas na investigação.

Estudo vídeo-polissonográfico em recém-nascidos de termo pequenos para a idade gestacional

Analisamos o exame video-polissonográfico de 26 recém-nascidos de termo (RNT) com 24 horas de vida. Os RN tinham exame neurológico e ultrassonográfico cerebral normais e apresentaram período perinatal isento de complicações. Foram subdivididos em dois grupos, um controle constituído de 11 RNT com peso adequado para a idade gestacional; e um grupo de 15 RN com peso abaixo do esperado para o termo (RNT-PIG). do segundo grupo, 13 RN apresentaram algum tipo de alteração ao exame video-polissonográfico. As alterações mais frequentes foram na arquitetura do sono, 11 casos, e no comportamento, em que oito RN apresentaram número excessivo de sobressaltos (startle) em relação ao grupo controle e dois RN uma atividade motora reduzida. Os resultados deste estudo demonstram a utilidade da video-polissonografia quando aplicada a RNT-PIG. O exame mostrou-se sensível em detectar diferenças no comportamento, arquitetura do sono e padrão eletrencefalográfico dos RNT-PIG quando comparados ao grupo controle.

Utilização de um monitor de vídeo como fonte de alta tensão para eletroforese capilar

This article describes the use of a conventional CRT monitor as a high voltage power supply for capillary electrophoresis. With this monitor, a 23-kV high voltage with a ripple of 1.32% was observed. The reproducibility of the applied high voltage was evaluated by measuring the standard deviations of peak area and migration time for five consecutive injections of a test mixture containing potassium, sodium, and lithium cations at 50 mmol L-1. The errors were about 2.5% and 0.6% for peak area and migration time, respectively. The maximum current tested was about 180 mA, which covers most capillary electrophoresis applications. This system has been successfully used for several months, maintaining the desired level of performance.

The Brain as a Distributed Intelligent Processing System: An EEG Study

Background: Various neuroimaging studies, both structural and functional, have provided support for the proposal that a distributed brain network is likely to be the neural basis of intelligence. The theory of Distributed Intelligent Processing Systems (DIPS), first developed in the field of Artificial Intelligence, was proposed to adequately model distributed neural intelligent processing. In addition, the neural efficiency hypothesis suggests that individuals with higher intelligence display more focused cortical activation during cognitive performance, resulting in lower total brain activation when compared with individuals who have lower intelligence. This may be understood as a property of the DIPS. Methodology and Principal Findings: In our study, a new EEG brain mapping technique, based on the neural efficiency hypothesis and the notion of the brain as a Distributed Intelligence Processing System, was used to investigate the correlations between IQ evaluated with WAIS (Whechsler Adult Intelligence Scale) and WISC (Wechsler Intelligence Scale for Children), and the brain activity associated with visual and verbal processing, in order to test the validity of a distributed neural basis for intelligence. Conclusion: The present results support these claims and the neural efficiency hypothesis.

Kernel machines for epilepsy diagnosis via EEG signal classification: A comparative study

Objective: We carry out a systematic assessment on a suite of kernel-based learning machines while coping with the task of epilepsy diagnosis through automatic electroencephalogram (EEG) signal classification. Methods and materials: The kernel machines investigated include the standard support vector machine (SVM), the least squares SVM, the Lagrangian SVM, the smooth SVM, the proximal SVM, and the relevance vector machine. An extensive series of experiments was conducted on publicly available data, whose clinical EEG recordings were obtained from five normal subjects and five epileptic patients. The performance levels delivered by the different kernel machines are contrasted in terms of the criteria of predictive accuracy, sensitivity to the kernel function/parameter value, and sensitivity to the type of features extracted from the signal. For this purpose, 26 values for the kernel parameter (radius) of two well-known kernel functions (namely. Gaussian and exponential radial basis functions) were considered as well as 21 types of features extracted from the EEG signal, including statistical values derived from the discrete wavelet transform, Lyapunov exponents, and combinations thereof. Results: We first quantitatively assess the impact of the choice of the wavelet basis on the quality of the features extracted. Four wavelet basis functions were considered in this study. Then, we provide the average accuracy (i.e., cross-validation error) values delivered by 252 kernel machine configurations; in particular, 40%/35% of the best-calibrated models of the standard and least squares SVMs reached 100% accuracy rate for the two kernel functions considered. Moreover, we show the sensitivity profiles exhibited by a large sample of the configurations whereby one can visually inspect their levels of sensitiveness to the type of feature and to the kernel function/parameter value. Conclusions: Overall, the results evidence that all kernel machines are competitive in terms of accuracy, with the standard and least squares SVMs prevailing more consistently. Moreover, the choice of the kernel function and parameter value as well as the choice of the feature extractor are critical decisions to be taken, albeit the choice of the wavelet family seems not to be so relevant. Also, the statistical values calculated over the Lyapunov exponents were good sources of signal representation, but not as informative as their wavelet counterparts. Finally, a typical sensitivity profile has emerged among all types of machines, involving some regions of stability separated by zones of sharp variation, with some kernel parameter values clearly associated with better accuracy rates (zones of optimality). (C) 2011 Elsevier B.V. All rights reserved.

A Nonstationary Model of Newborn EEG

The detection of seizure in the newborn is a critical aspect of neurological research. Current automatic detection techniques are difficult to assess due to the problems associated with acquiring and labelling newborn electroencephalogram (EEG) data. A realistic model for newborn EEG would allow confident development, assessment and comparison of these detection techniques. This paper presents a model for newborn EEG that accounts for its self-similar and non-stationary nature. The model consists of background and seizure sub-models. The newborn EEG background model is based on the short-time power spectrum with a time-varying power law. The relationship between the fractal dimension and the power law of a power spectrum is utilized for accurate estimation of the short-time power law exponent. The newborn EEG seizure model is based on a well-known time-frequency signal model. This model addresses all significant time-frequency characteristics of newborn EEG seizure which include; multiple components or harmonics, piecewise linear instantaneous frequency laws and harmonic amplitude modulation. Estimates of the parameters of both models are shown to be random and are modelled using the data from a total of 500 background epochs and 204 seizure epochs. The newborn EEG background and seizure models are validated against real newborn EEG data using the correlation coefficient. The results show that the output of the proposed models has a higher correlation with real newborn EEG than currently accepted models (a 10% and 38% improvement for background and seizure models, respectively).

A matching pursuit-based signal complexity measure for the analysis of newborn EEG

This paper presents a new relative measure of signal complexity, referred to here as relative structural complexity, which is based on the matching pursuit (MP) decomposition. By relative, we refer to the fact that this new measure is highly dependent on the decomposition dictionary used by MP. The structural part of the definition points to the fact that this new measure is related to the structure, or composition, of the signal under analysis. After a formal definition, the proposed relative structural complexity measure is used in the analysis of newborn EEG. To do this, firstly, a time-frequency (TF) decomposition dictionary is specifically designed to compactly represent the newborn EEG seizure state using MP. We then show, through the analysis of synthetic and real newborn EEG data, that the relative structural complexity measure can indicate changes in EEG structure as it transitions between the two EEG states; namely seizure and background (non-seizure).

EEG-based Brain-Computer Interfaces: An Overview of Basic Concepts and Clinical Applications in Neurorehabilitation

Some patients are no longer able to communicate effectively or even interact with the outside world in ways that most of us take for granted. In the most severe cases, tetraplegic or post-stroke patients are literally `locked in` their bodies, unable to exert any motor control after, for example, a spinal cord injury or a brainstem stroke, requiring alternative methods of communication and control. But we suggest that, in the near future, their brains may offer them a way out. Non-invasive electroencephalogram (EEG)-based brain-computer interfaces (BCD can be characterized by the technique used to measure brain activity and by the way that different brain signals are translated into commands that control an effector (e.g., controlling a computer cursor for word processing and accessing the internet). This review focuses on the basic concepts of EEG-based BC!, the main advances in communication, motor control restoration and the down-regulation of cortical activity, and the mirror neuron system (MNS) in the context of BCI. The latter appears to be relevant for clinical applications in the coming years, particularly for severely limited patients. Hypothetically, MNS could provide a robust way to map neural activity to behavior, representing the high-level information about goals and intentions of these patients. Non-invasive EEG-based BCIs allow brain-derived communication in patients with amyotrophic lateral sclerosis and motor control restoration in patients after spinal cord injury and stroke. Epilepsy and attention deficit and hyperactive disorder patients were able to down-regulate their cortical activity. Given the rapid progression of EEG-based BCI research over the last few years and the swift ascent of computer processing speeds and signal analysis techniques, we suggest that emerging ideas (e.g., MNS in the context of BC!) related to clinical neuro-rehabilitation of severely limited patients will generate viable clinical applications in the near future.

From EEG to BOLD: Brain mapping and estimating transfer functions in simultaneous EEG-fMRI acquisitions

Simultaneous acquisition of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) aims to disentangle the description of brain processes by exploiting the advantages of each technique. Most studies in this field focus on exploring the relationships between fMRI signals and the power spectrum at some specific frequency bands (alpha, beta, etc.). On the other hand, brain mapping of EEG signals (e.g., interictal spikes in epileptic patients) usually assumes an haemodynamic response function for a parametric analysis applying the GLM, as a rough approximation. The integration of the information provided by the high spatial resolution of MR images and the high temporal resolution of EEG may be improved by referencing them by transfer functions, which allows the identification of neural driven areas without strong assumptions about haemodynamic response shapes or brain haemodynamic`s homogeneity. The difference on sampling rate is the first obstacle for a full integration of EEG and fMRI information. Moreover, a parametric specification of a function representing the commonalities of both signals is not established. In this study, we introduce a new data-driven method for estimating the transfer function from EEG signal to fMRI signal at EEG sampling rate. This approach avoids EEG subsampling to fMRI time resolution and naturally provides a test for EEG predictive power over BOLD signal fluctuations, in a well-established statistical framework. We illustrate this concept in resting state (eyes closed) and visual simultaneous fMRI-EEG experiments. The results point out that it is possible to predict the BOLD fluctuations in occipital cortex by using EEG measurements. (C) 2010 Elsevier Inc. All rights reserved.

Gamma band oscillations under influence of bromazepam during a sensorimotor integration task: An EEG coherence study

The goal of the present study was to explore the dynamics of the gamma band using the coherence of the quantitative electroencephalography (qEEG) in a sensorimotor integration task and the influence of the neuromodulator bromazepam on the band behavior. Our hypothesis is that the needs of the typewriting task will demand the coupling of different brain areas, and that the gamma band will promote the binding of information. It is also expected that the neuromodulator will modify this coupling. The sample was composed of 39 healthy subjects. We used a randomized double-blind design and divided subjects into three groups: placebo (n = 13), bromazepam 3 mg (n = 13) and bromazepam 6 mg (n = 13). The two-way ANOVA analysis demonstrated a main effect for the factors condition (i.e., C4-CZ electrode pair) and moment (i.e., C3-CZ, C3-C4 and C4-CZ pairs of electrodes). We propose that the gamma band plays an important role in the binding among several brain areas in complex motor tasks and that each hemisphere is influenced in a different manner by the neuromodulator. (C) 2009 Elsevier Ireland Ltd. All rights reserved.