Valeur diagnostique des micro-éveils autonomiques comme substitut aux éveils cérébraux dans l'apnée du sommeil

Les troubles respiratoires du sommeil ont une prévalence élevée dans la population générale, l’apnée obstructive du sommeil étant le plus important de ces troubles. Malgré tout, une grande proportion des patients avec apnée ne sont pas diagnostiqués. La méthode la plus complète pour diagnostiquer l’apnée est l’enregistrement d’une nuit de sommeil par polysomnographie, aussi appelée enregistrement de type 1, qui est un processus long et coûteux. Pour surmonter ces difficultés, des appareils d’enregistrements portables (ou de type 3) ont été développés. Toutefois, ces enregistrements de type 3 ne capturent pas l’activité cérébrale, mesurée avec l’électroencéphalographie (EEG). Le présent mémoire décrit une étude comparative entre les enregistrements de type 1 et de type 3. Tous les événements respiratoires d’apnée, d’hypopnée et d’éveils liés à un effort respiratoire (RERA, en anglais) seront analysés ainsi que les éveils cérébraux (ou éveils EEG) et les éveils autonomiques. Ces éveils autonomiques se définissent par une hausse de la fréquence cardiaque suite à un événement respiratoire. Pour enrichir les analyses, les variables respiratoires suivantes ont été étudiées : une chute de la saturation en oxygène de 4 % (ODI), l’index d’apnée-hypopnée (IAH), l’indice de perturbations respiratoires avec apnées + hypopnées + RERAs et les éveils EEG (RDIe, en anglais) et le RDI incluant les éveils autonomiques définis par une augmentation de la fréquence cardiaque de 5 bpm (RDIa5). L’objectif de la présente étude est d’évaluer la proportion d’événements respiratoires avec éveils autonomiques ainsi que leur impact sur le RDI des enregistrements de type 1 et 3. L’hypothèse suggère que les événements avec éveils autonomiques auraient un plus grand impact sur le RDI des enregistrements de type 3 contrairement au type 1. Cette étude inclut 72 sujets ayant suivi un enregistrement de polysomnographie complète de type 1 ainsi que 79 sujets différents ayant suivi un enregistrement ambulatoire de type 3. Les analyses suivantes ont été effectuées : 1) les pourcentages d’événements associés avec seulement des éveils autonomiques dans les enregistrements de type 1 et de type 3 ; 2) les fréquences de migration entre les catégories basses et élevées de sévérité de l’AHI en prenant en compte les événements associés avec seulement des éveils autonomiques ; 3) les Bland-Altman (B-A) pour mesurer l’accord entre l’AHI, le RDIe et le RDIa5 (type 1), et entre l’AHI et le RDIa5 (type 3) et ; 4) les corrélations de Pearson et les coefficients de corrélation intraclasse (ICC) pour mesurer l’accord entre l’AHI, le RDIe et le RDIa5 (type 1), et entre l’AHI et le RDIa5 (type 3). L’utilisation du critère de RDIa5 permet d’ajouter 49 % d’événements comptés avec l’AHI pour les enregistrements de type 1, et 51 % d’événements pour ceux de type 3. La présente étude montre que les éveils autonomiques ont un impact similaire autant pour le RDI des enregistrements de type 3 que de type 1. En conclusion, on peut affirmer que le RDIa5 est acceptable et fiable pour les enregistrements de type 3.

Valeur diagnostique des micro-éveils autonomiques comme substitut aux éveils cérébraux dans l'apnée du sommeil

Les troubles respiratoires du sommeil ont une prévalence élevée dans la population générale, l’apnée obstructive du sommeil étant le plus important de ces troubles. Malgré tout, une grande proportion des patients avec apnée ne sont pas diagnostiqués. La méthode la plus complète pour diagnostiquer l’apnée est l’enregistrement d’une nuit de sommeil par polysomnographie, aussi appelée enregistrement de type 1, qui est un processus long et coûteux. Pour surmonter ces difficultés, des appareils d’enregistrements portables (ou de type 3) ont été développés. Toutefois, ces enregistrements de type 3 ne capturent pas l’activité cérébrale, mesurée avec l’électroencéphalographie (EEG). Le présent mémoire décrit une étude comparative entre les enregistrements de type 1 et de type 3. Tous les événements respiratoires d’apnée, d’hypopnée et d’éveils liés à un effort respiratoire (RERA, en anglais) seront analysés ainsi que les éveils cérébraux (ou éveils EEG) et les éveils autonomiques. Ces éveils autonomiques se définissent par une hausse de la fréquence cardiaque suite à un événement respiratoire. Pour enrichir les analyses, les variables respiratoires suivantes ont été étudiées : une chute de la saturation en oxygène de 4 % (ODI), l’index d’apnée-hypopnée (IAH), l’indice de perturbations respiratoires avec apnées + hypopnées + RERAs et les éveils EEG (RDIe, en anglais) et le RDI incluant les éveils autonomiques définis par une augmentation de la fréquence cardiaque de 5 bpm (RDIa5). L’objectif de la présente étude est d’évaluer la proportion d’événements respiratoires avec éveils autonomiques ainsi que leur impact sur le RDI des enregistrements de type 1 et 3. L’hypothèse suggère que les événements avec éveils autonomiques auraient un plus grand impact sur le RDI des enregistrements de type 3 contrairement au type 1. Cette étude inclut 72 sujets ayant suivi un enregistrement de polysomnographie complète de type 1 ainsi que 79 sujets différents ayant suivi un enregistrement ambulatoire de type 3. Les analyses suivantes ont été effectuées : 1) les pourcentages d’événements associés avec seulement des éveils autonomiques dans les enregistrements de type 1 et de type 3 ; 2) les fréquences de migration entre les catégories basses et élevées de sévérité de l’AHI en prenant en compte les événements associés avec seulement des éveils autonomiques ; 3) les Bland-Altman (B-A) pour mesurer l’accord entre l’AHI, le RDIe et le RDIa5 (type 1), et entre l’AHI et le RDIa5 (type 3) et ; 4) les corrélations de Pearson et les coefficients de corrélation intraclasse (ICC) pour mesurer l’accord entre l’AHI, le RDIe et le RDIa5 (type 1), et entre l’AHI et le RDIa5 (type 3). L’utilisation du critère de RDIa5 permet d’ajouter 49 % d’événements comptés avec l’AHI pour les enregistrements de type 1, et 51 % d’événements pour ceux de type 3. La présente étude montre que les éveils autonomiques ont un impact similaire autant pour le RDI des enregistrements de type 3 que de type 1. En conclusion, on peut affirmer que le RDIa5 est acceptable et fiable pour les enregistrements de type 3.

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.

Good surgical outcome in discordant ictal EEG-MRI unilateral mesial temporal sclerosis patients

Purpose:Video electroencephalography (vEEG) monitoring of patients with unilateral mesial temporal sclerosis (uMTS) may show concordant or discordant seizure onset in relation to magnetic resonance imaging (MRI) evidence of MTS. Contralateral seizure usually leads to an indication of invasive monitoring. Contralateral seizure onset on invasive monitoring may contraindicate surgery. We evaluated long-term outcome after anteromesial temporal lobectomy (AMTL) in a consecutive series of uMTS patients with concordant and discordant vEEG findings, uniformly submitted to AMTL on the MRI evidence of MTS side without invasive monitoring. Methods:We compared surgical outcome of all uMTS patients undergoing vEEG monitoring between January 1999 and April 2005 in our service. Discordant cases were defined by at least one seizure onset contralateral to the MRI evidence of MTS. Good surgical outcome was considered as Engel`s class I. We also evaluated ictal SPECT concordance to ictal EEG and surgical outcome. Results:Fifty-four patients had concordant (C) and 22 had discordant (D) scalp EEG and MRI. Surgical outcome was similar in both groups (C = 74% versus D = 86%). Duration of follow-up was comparable in both groups: C = 56.1 +/- 20.7 months versus D = 59.8 +/- 21.2 months (p = 0.83, nonsignificant). Discordant single-photon emission computed tomography (SPECT) results did not influence surgical outcome. Discussion:Surgical outcome was not influenced by contralateral vEEG seizure onset or contralateral increased flow on ictal SPECT. Although vEEG monitoring should still be performed in these patients, to rule out psychogenic seizures and extratemporal seizure onset, a potentially risky procedure such as invasive monitoring may not only not be indicated in this patient population, but may also lead to patients erroneously being denied surgery.

Preprocessing and time-frequency analysis of newborn EEG seizures

Neurological disease or dysfunction in newborn infants is often first manifested by seizures. Prolonged seizures can result in impaired neurodevelopment or even death. In adults, the clinical signs of seizures are well defined and easily recognized. In newborns, however, the clinical signs are subtle and may be absent or easily missed without constant close observation. This article describes the use of adaptive signal processing techniques for removing artifacts from newborn electroencephalogram (EEG) signals. Three adaptive algorithms have been designed in the context of EEG signals. This preprocessing is necessary before attempting a fine time-frequency analysis of EEG rhythmical activities, such as electrical seizures, corrupted by high amplitude signals. After an overview of newborn EEG signals, the authors describe the data acquisition set-up. They then introduce the basic physiological concepts related to normal and abnormal newborn EEGs and discuss the three adaptive algorithms for artifact removal. They also present time-frequency representations (TFRs) of seizure signals and discuss the estimation and modeling of the instantaneous frequency related to the main ridge of the TFR.

Detection and description of non-linear interdependence in normal multichannel human EEG data

Objectives: This study examines human scalp electroencephalographic (EEG) data for evidence of non-linear interdependence between posterior channels. The spectral and phase properties of those epochs of EEG exhibiting non-linear interdependence are studied. Methods: Scalp EEG data was collected from 40 healthy subjects. A technique for the detection of non-linear interdependence was applied to 2.048 s segments of posterior bipolar electrode data. Amplitude-adjusted phase-randomized surrogate data was used to statistically determine which EEG epochs exhibited non-linear interdependence. Results: Statistically significant evidence of non-linear interactions were evident in 2.9% (eyes open) to 4.8% (eyes closed) of the epochs. In the eyes-open recordings, these epochs exhibited a peak in the spectral and cross-spectral density functions at about 10 Hz. Two types of EEG epochs are evident in the eyes-closed recordings; one type exhibits a peak in the spectral density and cross-spectrum at 8 Hz. The other type has increased spectral and cross-spectral power across faster frequencies. Epochs identified as exhibiting non-linear interdependence display a tendency towards phase interdependencies across and between a broad range of frequencies. Conclusions: Non-linear interdependence is detectable in a small number of multichannel EEG epochs, and makes a contribution to the alpha rhythm. Non-linear interdependence produces spatially distributed activity that exhibits phase synchronization between oscillations present at different frequencies. The possible physiological significance of these findings are discussed with reference to the dynamical properties of neural systems and the role of synchronous activity in the neocortex. (C) 2002 Elsevier Science Ireland Ltd. All rights reserved.

Uma contribuição ao estudo de sinais de EEG para avaliar estados emocionais e mentais de crianças com autismo na interação com robô móvel

O Transtorno do Espectro do Autismo (TEA) caracteriza-se por uma série de distúrbios cognitivos e neurocomportamentais e sua prevalência mundial é estimada em 1 criança com TEA a cada 160 crianças com típico desenvolvimento (TD). Indivíduos com TEA apresentam dificuldade em interpretar as emoções alheias e em expressar sentimentos. As emoções podem ser associadas à manifestação de sinais fisiológicos, e, dentre eles, os sinais cerebrais têm sido muito abordados. A detecção dos sinais cerebrais de crianças com TEA pode ser benéfica para o esclarecimento de suas emoções e expressões. Atualmente, muitas pesquisas integram a robótica ao tratamento pedagógico do TEA, através da interação com crianças com esse transtorno, estimulando habilidades sociais, como a imitação e a comunicação. A avaliação dos estados mentais de crianças com TEA durante a sua interação com um robô móvel é promissora e assume um aspecto inovador. Assim, os objetivos deste trabalho foram captar sinais cerebrais de crianças com TEA e de crianças com TD, como grupo controle, para o estudo de seus estados emocionais e para avaliar seus estados mentais durante a interação com um robô móvel, e avaliar também a interação dessas crianças com o robô, através de escalas quantitativas. A técnica de registro dos sinais cerebrais escolhida foi a eletroencefalografia (EEG), a qual utiliza eletrodos colocados de forma não invasiva e não dolorosa sobre o couro cabeludo da criança. Os métodos para avaliar a eficiência do uso da robótica nessa interação foram baseados em duas escalas internacionais quantitativas: Escala de Alcance de Metas (do inglês Goal Attainment Scaling - GAS) e Escala de Usabilidade de Sistemas (do inglês System Usability Scale - SUS). Os resultados obtidos mostraram que, pela técnica de EEG, foi possível classificar os estados emocionais de crianças com TD e com TEA e analisar a atividade cerebral durante o início da interação com o robô, através dos ritmos alfa e beta. Com as avaliações GAS e SUS, verificou-se que o robô móvel pode ser considerado uma potencial ferramenta terapêutica para crianças com TEA.

Effects of Age-Related Cognitive Deficits on EEG Phase Coherence

Body and brain undergo several changes with aging. One of these changes is the loss of neuroplasticity, which leads to the decrease of cognitive abilities. Hence the necessity of stopping or reversing these changes is of utmost importance to contemporary society. In the present work, electroencephalogram (EEG) markers of cognitive decline are sought whilst the subjects perform the Wisconsin Card Sorting Test (WCST). Considering the expected age-related cognitive deficits, WCST was applied to young and elder participants. The results suggest that coherence on theta and alpha EEG rhythms decrease with aging and increase with performance. Additionally, theta phase coherence seems more sensitive to performance, while alpha synchronization appears as a potential ageing marker.