Dans de nombreux pays, le taux de mortalité des motocyclistes est beaucoup plus élevé que celui des autres conducteurs de véhicules. Parmi de nombreux autres facteurs, les collisions arrière des motocyclettes contribuent fortement à ces décès de motards. Les systèmes de détection de collision peuvent être utilisés pour minimiser ces accidents mortels. Cependant, la plupart des systèmes de détection de collision existants n'identifient pas le type de danger potentiel auquel sont exposés les motocyclistes. Chaque système d'alerte de collision utilise une technique de détection de collision distincte, ce qui limite ses performances et rend impératif l'étude de son efficacité. Malheureusement, aucun travail de ce type n'a été signalé dans ce domaine particulier pour les motocyclistes. Par conséquent, il est important d'étudier la réponse physiologique du motocycliste contre ces systèmes d'alerte de collision. Dans cette recherche, une méthode de détection et de classification des véhicules approchant par l'arrière est présentée. Pour la détection de collision, une approche basee vision et la technique basee sur le son ont été utilisées. Pour les techniques visuelles et acoustiques, des caractéristiques d'apparence et de spectre de puissance ont été utilisées, respectivement, pour détecter le véhicule qui s'approche à l'extrémité arrière de la motocyclette. En ce qui concerne la classification des véhicules, seule une technique acoustique est utilisée; un spectre de puissance acoustique et des caractéristiques énergétiques sont utilisés pour classer les véhicules qui approchent. Deux types d'ensembles de données, à savoir des ensembles de données acquises durant ce travail (obtenues en plaçant une caméra à l'arrière d'une motocyclette) et des ensembles de données disponibles telechargeables (pour la détection visuelle et pour la classification audio des véhicules) sont utilisés pour la validation. La méthodologie proposée a permis de détecter et de classer les véhicules pour des ensembles de données acquises durent cette these. De même, pour les ensembles de données disponibles , le taux positif vrai le plus élevé et le taux de détection faux le plus faible ont été atteints par rapport aux méthodes de l etat de l art. En outre, une étude physiologique basée sur le potentiel lié à l'événement (ERP) a été réalisée sur les motocyclistes afin d'étudier leurs réponses vis-à-vis du système d'alerte de collision arrière. Deux types d'avertissements auditifs (c'est-à-dire verbal et buzzer) sont utilisés pour ce système d'avertissement. Pour étudier la réponse des motocyclistes, les composantes N1, N2, P3 et N400 ont été extraits des données d'électroencéphalographie (EEG). Ces systèmes d avertissement ont montré des effets positifs au niveau des neuronal sur les motocyclistes et réduisent leur temps de réaction et les ressources attentionnelles nécessaires pour traiter correctement la cible. En résumé, le système d'avertissement de collision par l'arrière proposé avec des avertissements verbaux auditifs augmente considérablement la vigilance du motocycliste et peut être utile pour éviter les scénarios possibles de collision arrière. / In many countries, motorcyclist fatality rate is much higher than that of other vehicle drivers. Among many other factors, motorcycle rear-end collisions are also contributing to these biker fatalities. Collision detection systems can be used to minimize these fatalities. However, most of the existing collision detection systems do not identify the type of potential hazard faced by motorcyclists. Every collision warning system used a distinctive collision detection technique, which limits its performance and makes it imperative to study its effectiveness. Unfortunately, no such work has been reported in that particular domain for motorcyclists. Therefore, it is important to study the physiological response of the motorcyclist against these collision warning systems. In this research, a rear end vehicle detection and classification method is presented for motorcyclists. For collision detection, vision technique and acoustic technique have been used. For visual and acoustic techniques, appearance features and power spectrum have been used, respectively, to detect the approaching vehicle at the rear end of the motorcycle. As for the vehicle classification, only an acoustic technique is utilized; an acoustic power spectrum and energy features are used to classify the approaching vehicles. Two types of datasets which are comprised of self-recorded datasets (obtained by placing a camera at the rear end of a motorcycle) and online datasets (for vision-based vehicle detection and for audio based vehicle classification techniques) are used for validation. Proposed methodology successfully detected and classified the vehicle for self-recorded datasets. Similarly, for online datasets, the higher true positive rate and less false detection rate has been achieved as compared to the existing state of the art methods. Moreover, an event-related potential (ERP) based physiological study has been performed on motorcyclists to investigate their responses towards the rear end collision warning system. Two types of auditory warnings (i.e., verbal and buzzer) are used for this warning system. To study the response of the motorcyclists, the N1, N2, P3, and N400 components have been extracted from the Electroencephalography (EEG) data. These introduced systems have shown positive effects at neural levels on motorcyclists and reduce their reaction time and attentional resources required for processing the target correctly. In summary, the proposed rear-end collision warning system with auditory verbal warnings significantly increases the alertness of the motorcyclist and can be helpful to avoid the possible rear-end collision scenarios.

The standard approach to analyzing brain electrical activity is to examine the spectral density function (SDF) and identify frequency bands, defined apriori, that have the most substantial relative contributions to the overall variance of the signal. However, a limitation of this approach is that the precise frequency and bandwidth of oscillations are not uniform across cognitive demands. Thus, these bands should not be arbitrarily set in any analysis. To overcome this limitation, we propose three Bayesian Non-parametric models for time series decomposition which are data-driven approaches that identifies (i) the number of prominent spectral peaks, (ii) the frequency peak locations, and (iii) their corresponding bandwidths (or spread of power around the peaks). The standardized SDF is represented as a Dirichlet process mixture based on a kernel derived from second-order auto-regressive processes which completely characterize the location (peak) and scale (bandwidth) parameters. A Metropolis-Hastings within Gibbs algorithm is developed for sampling from the posterior distribution of the mixture parameters for each project. Simulation studies demonstrate the robustness and performance of the proposed methods. The methods developed were applied to analyze local field potential (LFP) activity from the hippocampus of laboratory rats across different conditions in a non-spatial sequence memory experiment to identify the most prominent frequency bands and examine the link between specific patterns of brain oscillatory activity and trial-specific cognitive demands. The second application study 61 EEG channels from two subjects performing a visual recognition task to discover frequency-specific oscillations present across brain zones. The third application extends the model to characterize the data coming from 10 alcoholics and 10 controls across three experimental conditions across 30 trials. The proposed models generate a framework to condense the oscillatory behavior of populations across different tasks isolating the target fundamental components allowing the practitioner different perspectives of analysis.

Wiederholte Messungen mehrerer Individuen sind von entscheidender Bedeutung für die Psychologie. Beispiele sind längsschnittliche Paneldaten und Elektroenzephalografie-Daten (EEG-Daten). In dieser Arbeit entwickle ich für jede dieser beiden Datenarten neue Analyseansätze, denen Methoden des maschinellen Lernens zu Grunde liegen. Für Paneldaten entwickle ich Gauß-Prozess-Panelmodellierung (GPPM), die auf der flexiblen Bayesschen Methode der Gauß-Prozess-Regression basiert. Der Vergleich von GPPM mit längsschnittlicher Strukturgleichungsmodellierung (lSEM), welche die meisten herkömmlichen Panelmodellierungsmethoden als Sonderfälle enthält, zeigt, dass lSEM wiederum als Sonderfall von GPPM aufgefasst werden kann. Im Gegensatz zu lSEM eignet sich GPPM gut zur zeitkontinuierlichen Modellierung, kann eine größere Menge von Modellen beschreiben, und beinhaltet einen einfachen Ansatz zur Generierung personenspezifischer Vorhersagen. Ich zeige, dass die implementierte GPPM-Darstellung gegenüber bestehender SEM Software eine bis zu neunfach beschleunigte Parameterschätzung erlaubt. Für EEG-Daten entwickle ich einen personenspezifischen Modellierungsansatz zur Identifizierung und Quantifizierung von Unterschieden zwischen Personen, die in konventionellen EEG-Analyseverfahren ignoriert werden. Im Rahmen dieses Ansatzes wird aus einer großen Menge hypothetischer Kandidatenmodelle das beste Modell für jede Person ausgewählt. Zur Modellauswahl wird ein Verfahren aus dem Bereich des maschinellen Lernens genutzt. Ich zeig ich, wie die Modelle sowohl auf der Personen- als auch auf der Gruppenebene interpretiert werden können. Ich validiere den vorgeschlagenen Ansatz anhand von Daten zur Arbeitsgedächtnisleistung. Die Ergebnisse verdeutlichen, dass die erhaltenen personenspezifischen Modelle eine genauere Beschreibung des Zusammenhangs von Verhalten und Hirnaktivität ermöglichen als konventionelle, nicht personenspezifische EEG-Analyseverfahren. / Repeated measures obtained from multiple individuals are of crucial importance for developmental research. Examples of repeated measures obtained from multiple individuals include longitudinal panel and electroencephalography (EEG) data. In this thesis, I develop a novel analysis approach based on machine learning methods for each of these two data modalities. For longitudinal panel data, I develop Gaussian process panel modeling (GPPM), which is based on the flexible Bayesian approach of Gaussian process regression. The comparison of GPPM with longitudinal structural equation modeling (SEM), which contains most conventional panel modeling approaches as special cases, reveals that GPPM in turn encompasses longitudinal SEM as a special case. In contrast to longitudinal SEM, GPPM is well suited for continuous-time modeling, can express a larger set of models, and includes a straightforward approach to obtain person-specific predictions. The comparison between the developed GPPM toolbox and existing SEM software reveals that the GPPM representation of popular longitudinal SEMs decreases the amount of time needed for parameter estimation up to ninefold. For EEG data, I develop an approach to derive person-specific models for the identification and quantification of between-person differences in EEG responses that are ignored by conventional EEG analysis methods. The approach relies on a framework that selects the best model for each person based on a large set of hypothesized candidate models using a model selection approach from machine learning. I show how the obtained models can be interpreted on the individual as well as on the group level. I validate the proposed approach on a working memory data set. The results demonstrate that the obtained person-specific models provide a more accurate description of the link between behavior and EEG data than the conventional nonspecific EEG analysis approach.

O principal objetivo do presente trabalho é propor, desenvolver, testar e apresentar um método para a classificação do grau de desenvolvimento da capacidade cognitiva espacial de diferentes indivíduos. 37 alunos de graduação tiveram seus eletroencefalogramas (EEGs) capturados enquanto estavam engajados em tarefas de rotação mental de imagens tridimensionais. Seu grau de desenvolvimento da capacidade cognitiva espacial foi avaliado utilizando-se um teste psicológico BPR-5. O maior expoente de Lyapunov (LLE) foi calculado a partir de cada um dos 8 canais dos EEGs capturados. OS LLEs foram então utilizados como tuplas de entrada para 5 diferentes classificadores: i) perceptron de múltiplas camadas, ii) rede neural artificial de funções de base radial, iii) perceptron votado, iv) máquinas de vetor de suporte, e v) k-vizinhos. O melhor resultado foi obtido utilizando-se uma RBF com 4 clusters e a função de kernel Puk. Também foi realizada uma análise estatística das diferenças de atividade cerebral, baseando-se nos LLEs calculados, entre os dois grupos de interesse: SI+ (indivíduos com um suposto maior grau de desenvolvimento da sua capacidade cognitiva espacial) e SI- (grupo de controle) durante a realização de tarefas de rotação mental de imagens tridimensionais. Uma diferença média de 16% foi encontrada entre os dois grupos. O método de classificação proposto pode vir a contribuir e a interagir com outros processos na analise e no estudo da capacidade cognitiva espacial humana, assim como no entendimento da inteligência humana como um todo. Um melhor entendimento e avaliação das capacidades cognitivas de um indivíduo podem sugerir a este elementos de motivação, facilidade ou de inclinações naturais suas, podendo, provavelmente, afetar as decisões da sua vida e carreira de uma forma positiva. / The main objective of the present work is to propose, develop, test, and show a method for classifying the spatial cognition degree of development on different individuals. Thirty-Seven undergraduate students had their electroencephalogram (EEG) recorded while engaged in 3-D images mental rotation tasks. Their spatial cognition degree of development was evaluated using a BPR-5 psychological test. The Largest Lyapunov Exponent (LLE) was calculated from each of the 8 electrodes recorded in each EEG. The LLEs were used as input for five different classifiers: i) multi-layer perceptron artificial neural network, ii) radial base functions artificial neural network, iii) voted perceptron artificial neural network, iv) support vector machines, and v) K-Nearest Neighbors. The best result was achieved by using a RBF with 4 clusters and Puk kernel function. Also a statistical analysis of the brain activity, based in the calculated LLEs, differences between two interest groups: SI+ (participants with an alleged higher degree of development of their spatial cognition) and SI- (control group) during the performing of mental rotation of tridimensional images tasks was done.. An average difference of 16% was found between both groups. The proposed classification method can contribute and interact with other processes in the analysis and study of human spatial cognition, as in the understanding of the human intelligence at all. A better understanding and evaluation of the cognitive capabilities of an individual could suggest him elements of motivation, ease or natural inclinations, possibly affecting the decisions of his life and carrier positively.

O principal objetivo do presente trabalho é propor, desenvolver, testar e apresentar um método para a classificação do grau de desenvolvimento da capacidade cognitiva espacial de diferentes indivíduos. 37 alunos de graduação tiveram seus eletroencefalogramas (EEGs) capturados enquanto estavam engajados em tarefas de rotação mental de imagens tridimensionais. Seu grau de desenvolvimento da capacidade cognitiva espacial foi avaliado utilizando-se um teste psicológico BPR-5. O maior expoente de Lyapunov (LLE) foi calculado a partir de cada um dos 8 canais dos EEGs capturados. OS LLEs foram então utilizados como tuplas de entrada para 5 diferentes classificadores: i) perceptron de múltiplas camadas, ii) rede neural artificial de funções de base radial, iii) perceptron votado, iv) máquinas de vetor de suporte, e v) k-vizinhos. O melhor resultado foi obtido utilizando-se uma RBF com 4 clusters e a função de kernel Puk. Também foi realizada uma análise estatística das diferenças de atividade cerebral, baseando-se nos LLEs calculados, entre os dois grupos de interesse: SI+ (indivíduos com um suposto maior grau de desenvolvimento da sua capacidade cognitiva espacial) e SI- (grupo de controle) durante a realização de tarefas de rotação mental de imagens tridimensionais. Uma diferença média de 16% foi encontrada entre os dois grupos. O método de classificação proposto pode vir a contribuir e a interagir com outros processos na analise e no estudo da capacidade cognitiva espacial humana, assim como no entendimento da inteligência humana como um todo. Um melhor entendimento e avaliação das capacidades cognitivas de um indivíduo podem sugerir a este elementos de motivação, facilidade ou de inclinações naturais suas, podendo, provavelmente, afetar as decisões da sua vida e carreira de uma forma positiva. / The main objective of the present work is to propose, develop, test, and show a method for classifying the spatial cognition degree of development on different individuals. Thirty-Seven undergraduate students had their electroencephalogram (EEG) recorded while engaged in 3-D images mental rotation tasks. Their spatial cognition degree of development was evaluated using a BPR-5 psychological test. The Largest Lyapunov Exponent (LLE) was calculated from each of the 8 electrodes recorded in each EEG. The LLEs were used as input for five different classifiers: i) multi-layer perceptron artificial neural network, ii) radial base functions artificial neural network, iii) voted perceptron artificial neural network, iv) support vector machines, and v) K-Nearest Neighbors. The best result was achieved by using a RBF with 4 clusters and Puk kernel function. Also a statistical analysis of the brain activity, based in the calculated LLEs, differences between two interest groups: SI+ (participants with an alleged higher degree of development of their spatial cognition) and SI- (control group) during the performing of mental rotation of tridimensional images tasks was done.. An average difference of 16% was found between both groups. The proposed classification method can contribute and interact with other processes in the analysis and study of human spatial cognition, as in the understanding of the human intelligence at all. A better understanding and evaluation of the cognitive capabilities of an individual could suggest him elements of motivation, ease or natural inclinations, possibly affecting the decisions of his life and carrier positively.

O principal objetivo do presente trabalho é propor, desenvolver, testar e apresentar um método para a classificação do grau de desenvolvimento da capacidade cognitiva espacial de diferentes indivíduos. 37 alunos de graduação tiveram seus eletroencefalogramas (EEGs) capturados enquanto estavam engajados em tarefas de rotação mental de imagens tridimensionais. Seu grau de desenvolvimento da capacidade cognitiva espacial foi avaliado utilizando-se um teste psicológico BPR-5. O maior expoente de Lyapunov (LLE) foi calculado a partir de cada um dos 8 canais dos EEGs capturados. OS LLEs foram então utilizados como tuplas de entrada para 5 diferentes classificadores: i) perceptron de múltiplas camadas, ii) rede neural artificial de funções de base radial, iii) perceptron votado, iv) máquinas de vetor de suporte, e v) k-vizinhos. O melhor resultado foi obtido utilizando-se uma RBF com 4 clusters e a função de kernel Puk. Também foi realizada uma análise estatística das diferenças de atividade cerebral, baseando-se nos LLEs calculados, entre os dois grupos de interesse: SI+ (indivíduos com um suposto maior grau de desenvolvimento da sua capacidade cognitiva espacial) e SI- (grupo de controle) durante a realização de tarefas de rotação mental de imagens tridimensionais. Uma diferença média de 16% foi encontrada entre os dois grupos. O método de classificação proposto pode vir a contribuir e a interagir com outros processos na analise e no estudo da capacidade cognitiva espacial humana, assim como no entendimento da inteligência humana como um todo. Um melhor entendimento e avaliação das capacidades cognitivas de um indivíduo podem sugerir a este elementos de motivação, facilidade ou de inclinações naturais suas, podendo, provavelmente, afetar as decisões da sua vida e carreira de uma forma positiva. / The main objective of the present work is to propose, develop, test, and show a method for classifying the spatial cognition degree of development on different individuals. Thirty-Seven undergraduate students had their electroencephalogram (EEG) recorded while engaged in 3-D images mental rotation tasks. Their spatial cognition degree of development was evaluated using a BPR-5 psychological test. The Largest Lyapunov Exponent (LLE) was calculated from each of the 8 electrodes recorded in each EEG. The LLEs were used as input for five different classifiers: i) multi-layer perceptron artificial neural network, ii) radial base functions artificial neural network, iii) voted perceptron artificial neural network, iv) support vector machines, and v) K-Nearest Neighbors. The best result was achieved by using a RBF with 4 clusters and Puk kernel function. Also a statistical analysis of the brain activity, based in the calculated LLEs, differences between two interest groups: SI+ (participants with an alleged higher degree of development of their spatial cognition) and SI- (control group) during the performing of mental rotation of tridimensional images tasks was done.. An average difference of 16% was found between both groups. The proposed classification method can contribute and interact with other processes in the analysis and study of human spatial cognition, as in the understanding of the human intelligence at all. A better understanding and evaluation of the cognitive capabilities of an individual could suggest him elements of motivation, ease or natural inclinations, possibly affecting the decisions of his life and carrier positively.

This thesis deals with analysis and processing of the Sleep Electroencephalogram (EEG) signals. The scope of this thesis can be split into several areas. The first area is application of the Independent Component Analysis (ICA) method for EEG signal analysis. A model of EEG signal formation is proposed and conditions under which this model is valid are examined. It is shown that ICA can be used to remove non-deterministic artifacts contained in the EEG signals. The second area of interest is analysis of stationarity of the Sleep EEG signal. Methods to identify stationary signal segments and to analyze statistical properties of these stationary segments are presented. The third area of interest focuses on spectral analysis of the Sleep EEG signals. Analyses are performed that shows the processes that form particular parts of EEG signals spectrum. Also, random signals that are an integral part of the EEG signals analysis are performed. The last area of interest focuses on elimination of the transition processes that are caused by the filtering of the short EEG signal segments.

Les cardiopathies congénitales (CC) représentent l’une des anomalies congénitales les plus communes. Près de la moitié des nouveau-nés atteints nécessitent une chirurgie cardiaque au cours de leur premier mois de vie, ce qui les expose à un risque accru d’atteintes cérébrales et a été associé à un développement neurologique altéré. Il est donc nécessaire de trouver des stratégies neuroprotectrices efficaces pour améliorer leur évolution neurologique. Ce mémoire décrit l’influence des doses d’agents pharmacologiques administrées en période périopératoire sur la récupération de l’EEG en postopératoire, ainsi que sur les résultats neurodéveloppementaux évalués à 12 et 24 mois. Nous avons émis l'hypothèse que l’administration de certains agents pharmacologiques de sédation, d’anesthésie et d’analgésie en période intra- et postopératoire pourrait avoir un effet bénéfique au niveau de la récupération cérébrale à court- et long-terme, soit en réduisant la discontinuité observée sur l’EEG postopératoire et/ou en améliorant la performance obtenue lors de divers tests neurodéveloppementaux standardisés. Nos résultats semblent préconiser l’administration de plus fortes doses d’opioïdes en période périopératoire pour réduire la douleur et le stress induit par la chirurgie cardiaque, ce qui serait associé avec une amélioration du pronostic neurodéveloppemental. L’administration de plus fortes doses de dexmédétomidine et de midazolam était marquée par une récupération retardée de l’activité cérébrale après la chirurgie cardiaque sans aucun impact remarquable sur les résultats neurodéveloppementaux dans notre cohorte. Similairement, les doses de kétamine n’ont eu aucun impact sur la récupération cérébrale post-opératoire ou les bilans neurodéveloppementaux. / Congenital heart disease (CHD) is one of the most common congenital defects. Almost half of neonates affected require heart surgery during their first month of life, which exposes them to an increased risk of brain damage and has been associated with impaired neurodevelopment. It is therefore important to identify effective neuroprotective strategies to improve their neurological outcomes. This thesis describes the influence of the administration of pharmacological agents during the perioperative period on postoperative EEG recovery and neurodevelopmental outcomes at 12 and 24 months. We hypothesized that the administration of certain pharmacological agents for sedation, anesthesia and analgesia given during the intra- and post-operative period could have a beneficial effect on cerebral recovery, by decreasing the postoperative EEG discontinuity and/or improving neurodevelopmental outcomes. Our results suggest that the administration of higher doses of opioids during the perioperative period could reduce the pain and stress induced by cardiac surgery and are thus associated with improved neurodevelopmental outcomes. The administration of higher doses of dexmedetomidine and midazolam was marked by a delayed recovery of brain activity after cardiac surgery without any noticeable impact on neurodevelopmental outcomes in our cohort. Similarly, the doses of ketamine had no impact on postoperative brain recovery and on their long-term development.

Environ 2-3% d’enfants avec convulsions fébriles (CF) développent une épilepsie, mais les outils cliniques existants ne permettent pas d’identifier les enfants susceptibles de développer une épilepsie post-convulsion fébrile. Des études ont mis en évidence des anomalies d’EEG quantifiée, et plus particulièrement en réponse à la stimulation lumineuse intermittente (SLI), chez des patients épileptiques. Aucune étude n’a analysé ces paramètres chez l’enfant avec CF et il importe de déterminer s’ils sont utiles pour évaluer le pronostic des CF. Les objectifs de ce programme de recherche étaient d’identifier, d’une part, des facteurs de risque cliniques qui déterminent le développement de l’épilepsie après des CF et, d’autre part, des marqueurs électrophysiologiques quantitatifs qui différencieraient les enfants avec CF des témoins et pourraient aider à évaluer leur pronostic. Afin de répondre à notre premier objectif, nous avons analysé les dossiers de 482 enfants avec CF, âgés de 3 mois à 6 ans. En utilisant des statistiques de survie, nous avons décrit les facteurs de risque pour développer une épilepsie partielle (antécédents prénataux, retard de développement, CF prolongées et focales) et généralisée (antécédents familiaux d’épilepsie, CF récurrentes et après l’âge de 4 ans). De plus, nous avons identifié trois phénotypes cliniques distincts ayant un pronostic différent : (i) CF simples avec des antécédents familiaux de CF et sans risque d’épilepsie ultérieure; (ii) CF récurrentes avec des antécédents familiaux d’épilepsie et un risque d’épilepsie généralisée; (iii) CF focales avec des antécédents familiaux d’épilepsie et un risque d’épilepsie partielle. Afin de répondre à notre deuxième objectif, nous avons d’abord analysé les potentiels visuels steady-state (PEVSS) évoqués par la SLI (5, 7,5, 10 et 12,5 Hz) en fonction de l’âge. Le tracé EEG de haute densité (128 canaux) a été enregistré chez 61 enfants âgés entre 6 mois et 16 ans et 8 adultes normaux. Nous rapportons un développement topographique différent de l’alignement de phase des composantes des PEVSS de basses (5-15 Hz) et de hautes (30-50 Hz) fréquences. Ainsi, l’alignement de phase des composantes de basses fréquences augmente en fonction de l’âge seulement au niveau des régions occipitale et frontale. Par contre, les composantes de hautes fréquences augmentent au niveau de toutes les régions cérébrales. Puis, en utilisant cette même méthodologie, nous avons investigué si les enfants avec CF présentent des anomalies des composantes gamma (50-100 Hz) des PEVSS auprès de 12 cas de CF, 5 frères et sœurs des enfants avec CF et 15 témoins entre 6 mois et 3 ans. Nous montrons une augmentation de la magnitude et de l’alignement de phase des composantes gamma des PEVSS chez les enfants avec CF comparés au groupe témoin et à la fratrie. Ces travaux ont permis d’identifier des phénotypes électro-cliniques d’intérêt qui différencient les enfants avec CF des enfants témoins et de leur fratrie. L’étape suivante sera de vérifier s’il y a une association entre les anomalies retrouvées, la présentation clinique et le pronostic des CF. Cela pourrait éventuellement aider à identifier les enfants à haut risque de développer une épilepsie et permettre l’institution d’un traitement neuroprotecteur précoce. / The incidence of epilepsy in children with febrile seizures (FS) varies from 2 to 3%, but available clinical tools do not allow the identification of those children who will later develop epilepsy. Evidences have shown quantitative EEG abnormalities, more particularly revealed by intermittent photic stimulation (IPS), in patients with epilepsy. No studies have yet examined quantitative EEG parameters in children with FS. It is not known either whether they can be relevant to the evaluation of FSs prognosis. The objectives of this research program were to identify, first, clinical risk factors for developing epilepsy after FS and, second, to determine quantitative EEG markers that differentiate FS patients from normal controls and may aid to evaluate their prognosis. In order to meet our first objective, we reviewed the charts of 482 children with FS, aged 3 months to 6 years. Using survival statistics, we described risk factors for developing partial (prenatal antecedents, developmental delay, prolonged and focal FS) and generalized (family history of epilepsy, recurrent FS and FS after the age of 4 years) epilepsy after FS. In addition, we identified several distinct clinical phenotypes related to the prognosis of FS: (i) simple FS with a family history of FS, not related to a subsequent epilepsy, (ii) recurrent FS with a family history of epilepsy and an increased risk of generalised epilepsy and (iii) focal FS with a family history of epilepsy and an increased risk of partial epilepsy. In order to meet our second objective, we analyzed the steady-state visual potentials (SSVEP) evoked by IPS (5, 7.5, 10 and 12.5 Hz) as a function of age. The high density EEG (128 channels) was recorded in 61 normal children between 6 months and 16 years of age and 8 adults. We showed different topographical development of low (5-15 Hz) and high (30-50 Hz) frequency SSVEP components phase alignment. Thus, low frequency phase alignment increased with age only over the frontal and occipital regions, whereas high frequency phase alignment increased over all cerebral regions. Then, using the same methodology, we investigated whether children with FS show abnormalities of gamma frequency SSVEP components. We show an increase of both magnitude and phase alignment of the gamma frequency SSVEP components in 12 FS patients compared to 5 siblings of FS patients and 15 control children between 6 and 36 months of age. This study has identified distinct electro-clinical phenotypes that differentiate FS patients from the group of siblings and controls. Future studies should investigate whether detected abnormalities are associated with the clinical presentation of FS and their prognosis. This could help identify children with FSs who will later develop epilepsy and would eventually allow the institution of an early neuroprotective treatment.

Environ 2-3% d’enfants avec convulsions fébriles (CF) développent une épilepsie, mais les outils cliniques existants ne permettent pas d’identifier les enfants susceptibles de développer une épilepsie post-convulsion fébrile. Des études ont mis en évidence des anomalies d’EEG quantifiée, et plus particulièrement en réponse à la stimulation lumineuse intermittente (SLI), chez des patients épileptiques. Aucune étude n’a analysé ces paramètres chez l’enfant avec CF et il importe de déterminer s’ils sont utiles pour évaluer le pronostic des CF. Les objectifs de ce programme de recherche étaient d’identifier, d’une part, des facteurs de risque cliniques qui déterminent le développement de l’épilepsie après des CF et, d’autre part, des marqueurs électrophysiologiques quantitatifs qui différencieraient les enfants avec CF des témoins et pourraient aider à évaluer leur pronostic. Afin de répondre à notre premier objectif, nous avons analysé les dossiers de 482 enfants avec CF, âgés de 3 mois à 6 ans. En utilisant des statistiques de survie, nous avons décrit les facteurs de risque pour développer une épilepsie partielle (antécédents prénataux, retard de développement, CF prolongées et focales) et généralisée (antécédents familiaux d’épilepsie, CF récurrentes et après l’âge de 4 ans). De plus, nous avons identifié trois phénotypes cliniques distincts ayant un pronostic différent : (i) CF simples avec des antécédents familiaux de CF et sans risque d’épilepsie ultérieure; (ii) CF récurrentes avec des antécédents familiaux d’épilepsie et un risque d’épilepsie généralisée; (iii) CF focales avec des antécédents familiaux d’épilepsie et un risque d’épilepsie partielle. Afin de répondre à notre deuxième objectif, nous avons d’abord analysé les potentiels visuels steady-state (PEVSS) évoqués par la SLI (5, 7,5, 10 et 12,5 Hz) en fonction de l’âge. Le tracé EEG de haute densité (128 canaux) a été enregistré chez 61 enfants âgés entre 6 mois et 16 ans et 8 adultes normaux. Nous rapportons un développement topographique différent de l’alignement de phase des composantes des PEVSS de basses (5-15 Hz) et de hautes (30-50 Hz) fréquences. Ainsi, l’alignement de phase des composantes de basses fréquences augmente en fonction de l’âge seulement au niveau des régions occipitale et frontale. Par contre, les composantes de hautes fréquences augmentent au niveau de toutes les régions cérébrales. Puis, en utilisant cette même méthodologie, nous avons investigué si les enfants avec CF présentent des anomalies des composantes gamma (50-100 Hz) des PEVSS auprès de 12 cas de CF, 5 frères et sœurs des enfants avec CF et 15 témoins entre 6 mois et 3 ans. Nous montrons une augmentation de la magnitude et de l’alignement de phase des composantes gamma des PEVSS chez les enfants avec CF comparés au groupe témoin et à la fratrie. Ces travaux ont permis d’identifier des phénotypes électro-cliniques d’intérêt qui différencient les enfants avec CF des enfants témoins et de leur fratrie. L’étape suivante sera de vérifier s’il y a une association entre les anomalies retrouvées, la présentation clinique et le pronostic des CF. Cela pourrait éventuellement aider à identifier les enfants à haut risque de développer une épilepsie et permettre l’institution d’un traitement neuroprotecteur précoce. / The incidence of epilepsy in children with febrile seizures (FS) varies from 2 to 3%, but available clinical tools do not allow the identification of those children who will later develop epilepsy. Evidences have shown quantitative EEG abnormalities, more particularly revealed by intermittent photic stimulation (IPS), in patients with epilepsy. No studies have yet examined quantitative EEG parameters in children with FS. It is not known either whether they can be relevant to the evaluation of FSs prognosis. The objectives of this research program were to identify, first, clinical risk factors for developing epilepsy after FS and, second, to determine quantitative EEG markers that differentiate FS patients from normal controls and may aid to evaluate their prognosis. In order to meet our first objective, we reviewed the charts of 482 children with FS, aged 3 months to 6 years. Using survival statistics, we described risk factors for developing partial (prenatal antecedents, developmental delay, prolonged and focal FS) and generalized (family history of epilepsy, recurrent FS and FS after the age of 4 years) epilepsy after FS. In addition, we identified several distinct clinical phenotypes related to the prognosis of FS: (i) simple FS with a family history of FS, not related to a subsequent epilepsy, (ii) recurrent FS with a family history of epilepsy and an increased risk of generalised epilepsy and (iii) focal FS with a family history of epilepsy and an increased risk of partial epilepsy. In order to meet our second objective, we analyzed the steady-state visual potentials (SSVEP) evoked by IPS (5, 7.5, 10 and 12.5 Hz) as a function of age. The high density EEG (128 channels) was recorded in 61 normal children between 6 months and 16 years of age and 8 adults. We showed different topographical development of low (5-15 Hz) and high (30-50 Hz) frequency SSVEP components phase alignment. Thus, low frequency phase alignment increased with age only over the frontal and occipital regions, whereas high frequency phase alignment increased over all cerebral regions. Then, using the same methodology, we investigated whether children with FS show abnormalities of gamma frequency SSVEP components. We show an increase of both magnitude and phase alignment of the gamma frequency SSVEP components in 12 FS patients compared to 5 siblings of FS patients and 15 control children between 6 and 36 months of age. This study has identified distinct electro-clinical phenotypes that differentiate FS patients from the group of siblings and controls. Future studies should investigate whether detected abnormalities are associated with the clinical presentation of FS and their prognosis. This could help identify children with FSs who will later develop epilepsy and would eventually allow the institution of an early neuroprotective treatment.