Novas estratégias de pré-processamento, extração de atributos e classificação em sistemas BCI

Orientador: Romis Ribeiro de Faissol AttuxTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: As interfaces cérebro-computador (BCIs) visam controlar um dispositivo externo, utilizando diretamente os sinais cerebrais do usuário. Tais sistemas requerem uma série de etapas para processar e extrair atributos relevantes dos sinais observados para interpretar correta e eficientemente as intenções do usuário. Embora o campo tenha se desenvolvido continuamente e algumas dificuldades tenham sido superadas, ainda é necessário aumentar a capacidade de uso, melhorando sua capacidade de classificação e aumentando a confiabilidade de sua resposta. O objetivo clássico da pesquisa de BCI é apoiar a comunicação e o controle para usuários com comunicação prejudicada devido a doenças ou lesões. Aplicações típicas das BCI são a operação de cursores de interface, programas de escrita de texto ou dispositivos externos, como cadeiras de rodas, robôs e diferentes tipos de próteses. O usuário envia informações moduladas para a BCI, realizando tarefas mentais que produzem padrões cerebrais distintos. A BCI adquire sinais do cérebro do usuário e os traduz em comunicação adequada. Esta tese tem como objetivo desenvolver uma comunicação BCI não invasiva mais rápida e confiável baseada no estudo de diferentes técnicas que atuam nas etapas de processamento do sinal, considerando dois aspectos principais, a abordagem de aprendizado de máquina e a redução da complexidade na tarefa de aprendizado dos padrões mentais pelo usuário. A pesquisa foi focada em dois paradigmas de BCI, Imagética Motora (IM) e o potencial relacionado ao evento P300. Algoritmos de processamento de sinais para a detecção de ambos os padrões cerebrais foram aplicados e avaliados. O aspecto do pré-processamento foi a primeira perspectiva estudada, considerando como destacar a resposta dos fenômenos cerebrais, em relação ao ruído e a outras fontes de informação que talvez distorçam o sinal de EEG; isso em si é um passo que influenciará diretamente a resposta dos seguintes blocos de processamento e classificação. A Análise de Componente Independente (ICA) foi usada em conjunto com métodos de seleção de atributos e diferentes classificadores para separar as fontes originais relacionadas à dessincronização produzida pelo fenômeno de IM; esta foi uma tentativa de criar um tipo de filtro espacial que permitisse o sinal ser pré-processado, reduzindo a influência do ruído. Além disso, os resultados dos valores de classificação foram analisados considerando a comparação com métodos padrão de pré-processamento, como o filtro CAR. Os resultados mostraram que é possível separar os componentes relacionados à atividade motora. A proposta da ICA, em média, foi 4\% mais alta em porcentagem de precisão de classificação do que os resultados obtidos usando o CAR, ou quando nenhum filtro foi usado. O papel dos métodos que estudam a conectividade de diferentes áreas do cérebro foi avaliado como a segunda contribuição deste trabalho; Isso permitiu considerar aspectos que contemplam a complexidade da resposta cerebral de um usuário. A área da BCI precisa de uma interpretação mais profunda do que acontece no nível do cérebro em vários dos fenômenos estudados. A técnica utilizada para construir grafos de conectividade funcional foi a correntropia, esta medida foi utilizada para quantificar a similaridade; uma comparação foi feita usando também, as medidas de correlação de Spearman e Pearson. A conectividade funcional relaciona diferentes áreas do cérebro analisando sua atividade cerebral, de modo que o estudo do grafo foi avaliado utilizando três medidas de centralidade, onde a importância de um nó na rede é medida. Também, dois tipos de classificadores foram testados, comparando os resultados no nível de precisão de classificação. Em conclusão, a correntropia pode trazer mais informações para o estudo da conectividade do que o uso da correlação simples, o que trouxe melhorias nos resultados da classificação, especialmente quando ela foi utilizada com o classificador ELM. Finalmente, esta tese demonstra que os BCIs podem fornecer comunicação efetiva em uma aplicação onde a predição da resposta de classificação foi modelada, o que permitiu a otimização dos parâmetros do processamento de sinal realizado usando o filtro espacial xDAWN e um classificador FLDA para o problema do speller P300, buscando a melhor resposta para cada usuário. O modelo de predição utilizado foi Bayesiano e confirmou os resultados obtidos com a operação on-line do sistema, permitindo otimizar os parâmetros tanto do filtro quanto do classificador. Desta forma, foi visto que usando filtros com poucos canais de entrada, o modelo otimizado deu melhores resultados de acurácia de classificação do que os valores inicialmente obtidos ao treinar o filtro xDAWN para os mesmos casos. Os resultados obtidos mostraram que melhorias nos métodos do transdutor BCI, no pré-processamento, extração de características e classificação constituíram a base para alcançar uma comunicação BCI mais rápida e confiável. O avanço nos resultados da classificação foi obtido em todos os casos, comparado às técnicas que têm sido amplamente utilizadas e já mostraram eficácia para esse tipo de problema. No entanto, ainda há aspectos a considerar da resposta dos sujeitos para tipos específicos de paradigmas, lembrando que sua resposta pode variar ao longo de diferentes dias e as implicações reais disso na definição e no uso de diferentes métodos de processamento de sinalAbstract: Brain-computer interfaces (BCIs) aim to control an external device by directly employing user's brain signals. Such systems require a series of steps to process and extract relevant features from the observed signals to correctly and efficiently interpret the user's intentions. Although the field has been continuously developing and some difficulties have been overcome, it is still necessary to increase usability by enhancing their classification capacity and increasing the reliability of their response. The classical objective of BCI research is to support communication and control for users with impaired communication due to illness or injury. Typical BCI applications are the operation of interface cursors, spelling programs or external devices, such as wheelchairs, robots and different types of prostheses. The user sends modulated information to the BCI by engaging in mental tasks that produce distinct brain patterns. The BCI acquires signals from the user¿s brain and translates them into suitable communication. This thesis aims to develop faster and more reliable non-invasive BCI communication based on the study of different techniques that serve in the signal processing stages, considering two principal aspects, the machine learning approach, and the reduction of the complexity in the task of learning the mental patterns by the user. Research was focused on two BCI paradigms, Motor Imagery (MI) and the P300 event related potential (ERP). Signal processing algorithms for the detection of both brain patterns were applied and evaluated. The aspect of the pre-processing was the first perspective studied to consider how to highlight the response of brain phenomena, in relation to noise and other sources of information that maybe distorting the EEG signal; this in itself is a step that will directly influence the response of the following blocks of processing and classification. The Independent Component Analysis (ICA) was used in conjunction with feature selection methods and different classifiers to separate the original sources that are related to the desynchronization produced by MI phenomenon; an attempt was made to create a type of spatial filter that pre-processed the signal, reducing the influence of the noise. Furthermore, some of the classifications values were analyzed considering comparison when used other standard pre-processing methods, as the CAR filter. The results showed that it is possible to separate the components related to motor activity. The ICA proposal on average were 4\% higher in percent of classification accuracy than those obtained using CAR, or when no filter was used. The role of methods that study the connectivity of different brain areas were evaluated as the second contribution of this work; this allowed to consider aspects that contemplate the complexity of the brain response of a user. The area of BCI needs a deeper interpretation of what happens at the brain level in several of the studied phenomena. The technique used to build functional connectivity graphs was correntropy, this quantity was used to measure similarity, a comparison was made using also, the Spearman and Pearson correlation. Functional connectivity relates different brain areas activity, so the study of the graph was evaluated using three measures of centrality of graph, where the importance of a node in the network is measured. In addition, two types of classifiers were tested, comparing the results at the level of classification precision. In conclusion, the correntropy can bring more information for the study of connectivity than the use of the simple correlation, which brought improvements in the classification results especially when it was used with the ELM classifier. Finally, this thesis demonstrates that BCIs can provide effective communication in an application where the prediction of the classification response was modeled, which allowed the optimization of the parameters of the signal processing performed using the xDAWN spatial filter and a FLDA classifier for the problem of the P300 speller, seeking the best response for each user. The prediction model used was Bayesian and confirmed the results obtained with the on-line operation of the system, thus allowing to optimize the parameters of both the filter and the classifier. In this way it was seen that using filters with few inputs the optimized model gave better results of acuraccy classification than the values initially obtained when the training ofthe xDAWN filter was made for the same cases. The obtained results showed that improvements in the BCI transducer, pre-processing, feature extraction and classification methods constituted the basis to achieve faster and more reliable BCI communication. The advance in the classification results were obtained in all cases, compared to techniques that have been widely used and had already shown effectiveness for this type of problemsDoutoradoEngenharia de ComputaçãoDoutora em Engenharia Elétrica153311/2014-2CNP

Factorized Binary Search: change point detection in the network structure of multivariate high-dimensional time series

Functional magnetic resonance imaging (fMRI) time series data presents a unique opportunity to understand temporal brain connectivity, and models that uncover the complex dynamic workings of this organ are of keen interest in neuroscience. Change point models can capture and reflect the dynamic nature of brain connectivity, however methods that translate well into a high-dimensional context (where $p>>n$) are scarce. To this end, we introduce $\textit{factorized binary search}$ (FaBiSearch), a novel change point detection method in the network structure of multivariate high-dimensional time series. FaBiSearch uses non-negative matrix factorization, an unsupervised dimension reduction technique, and a new binary search algorithm to identify multiple change points. In addition, we propose a new method for network estimation for data between change points. We show that FaBiSearch outperforms another state-of-the-art method on simulated data sets and we apply FaBiSearch to a resting-state and to a task-based fMRI data set. In particular, for the task-based data set, we explore network dynamics during the reading of Chapter 9 in $\textit{Harry Potter and the Sorcerer's Stone}$ and find that change points across subjects coincide with key plot twists. Further, we find that the density of networks was positively related to the frequency of speech between characters in the story. Finally, we make all the methods discussed available in the R package $\textbf{fabisearch}$ on CRAN

Development of nonlinear techniques based on time-frequency representation and information theory for the analysis of EEG signals to assess different states of consciousness

Electroencephalogram (EEG) recordings provide insight into the changes in brain activity associated with various states of anesthesia, epilepsy, brain attentiveness, sleep disorders, brain disorders, etc. EEG's are complex signals whose statistical properties depend on both space and time. Their randomness and non-stationary characteristics make them impossible to be described in an accurate way with a simple technique, requiring analysis and characterization involves techniques that take into account their non-stationarity. For that, new advanced techniques in order to improve the efficiency of the EEG based methods used in the clinical practice have to be developed. The main objective of this thesis was to investigate and implement different methods based on nonlinear techniques in order to develop indexes able to characterize the frequency spectrum, the nonlinear dynamics and the complexity of the EEG signals recorded in different state of consciousness. Firstly, a new method for removing peak and spike in biological signal based on the signal envelope was successfully designed and applied to simulated and real EEG signals, obtaining performances significantly better than the traditional adaptive filters. Then, several studies were carried out in order to extract and evaluate EEG measures based on nonlinear techniques in different contexts such as the automatic detection of sleepiness and the characterization and prediction of the nociceptive stimuli and the assessment of the sedation level. Four novel indexes were defined by calculating entropy of the Choi-Williams distribution (CWD) with respect to time or frequency, by using the probability mass function at each time instant taken independently or by using the probability mass function of the entire CWD. The values of these indexes tend to decrease, with different proportion, when the behavior of the signals evolved from chaos or randomness to periodicity and present differences when comparing EEG recorded in eyes-open and eyes-closed states and in ictal and non-ictal states. Measures obtained with time-frequency representation, mutual information function and correntropy, were applied to EEG signals for the automatic sleepiness detection in patients suffering sleep disorders. The group of patients with excessive daytime sleepiness presented more power in ¿ band than the group without sleepiness, which presented higher spectral and cross-spectral entropy in the frontal zone in d band. More complexity in the occipital zone was found in the group of patients without sleepiness in ß band, while a stronger nonlinear coupling between the occipital and frontal regions was detected in patients with excessive daytime sleepiness, in ß band. Time-frequency representation and non-linear measures were also used in order to study how adaptation and fatigue affect the event-related brain potentials to stimuli of different modalities. Differences between the responses to infrequent and frequent stimulation in different recording periods were found in series of averaged EEG epochs recorded after thermal, electrical and auditory stimulation. Nonlinear measures calculated on EEG filtered in the traditional frequency bands and in higher frequency bands improved the assessment of the sedation level. These measures were obtained by applying all the developed techniques on signals recorded from patients sedated, in order to predict the responses to pain stimulation such as nail bad compression and endoscopy tube insertion. The proposed measures exhibit better performances than the bispectral index (BIS), a traditional indexes used for hypnosis assessment. In conclusion, nonlinear measures based on time-frequency representation, mutual information functions and correntropy provided additional information that helped to improve the automatic sleepiness detection, the characterization and prediction of the nociceptive responses and thus the assessment of the sedation level.El registro de la señal Electroencefalografíca (EEG) proporciona información sobre los cambios en la actividad cerebral asociados con varios estados de la anestesia, la epilepsia, la atención cerebral, los trastornos del sueño, los trastornos cerebrales, etc. Los EEG son señales complejas cuyas propiedades estadísticas dependen del espacio y del tiempo. Sus características aleatorias y no estacionarias hacen imposible que el EEG se describa de forma precisa con una técnica sencilla requiriendo un análisis y una caracterización que implica técnicas que tengan en cuenta su no estacionariedad. Todo esto aumenta la necesidad de desarrollar nuevas técnicas avanzadas con el fin de mejorar la eficiencia de los métodos utilizados en la práctica clínica que son basados en el análisis de EEG. En esta tesis se han investigado y aplicado diferentes métodos utilizando técnicas no lineales con el fin de desarrollar índices capaces de caracterizar el espectro de frecuencias, la dinámica no lineal y la complejidad de las señales EEG registradas en diferentes estados de conciencia. En primer lugar, se ha desarrollado un nuevo algoritmo basado en la envolvente de la señal para la eliminación de ruido de picos en las señales biológicas. Este algoritmo ha sido aplicado a señales simuladas y reales obteniendo resultados significativamente mejores comparados con los filtros adaptativos tradicionales. Seguidamente, se han llevado a cabo varios estudios con el fin de extraer y evaluar las medidas de EEG basadas en técnicas no lineales en diferentes contextos. Se han definido nuevos índices mediante el cálculo de la entropía de la distribución de Choi-Williams (DCW) con respecto al tiempo o la frecuencia. Se ha observado que los valores de estos índices tienden a disminuir, en diferentes proporciones, cuando el comportamiento de las señales evoluciona de caótico o aleatorio a periódico. Además, se han encontrado valores diferentes de estos índices aplicados a la señal EEG registrada en diferentes estados. Diferentes medidas basadas en la representación tiempo-frecuencia, la función de información mutua y la correntropia se han aplicado al EEG para la detección automática de la somnolencia en pacientes que sufren trastornos del sueño. Se ha observado en la zona frontal que la potencia en la banda θ es mayor en los pacientes con somnolencia diurna excesiva, mientras que la entropía espectral y la entropía espectral cruzada en la banda δ es mayor en los pacientes sin somnolencia. En el grupo sin somnolencia se ha encontrado más complejidad en la zona occipital, mientras que el acoplamiento no lineal entre las regiones occipital y frontal ha resultado más fuerte en pacientes con somnolencia diurna excesiva, en la banda β. La representación tiempo-frecuencia y las medidas no lineales se han utilizado para estudiar cómo la adaptación y la fatiga afectan a los potenciales cerebrales relacionados con estímulos térmicos, eléctricos y auditivos. Analizando el promedio de varias épocas de EEG grabadas después de la estimulación, se han encontrado diferencias entre las respuestas a la estimulación frecuente e infrecuente en diferentes períodos de registro. Todas las técnicas que se han desarrollado, se han aplicado a señales EEG registradas en pacientes sedados, con el fin de predecir las respuestas a la estimulación del dolor. Un conjunto de medidas calculadas en señales EEG filtradas en diferentes bandas de frecuencia ha permitido mejorar la evaluación del nivel de sedación. Las medidas propuestas han presentado un mejor rendimiento comparado con el índice bispectral, un indicador de hipnosis tradicional. En conclusión, las medidas no lineales basadas en la representación tiempofrecuencia, funciones de información mutua y correntropia han proporcionado informaciones adicionales que contribuyeron a mejorar la detección automática de la somnolencia, la caracterización y predicción de las respuestas nociceptivas y por lo tanto la evaluación del nivel de sedación

Network-based brain computer interfaces: principles and applications

Brain-computer interfaces (BCIs) make possible to interact with the external environment by decoding the mental intention of individuals. BCIs can therefore be used to address basic neuroscience questions but also to unlock a variety of applications from exoskeleton control to neurofeedback (NFB) rehabilitation. In general, BCI usability critically depends on the ability to comprehensively characterize brain functioning and correctly identify the user s mental state. To this end, much of the efforts have focused on improving the classification algorithms taking into account localized brain activities as input features. Despite considerable improvement BCI performance is still unstable and, as a matter of fact, current features represent oversimplified descriptors of brain functioning. In the last decade, growing evidence has shown that the brain works as a networked system composed of multiple specialized and spatially distributed areas that dynamically integrate information. While more complex, looking at how remote brain regions functionally interact represents a grounded alternative to better describe brain functioning. Thanks to recent advances in network science, i.e. a modern field that draws on graph theory, statistical mechanics, data mining and inferential modelling, scientists have now powerful means to characterize complex brain networks derived from neuroimaging data. Notably, summary features can be extracted from these networks to quantitatively measure specific organizational properties across a variety of topological scales. In this topical review, we aim to provide the state-of-the-art supporting the development of a network theoretic approach as a promising tool for understanding BCIs and improve usability

Statistical signal processing of nonstationary tensor-valued data

Real-world signals, such as the evolution of three-dimensional vector fields over time, can exhibit highly structured probabilistic interactions across their multiple constitutive dimensions. This calls for analysis tools capable of directly capturing the inherent multi-way couplings present in such data. Yet, current analyses typically employ multivariate matrix models and their associated linear algebras which are agnostic to the global data structure and can only describe local linear pairwise relationships between data entries. To address this issue, this thesis uses the property of linear separability -- a notion intrinsic to multi-dimensional data structures called tensors -- as a linchpin to consider the probabilistic, statistical and spectral separability under one umbrella. This helps to both enhance physical meaning in the analysis and reduce the dimensionality of tensor-valued problems. We first introduce a new identifiable probability distribution which appropriately models the interactions between random tensors, whereby linear relationships are considered between tensor fibres as opposed to between individual entries as in standard matrix analysis. Unlike existing models, the proposed tensor probability distribution formulation is shown to yield a unique maximum likelihood estimator which is demonstrated to be statistically efficient. Both matrices and vectors are lower-order tensors, and this gives us a unique opportunity to consider some matrix signal processing models under the more powerful framework of multilinear tensor algebra. By introducing a model for the joint distribution of multiple random tensors, it is also possible to treat random tensor regression analyses and subspace methods within a unified separability framework. Practical utility of the proposed analysis is demonstrated through case studies over synthetic and real-world tensor-valued data, including the evolution over time of global atmospheric temperatures and international interest rates. Another overarching theme in this thesis is the nonstationarity inherent to real-world signals, which typically consist of both deterministic and stochastic components. This thesis aims to help bridge the gap between formal probabilistic theory of stochastic processes and empirical signal processing methods for deterministic signals by providing a spectral model for a class of nonstationary signals, whereby the deterministic and stochastic time-domain signal properties are designated respectively by the first- and second-order moments of the signal in the frequency domain. By virtue of the assumed probabilistic model, novel tests for nonstationarity detection are devised and demonstrated to be effective in low-SNR environments. The proposed spectral analysis framework, which is intrinsically complex-valued, is facilitated by augmented complex algebra in order to fully capture the joint distribution of the real and imaginary parts of complex random variables, using a compact formulation. Finally, motivated by the need for signal processing algorithms which naturally cater for the nonstationarity inherent to real-world tensors, the above contributions are employed simultaneously to derive a general statistical signal processing framework for nonstationary tensors. This is achieved by introducing a new augmented complex multilinear algebra which allows for a concise description of the multilinear interactions between the real and imaginary parts of complex tensors. These contributions are further supported by new physically meaningful empirical results on the statistical analysis of nonstationary global atmospheric temperatures.Open Acces

Analysis of the propagation of uterine electrical activity applied to predict preterm labor

There are many open questions concerning the functioning of the human uterus. One of these open questions concerns exactly how the uterus operates as an organ to perform the very organized act of contracting in a synchronized fashion to expulse a new human into this world. If we don‟t understand how it works when it is working normally, it is obvious that we will not be as capable of intervening or preventing when, sometimes with tragic consequences, it does not do its job properly and a child is born before it is ready. The aim of our research is to be able to understand what the electrical activity of the uterus can tell us about the risk of premature birth, to understand better how the uterus works and to benefit from these understanding to find tool that can be used for labor detection and prediction of preterm labor. This idea of using the externally detected electrical activity of the uterus (electrohysterogram or EHG) to predict preterm labor is not new and lot of work has already been put into it. The novel approach in this work is not to use the signal collected from one or two isolated places on the expectant mother‟s abdomen but to map the propagation of the signals and to investigate the auto organization of the contractions. We therefore use a matrix of electrodes to give us a much more complete picture of the organization and operation of the uterus as pregnancy reaches its conclusion. Labor is the physiologic process by which a fetus is expelled from the uterus to the outside world and is defined as regular uterine contractions accompanied by cervical effacement and dilatation. In the normal labor, the uterine contractions and cervix dilatation are preceded by biochemical changes in the cervical connective tissue.Il reste beaucoup de questions ouvertes concernant le fonctionnement de l'utérus humain. L'une de ces questions est comment l'utérus fonctionne en tant qu‟organe organisé pour générer une contraction synchrone et expulser un nouvel être humain dans ce monde ? Si nous ne comprenons pas comment l‟utérus fonctionne, quand il fonctionne normalement, il est évident que nous ne serons pas en mesure d'intervenir ou de prévoir quand, avec parfois des conséquences tragiques, il ne fait pas son travail correctement et qu‟un enfant nait avant d‟être prêt ! Le but de notre recherche est de comprendre ce que l'activité électrique de l'utérus peut nous apporter sur la prévention du risque de naissance prématurée, de mieux comprendre comment fonctionne l'utérus et de bénéficier de ces connaissances pour développer un outil qui peut être utilisé pour la détection de l‟accouchement et la prédiction du travail prématuré. Cette idée d'utiliser l'activité électrique détectée à la surface de l‟abdomen (ou électrohystérogramme EHG) pour prédire un accouchement prématuré n'est pas nouvelle et beaucoup de travaux ont déjà été mis en oeuvre. La nouvelle approche dans ce travail n‟est pas d‟utiliser le signal recueilli par un ou deux endroits isolés sur l'abdomen de la future mère, mais de cartographier la propagation des signaux et d‟explorer l'auto organisation des contractions. Nous utilisons donc une matrice d'électrodes pour nous donner une image beaucoup plus complète de l'organisation et du fonctionnement de l'utérus. L‟accouchement est le processus physiologique par lequel le foetus est expulsé de l'utérus vers le monde extérieur. Il est défini comme la survenue de contractions utérines régulières accompagnées de l'effacement du col et de la dilatation cervicale. Dans le travail normal, les contractions de l'utérus et la dilatation du col sont précédées par des changements biochimiques du tissu conjonctif du col utérin

Etude de la propagation de l’activité électrique utérine dans une optique clinique: Application à la détection des menaces d’accouchement prématuré.

Uterine contractions are essentially controlled by two physiological phenomena: cell excitability and propagation of uterine electrical activity probably related to high and low frequencies of uterine electromyogram, called electrohysterogram -EHG-, respectively. All previous studies have been focused on extracting parameters from the high frequency part and did not show a satisfied potential for clinical application. The objective of this thesis is the analysis of the propagation EHG signals of during pregnancy and labor in the view of extracting tool for clinical application. A novelty of our thesis is the multichannel recordings by using 4x4 electrodes matrix posed on the woman abdomen. Monovariate analysis was aimed to investigate the nonlinear characteristics of EHG signals. Bivariate and multivariate analyses have been done to analyze the propagation of the EHG signals by detecting the connectivity between the signals. An increase of the nonlinearity associated by amplitude synchronization and phase desynchronization were detected. Results indicate a highest EHG propagation during labor than pregnancy and an increase of this propagation with the week of gestations. The results show the high potential of propagation’s parameters in clinical point of view such as labor detection and then preterm labor prediction. We proposed novel combination of Blind Source Separation and empirical mode decomposition to denoise monopolar EHG as a possible way to increase the classification rate of pregnancy and labor.Les contractions utérines sont contrôlées par deux phénomènes physiologiques: l'excitabilité cellulaire et la propagation de l'activité électrique utérine probablement liées aux hautes et basses fréquences de l’electrohysterograme (EHG) respectivement. Toutes les études précédentes ont porté sur l'extraction de paramètres de la partie haute fréquence et n'ont pas montré un potentiel satisfait pour l'application clinique. L'objectif de cette thèse est l'analyse de propagation de l'EHG pendant la grossesse et le travail dans la vue de l'extraction des outils pour une application clinique. Une des nouveautés de la thèse est l’enregistrement multicanaux à l'aide d’une matrice d'électrodes 4x4 posée sur l'abdomen de la femme. Analyse monovariés visait à étudier les caractéristiques non linéaires des signaux EHG, analyses bivariées et multivariées ont été effectuées pour analyser la propagation des signaux EHG par la détection de la connectivité entre les signaux. Une augmentation de la non-linéarité associée par une synchronisation en amplitude et de désynchronisation en phase a été détectée. Les résultats indiquent plus de propagation au cours du travail que la grossesse et une augmentation de cette propagation avec les semaines de gestations. Les résultats montrent le potentiel élevé de paramètres de propagation dans le point de vue clinique tel que la détection du travail et de prédiction du travail prématuré. Finalement, nous avons proposé une nouvelle combinaison entre Séparation Aveugles de Sources et la Décomposition en Modes Empiriques pour débruiter les signaux EHG monopolaires comme un moyen possible d'augmenter le taux de classification de signaux grossesse et l'accouchement

Etude de la propagation de l‟activité électrique utérine dans une optique clinique : Application a la détection des menaces d‟accouchement prématuré

Uterine contractions are essentially controlled by two physiological phenomena: cell excitability and propagation of uterine electrical activity probably related to high and low frequencies of uterine electromyogram, called electrohysterogram -EHG-, respectively. All previous studies have been focused on extracting parameters from the high frequency part and did not show a satisfied potential for clinical application. The objective of this thesis is the analysis of the propagation EHG signals of during pregnancy and labor in the view of extracting tool for clinical application. A novelty of our thesis is the multichannel recordings by using 4x4 electrodes matrix posed on the woman abdomen. Monovariate analysis was aimed to investigate the nonlinear characteristics of EHG signals. Bivariate and multivariate analyses have been done to analyze the propagation of the EHG signals by detecting the connectivity between the signals. An increase of the nonlinearity associated by amplitude synchronization and phase desynchronization were detected. Results indicate a highest EHG propagation during labor than pregnancy and an increase of this propagation with the week of gestations. The results show the high potential of propagation‟s parameters in clinical point of view such as labor detection and then preterm labor prediction. We proposed novel combination of Blind Source Separation and empirical mode decomposition to denoise monopolar EHG as a possible way to increase the classification rate of pregnancy and labor.Les contractions utérines sont contrôlées par deux phénomènes physiologiques: l'excitabilité cellulaire et la propagation de l'activité électrique utérine probablement liées aux hautes et basses fréquences de l‟electrohysterograme (EHG) respectivement. Toutes les études précédentes ont porté sur l'extraction de paramètres de la partie haute fréquence et n'ont pas montré un potentiel satisfait pour l'application clinique. L'objectif de cette thèse est l'analyse de propagation de l'EHG pendant la grossesse et le travail dans la vue de l'extraction des outils pour une application clinique. Une des nouveautés de la thèse est l‟enregistrement multicanaux à l'aide d‟une matrice d'électrodes 4x4 posée sur l'abdomen de la femme. Analyse monovariés visait à étudier les caractéristiques non linéaires des signaux EHG, analyses bivariées et multivariées ont été effectuées pour analyser la propagation des signaux EHG par la détection de la connectivité entre les signaux. Une augmentation de la non- linéarité associée par une synchronisation en amplitude et de désynchronisation en phase a été détectée. Les résultats indiquent plus de propagation au cours du travail que la grossesse et une augmentation de cette propagation avec les semaines de gestations. Les résultats montrent le potentiel élevé de paramètres de propagation dans le point de vue clinique tel que la détection du travail et de prédiction du travail prématuré. Finalement, nous avons proposé une nouvelle combinaison entre Séparation Aveugles de Sources et la Décomposition en Modes Empiriques pour débruiter les signaux EHG monopolaires comme un moyen possible d'augmenter le taux de classification de signaux grossesse et l'accouchement