No description available.

Wavelet analysis is a powerful mathematical tool commonly used in signal processing applications, such as image analysis, image compression, image edge detection, and communications systems. Unlike traditional Fourier analysis, wavelet analysis allows for multiple resolutions in the time and frequency domains; it can preserve time information while decomposing a signal spectrum over a range of frequencies. Wavelet analysis is also more suitable for detecting numerous transitory characteristics, such as drift, trends, abrupt changes, and beginnings and ends of events. These characteristics are often the most important and critical part of some non-stationary signals, such as physiological signals. The thesis focuses on a formal analysis of using wavelet transform for noise filtering. The performance of the wavelet analysis is simulated on a variety of patient samples of Arterial Blood Pressure (ABP 14 sets) and Electrocardiography (ECG 14 sets) from the Mayo Clinic at Jacksonville. The performance of the Fourier analysis is also simulated on the same patient samples for comparison purpose. Additive white Gaussian noise (AWGN) is generated and added to the samples for studying the AWGN effect on physiological signals and both analysis methods. The algorithms of finding the optimal level of approximation and calculating the threshold value of filtering are created and different ways of adding the details back to the approximation are studied. Wavelet analysis has the ability to add or remove certain frequency bands with threshold selectivity from the original signal. It can effectively preserve the spikes and humps, which are the information that is intended to be kept, while de-noising physiological signals. The simulation results show that the wavelet analysis has a better performance than Fourier analysis in preserving the transitory information of the physiological signals.

A avaliação perceptivo-auditiva tem papel fundamental no estudo e na avaliação da voz, no entanto, por ser subjetiva está sujeita a imprecisões e variações. Por outro lado, a análise acústica permite a reprodutibilidade de resultados, porém precisa ser aprimorada, pois não analisa com precisão vozes com disfonias mais intensas e com ondas caóticas. Assim, elaborar medidas que proporcionem conhecimentos confiáveis em relação à função vocal resulta de uma necessidade antiga dentro desta linha de pesquisa e atuação clínica. Neste contexto, o uso da inteligência artificial, como as redes neurais artificiais, indica ser uma abordagem promissora. Objetivo: Validar um sistema automático utilizando redes neurais artificiais para a avaliação de vozes rugosas e soprosas. Materiais e métodos: Foram selecionadas 150 vozes, desde neutras até com presença em grau intenso de rugosidade e/ou soprosidade, do banco de dados da Clínica de Fonoaudiologia da Faculdade de Odontologia de Bauru (FOB/USP). Dessas vozes, 23 foram excluídas por não responderem aos critérios de inclusão na amostra, assim utilizaram-se 123 vozes. Procedimentos: avaliação perceptivo-auditiva pela escala visual analógica de 100 mm e pela escala numérica de quatro pontos; extração de características do sinal de voz por meio da Transformada Wavelet Packet e dos parâmetros acústicos: jitter, shimmer, amplitude da derivada e amplitude do pitch; e validação do classificador por meio da parametrização, treino, teste e avaliação das redes neurais artificiais. Resultados: Na avaliação perceptivo-auditiva encontrou-se, por meio do teste Coeficiente de Correlação Intraclasse (CCI), concordâncias inter e intrajuiz excelentes, com p = 0,85 na concordância interjuízes e p variando de 0,87 a 0,93 nas concordâncias intrajuiz. Em relação ao desempenho da rede neural artificial, na discriminação da soprosidade e da rugosidade e dos seus respectivos graus, encontrou-se o melhor desempenho para a soprosidade no subconjunto composto pelo jitter, amplitude do pitch e frequência fundamental, no qual obteve-se taxa de acerto de 74%, concordância excelente com a avaliação perceptivo-auditiva da escala visual analógica (0,80 no CCI) e erro médio de 9 mm. Para a rugosidade, o melhor subconjunto foi composto pela Transformada Wavelet Packet com 1 nível de decomposição, jitter, shimmer, amplitude do pitch e frequência fundamental, no qual obteve-se 73% de acerto, concordância excelente (0,84 no CCI), e erro médio de 10 mm. Conclusão: O uso da inteligência artificial baseado em redes neurais artificiais na identificação, e graduação da rugosidade e da soprosidade, apresentou confiabilidade excelente (CCI > 0,80), com resultados semelhantes a concordância interjuízes. Dessa forma, a rede neural artificial revela-se como uma metodologia promissora de avaliação vocal, tendo sua maior vantagem a objetividade na avaliação. / The auditory-perceptual evaluation is fundamental in the study and analysis of voice. This evaluation, however, is subjective and tends to be imprecise and variable. On the other hand, acoustic analysis allows reproducing results, although these results must be refined since the analysis is not precise enough for intense dysphonia or chaotic waves. Therefore, the will to develop measurements allowing reliable knowledge related to vocal function is not new on this research and clinical actuation field. In this context, the use of artificial intelligence such as neural networks seems to be a promising research field. Objective: to validate an automatic system using artificial neural networks for evaluation of vocal roughness and breathiness. Methods: One hundred fifty (150) voices were selected from from Clínica de Fonoaudiologia da Faculdade de Odontologia de Bauru (FOB/USP) database. These voices presented variation from neutral to intense roughness and/or breathiness. Twenty-three of them were excluded since they did not match inclusion criteria. Thus, 123 voices were used for analysis. The procedures include use of auditoryperception based on two scales: visual analog scale of 100 mm and four points numerical scale. Additionally, the characteristics of voice signals were extracted by Wavelet Packet Transform and by analysis of acoustic parameters: jitter, shimmer, derivative amplitude and pitch amplitude. Validation of classifying system was carried out by parameterization, training, test and evaluation of artificial neural networks. Results: In the auditory-perceptual evaluation, excellent interrater (p=0.85) and intrarater (0.87<p<0.93) agreement were obtained by means of Intraclass Correlation Coefficient (ICC) testing. The artificial neural network performance has achieved the best results for breathiness in the subset composed by parameters jitter, pitch amplitude and fundamental frequency. In this case, the neural network obtained a rate of 74%, demonstrating excellent concordance with auditory-perceptual evaluation for visual analog scale (0.80 ICC) and mean error of 9 mm. As for roughness evaluation, the best subset is composed by Wavelet Packet Transform with 1 resolution level, jitter, shimmer, pitch amplitude and fundamental frequency. For this case, a 73% rate was achieved (0.84 ICC) and mean error of 10 mm was obtained. Conclusion: The use of artificial neural networks for roughness and breathiness evaluation present high reliability (ICC&gt;0.80), with results similar to interrater agreement. Thus, the artificial neural network reveals a promising method for vocal evaluation, bringing objective analysis as a strong advantage.

This thesis studies the impact of shale gas on commodity and stock markets in the U.S. by employing wavelet approach and conducting a time-frequency analysis of dynamic correlations between natural gas and important representatives of commodity markets: crude oil, coal, corn, wheat, and several indices. It covers the period from 2006 to 2015 and is performed on daily data. Our thesis enlarges existing literature on comovement between natural gas with other energy commodities and stocks using wavelet coherence - a methodology which allows analyzing comovement among assets not only from a time series perspective but also over different frequencies. Financialization of natural gas and its involvement in investment portfolios under changing conditions on the U.S. gas market provide space for examination of gas proper correlation estimates in respect to other financial assets. Our results reveal natural gas comovement behaviour with examined commodities during the Financial Crisis. They show gradual decoupling between gas and crude oil prices in time. To the best of our knowledge we are the first to address natural gas using wavelet coherence in connection to agricultural commodities corn and wheat. These commodities together with natural gas are primary sources for bioethanol production being used in...

Dans cette thèse nous proposons de nouveaux modèles paramétriques en traitement du signal et de l'image, fondés sur la décomposition de Wold des processus stochastiques. Les approches de modélisation font appel à l'analyse fonctionnelle et harmonique, l'analyse par ondelettes, ainsi qu'à la théorie des champs stochastiques. Le premier chapitre a un caractère introductif théorique et précise les éléments de base concernant le contexte de la prédiction linéaire des processus stochastiques stationnaires et la décomposition Wold, dans le cas 1-D et multi-D. On montre comment les différentes parties de la décomposition sont obtenues à partir de l'hypothèse de stationnarité, via la représentation du processus comme l'orbite d'un certain opérateur unitaire, l'isomorphisme canonique de Kolmogorov et les conséquences sur la prédiction linéaire du théorème de Szégö et de ses extensions multidimensionnelles. Le deuxième chapitre traite une approche de factorisation spectrale de la densité spectrale de puissance qu'on utilisera pour l'identification des modèles de type Moyenne Ajustée (MA), Autorégressif (AR) et ARMA. On utilise la représentation par le noyau reproduisant de Poisson d'une fonction extérieure pour construire un algorithme d'estimation d'un modèle MA avec une densité spectrale de puissance donnée. Cette méthode d'estimation est présentée dans le cadre de deux applications: - Dans la simulation de canaux sans fil de type Rayleigh (cas 1-D). - Dans le cadre d'une approche de décomposition de Wold des images texturées (cas 2-D). Dans le troisième chapitre nous abordons la représentation et la compression hybride d'images. Nous proposons une approche de compression d'images qui utilise conjointement : - les modèles issus de la décomposition de Wold pour la représentation des régions dites texturées de l'image; - une approche fondée sur les ondelettes pour le codage de la partie "cartoon" (ou non-texturée) de l' image. Dans ce cadre, nous proposons une nouvelle approche pour la décomposition d'une image dans une partie texturée et une partie non-texturée fondée sur la régularité locale. Chaque partie est ensuite codée à l'aide de sa représentation particulière. / In this thesis we propose new parametric models in signal and image processing based on the Wold decomposition of stationary stochastic processes. These models rely upon several theoretical results from functional and harmonic analysis, wavelet analysis and the theory of stochastic fields, The first chapter presents the theoretical background of the linear prediction for stationary processes and of the Wold decomposition theorems in 1-D and n-D. It is shown how the different parts of the decomposition are obtained and represented, by the means of the unitary orbit representation of stationary processes, the Kolmogorov canonical model and Szego-type extensions. The second chapter deals with a spectral factorisation approach of the power spectral density used for the parameter estimation of Moving Avergage (MA), AutoRegressif (AR) and ARMA models. The method uses the Poisson integral representation in Hardy spaces in order to estimate an outer transfer function from its power spectral density. - Simulators for Rayleigh fading channels (1-D). - A scheme for the Wold decomposition for texture images (2-D). In the third chapter we deal with hybrid models for image representation and compression. We propose a compression scheme which jointly uses, on one hand, Wold models for textured regions of the image, and on the other hand a wavelet-based approach for coding the 'cartoon' (or non-textured) part of the image. In this context, we propose a new algorithm for the decomposing images in a textured part and a non-textured part. The separate parts are then coded with the appropriate representation.

Countries world-over have thousands of critical structures and bridges which have been built decades back when strength-based designs were the order of the day. Over the years, magnitude and frequency of loadings on these have increased. Also, these structures have been exposed to environmental degradation during their service life. Hence, structural health monitoring (SHM) has attracted the attention of researchers, world over. Structural health monitoring is recommended both for vulnerable old bridges and structures as well as for new important structures. Structural health monitoring as a principle is derived from condition monitoring of machinery, where the day-to-day recordings of sound and vibration from machinery is compared and sudden changes in their features is reported for inspection and trouble-shooting. With the availability of funds for repair and retrofitting being limited, it has become imperative to rank buildings and bridges that require rehabilitation for prioritization. Visual inspection and expert judgment continues to rule the roost. Non-destructive testing techniques though have come of age and are providing excellent inputs for judgment cannot be carried out indiscriminately. They are best suited for evaluating local damage when restricted areas are investigated in detail. A few modern bridges, particularly long-span bridges have been provided with sophisticated instrumentation for health monitoring. It is necessary to identify local damages existing in normal bridges. The methodology adopted for such identification should be simple, both in terms of investigations involved and the instrumentation. Researchers have proposed various methodologies including damage identification from mode shapes, wavelet-based formulations and optimization-based damage identification and instrumentation schemes and so on. These are technically involved but may be difficult to be applied for all critical bridges, where the sheer volume of number of bridges to be investigated is enormous. Ideally, structural health monitoring has to be carried out in two stages: (a) Stage-1: Remote monitoring of global damage indicators and inference of the health of the structure. Instrumentation for this stage should be less, simple, but at critical locations to capture the global damage in a reasonable sense. (b) Stage -2: If global indicators show deviation beyond a specified threshold, then a detailed and localized instrumentation and monitoring, with controlled application of static and dynamic loads is to be carried out to infer the health of the structure and take a decision on the repair and retrofit strategies. The thesis proposes the first stage structural health monitoring methodology using natural frequencies and static deflections as damage indicators. The idea is that the stage-1 monitoring has to be done for a large number of bridges and vulnerable structures in a remote and wire-less way and a centralized control and processing unit should be able to number-crunch the in-coming data automatically and the features extracted from the data should help in determining whether any particular bridge warrants second stage detailed investigation. Hence, simple and robust strategies are required for estimating the health of the structure using some of the globally available response data. Identification methodology developed in this thesis is applicable to distributed smeared damage, which is typical of reinforced concrete structures. Simplified expressions and methodologies are proposed in the thesis and numerically and experimentally validated towards damage estimation of typical structures and elements from measured natural frequencies and static deflections. The first-order perturbation equation for a dynamical system is used to derive the relevant expressions for damage identification. The sensitivity of Eigen-value-cumvector pair to damage, modeled as reduction in flexural rigidity (EI for beams, AE for axial rods and Et 12(1 2 )3− μ for plates) is derived. The forward equation relating the changes in EI to changes in frequencies is derived for typical structural elements like simply-supported beams, plates and axial rods (along with position and extent of damage as the other controlling parameters). A distributed damage is uniquely defined with its position, extent and magnitude of EI reduction. A methodology is proposed for the inverse problem, making use of the linear relationship between the reductions in EI (in a smeared sense) to Eigen-values, such that multiple damages could be estimated using changes in natural frequencies. The methodology is applied to beams, plates and axial rods. The performance of this inverse methodology under influence of measurement errors is investigated for typical error profiles. For a discrete three dimensional structure, computationally derived sensitivity matrix is used to solve the damages in each floor levels, simulating the post-earthquake damage scenario. An artificial neural network (ANN) based Radial basis function network (RBFN) is also used to solve the multivariate interpolation problem, with appropriate training sets involving a number of pairs of damage and Eigen-value-change vectors. The acclaimed Cawley-Adams criteria (1979) states that, “the ratio of changes in natural frequencies between two modes is independent of the damage magnitude” and is governed only by the position (or location) and extent of damage. This criterion is applied to a multiple damage problem and contours with equal frequency change ratios, termed as Iso_Eigen_value_change contours are developed. Intersection of these contours for different pairs of frequencies shows the position and extent of damage. Experimental and analytical verification of damage identification methodology using Cawley-Adams criteria is successfully demonstrated. Sensitivity expressions relating the damages to changes in static deflections are derived and numerically and experimentally proved. It is seen that this process of damage identification from static deflections is prone to more errors if not cautiously exercised. Engineering and physics based intuition is adopted in setting the guidelines for efficient damage detection using static deflections. In lines of Cawley-Adams criteria for frequencies, an invariant factor based on static deflections measured at pairs of symmetrical points on a simply supported beam is developed and established. The power of the factor is such that it is governed only by the position of damage and invariant with reference to extent and magnitude of damage. Such a revelation is one step ahead of Caddemi and Morassi’s (2007) recent paper, dealing with static deflection based damage identification for concentrated damage. The invariant factor makes it an ideal candidate for base-line-free measurement, if the quality and resolution of instrumentation is good. A moving damage problem is innovatively introduced in the experiment. An attempt is made to examine wave-propagation techniques for damage identification and a guideline for modeling wave propagation as a transient dynamic problem is done. The reflected-wave response velocity (peak particle velocity) as a ratio of incident wave response is proposed as a damage indicator for an axial rod (representing an end-supported pile foundation). Suitable modifications are incorporated in the classical expressions to correct for damping and partial-enveloping of advancing wave in the damage zone. The experimental results on axial dynamic response of free-free beams suggest that vibration frequency based damage identification is a viable complementary tool to wave propagation. Wavelet-multi-resolution analysis as a feature extraction tool for damage identification is also investigated and structural slope (rotation) and curvatures are found to be the better indicators of damage coupled with wavelet analysis. An adaptive excitation scheme for maximizing the curvature at any arbitrary point of interest is also proposed. However more work is to be done to establish the efficiency of wavelets on experimentally derived parameters, where large noise-ingression may affect the analysis. The application of time-period based damage identification methodology for post-seismic damage estimation is investigated. Seismic damage is postulated by an index based on its plastic displacement excursion and the cumulative energy dissipated. Damage index is a convenient tool for decision making on immediate-occupancy, life-safety after repair and demolition of the structure. Damage sensitive soft storey structure and a weak story structure are used in the non-linear dynamic analysis and the DiPasquale-Cakmak (1987) damage index is calibrated with Park-Ang (1985) damage index. The exponent of the time-period ratio of DiPasquale-Cakmak model is modified to have consistency of damage index with Park-Ang (1985) model.

This dissertation explores the role of brainstem structures in the development and expression of generalized tonic-clonic seizures. The functional consequences of brainstem seizures are investigated using the kindling paradigm in order to understand the behavioral and cognitive effects of generalized seizures. <BR><BR> I begin by investigating the general characteristics of brainstem kindling. The first experiment demonstrates that certain brainstem sites are indeed susceptible to kindling and begins to delineate the features that distinguish brainstem seizures from those evoked at other brain regions. Further investigation of the EEG signal features using wavelet analysis reveals that changes in the spectral properties of the electrographic activity during kindling include significant changes to high-frequency activity and organized low-frequency activity. I also identify transitions that include frequency sweeps and abrupt seizure terminations. The changing spectral features are shown to be critically associated with the evolution of the kindled seizures and may have important functional consequences. The surprising responsiveness of some brainstem structures to kindling forces us to reconsider the overall role of these structures in epileptogenesis as well as in the healthy dynamical functioning of the brain. <BR><BR> In order to study the functional consequences, a series of experiments examines the changes in behavior, cognition and affect that follow these brainstem seizures. Although the results show no effects on spatial learning or memory, there are significant and complex effects on anxiety- and depression-like behavior that appear to be related to motivation. In order to further study the cognitive effects, a second set of behavioral experiments considers how context (i.e., the environment) interacts with the behavioral changes. The results indicate that changes in affect may only be apparent when choice between seizure-related and seizure-free contexts is given, suggesting that the environment and choice can play key roles in the behavioral consequences of seizures. This thesis also includes an appendix that applies synchrotron imaging to investigate the anatomical consequences of electrode implantation in kindling and shows that significantly increased iron depositions occur even with purportedly biocompatible electrodes widely used in research and clinical settings. <BR><BR> Examination of the role of brainstem structures in generalized seizures in this dissertation offers new perspectives and insights to epileptogenesis and the behavioral effects of epilepsy. The changes in EEG features, behavior, affect and motivation observed after brainstem seizures and kindling may have important clinical implications. For example, the results suggest a need to reexamine the concept of psychogenic seizures, a potential connection to Sudden Unexplained Death in Epilepsy (SUDEP), and the contribution of environmental factors. It is hoped that these findings will help elucidate the complex issues involved in understanding and improving the quality of life for people with epilepsy.

This dissertation explores the role of brainstem structures in the development and expression of generalized tonic-clonic seizures. The functional consequences of brainstem seizures are investigated using the kindling paradigm in order to understand the behavioral and cognitive effects of generalized seizures. <BR><BR> I begin by investigating the general characteristics of brainstem kindling. The first experiment demonstrates that certain brainstem sites are indeed susceptible to kindling and begins to delineate the features that distinguish brainstem seizures from those evoked at other brain regions. Further investigation of the EEG signal features using wavelet analysis reveals that changes in the spectral properties of the electrographic activity during kindling include significant changes to high-frequency activity and organized low-frequency activity. I also identify transitions that include frequency sweeps and abrupt seizure terminations. The changing spectral features are shown to be critically associated with the evolution of the kindled seizures and may have important functional consequences. The surprising responsiveness of some brainstem structures to kindling forces us to reconsider the overall role of these structures in epileptogenesis as well as in the healthy dynamical functioning of the brain. <BR><BR> In order to study the functional consequences, a series of experiments examines the changes in behavior, cognition and affect that follow these brainstem seizures. Although the results show no effects on spatial learning or memory, there are significant and complex effects on anxiety- and depression-like behavior that appear to be related to motivation. In order to further study the cognitive effects, a second set of behavioral experiments considers how context (i.e., the environment) interacts with the behavioral changes. The results indicate that changes in affect may only be apparent when choice between seizure-related and seizure-free contexts is given, suggesting that the environment and choice can play key roles in the behavioral consequences of seizures. This thesis also includes an appendix that applies synchrotron imaging to investigate the anatomical consequences of electrode implantation in kindling and shows that significantly increased iron depositions occur even with purportedly biocompatible electrodes widely used in research and clinical settings. <BR><BR> Examination of the role of brainstem structures in generalized seizures in this dissertation offers new perspectives and insights to epileptogenesis and the behavioral effects of epilepsy. The changes in EEG features, behavior, affect and motivation observed after brainstem seizures and kindling may have important clinical implications. For example, the results suggest a need to reexamine the concept of psychogenic seizures, a potential connection to Sudden Unexplained Death in Epilepsy (SUDEP), and the contribution of environmental factors. It is hoped that these findings will help elucidate the complex issues involved in understanding and improving the quality of life for people with epilepsy.

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / One of the main causes of control loop performance degradation are the oscillations, which have a negative effect on the performance of these loops and may force the plant to operate at less than optimal conditions. One of the fundamental steps for the evaluation of industrial control loop performance is the detection and diagnosis of these oscillations, also motivated by the growing emphasis on security and earnings capacity of the installations. This paper uses wavelet analysis combined with other signal analysis techniques such as the autocorrelation function and the Granger Causality, to make the complete diagnosis of oscillations in control loops processes. Numerical test simulations are presented to demonstrate the effectiveness of the proposed method. First, the techniques are used for the diagnosis of a simple control loop in the format of internal model control. After, the methods are applied in a catalytic cracking unit operating under model predictive control (MPC). The results show the potentiality of the proposed methodology to real applications. / Uma das principais causas da degradação do desempenho em malhas de controle são as oscilações, as quais têm um efeito negativo sobre o desempenho dessas malhas e pode forçar a planta a operar em condições abaixo do ideal. Um dos passos fundamentais para a avaliação do desempenho de malhas de controle industriais são a detecção e diagnóstico dessas oscilações, motivados também pela crescente ênfase na segurança e capacidade de lucro das instalações. O presente trabalho usa a análise de Wavelet combinada com outras técnicas de análise de sinais, tais como a Função de Autocorrelação e a Causalidade de Granger, para fazer o diagnóstico completo de oscilações em malhas de controle de processos. Testes de simulações numéricas são apresentados para demonstrar a eficácia da metodologia proposta. Primeiramente, as técnicas são utilizadas para o diagnóstico de uma malha de controle simples no formato de controle por modelo interno. Posteriormente, os métodos são aplicados numa unidade de craqueamento catalítico operando sob controle preditivo (MPC). Os resultados obtidos mostram a potencialidade da metodologia proposta para aplicações reais.

L’objectif de ce travail de thèse a été d’étudier la réactivité autonomique cardiaque à des stimulations aversives au cours du sommeil et les phénomènes pouvant la moduler. Pour ce faire, nous avons utilisé une technique d’analyse temps-fréquence de la variabilité du signal RR (inverse de la fréquence cardiaque), basée sur des transformées en ondelettes de ce signal, lors de stimuli nociceptifs chez des sujets sains et en réponse à des évènements respiratoires obstructifs chez des patients apnéiques. Notre première étude suggère que la réactivité autonomique cardiaque en réaction à des stimuli nociceptifs est dépendante d’une activation sympathique qui est préservée dans tous les stades du sommeil. De plus, bien que cette réactivité cardiaque soit présente même lorsque la stimulation ne donne pas lieu à une réaction d’éveil, elle est plus importante si la stimulation est suivie d’une réaction d’éveil cortical, et ceci quelque soit le stade de sommeil. La deuxième étude, réalisée chez des patients apnéiques, montre que la réactivité autonomique en réponse aux évènements respiratoires obstructifs est dépendante essentiellement de la réactivité sympathique qui est modulée par le processus de réaction d’éveil plutôt que par les stades de sommeil ou par la sévérité des évènements respiratoires. Enfin, la troisième étude révèle qu’un niveau d’activité sympathique cardiaque élevé avant les stimulations nociceptives ou pendant les évènements respiratoires obstructifs peut favoriser l’apparition de réactions d’éveil. En conclusion, nos résultats sont en faveur du maintien de la réactivité sympathique cardiaque à des évènements aversifs au cours du sommeil et ceci dans tous les stades de sommeil. Cette réactivité sympathique est essentiellement modulée par le processus qui mène à la réaction d’éveil cortical, processus auquel semble participer un niveau sympathique basal élevé / The aim of this work was to study cardiac autonomic reactivity to aversive stimulations during sleep and the phenomena that could modulate this reactivity. We used time-frequency method of RR intervals variability (or heart rate variability), based on wavelet transform during nociceptive stimulations in healthy subjects and obstructive respiratory events in apnoeic patients. Our first study showed that the cardiac autonomic reactivity to nociceptive stimulations is sympathetically-driven cardiac activation in reaction, and preserved during all sleep stages. Furthermore, albeit cardiac reactivity persisted even in the absence of arousals, it was higher when a cortical arousal followed the noxious stimulus whatever the sleep stages. Our second work showed, in apnoeic patients, that cardiac autonomic reactivity in response to obstructive respiratory events was also dependent on sympathetic reactivity, mainly modulated by arousal process rather than sleep stages or severity of respiratory events. At last, our third work showed that cardiac sympathetic level before nociceptive stimuli or during respiratory events could favour cortical arousal. In conclusion, cardiac sympathetic reactivity in response to aversive stimuli during sleep is preserved during all sleep stages. This sympathetic reactivity is modulated by arousal process rather than sleep stages or severity of respiratory events. Cardiac sympathetic activity during sleep could take part in arousal process, by favouring cortical arousal