Les activités liées au développement d’objets connectés munis d’intelligence embarquée ont connu un essor considérable ces dernières années, en particulier pour les applications médicales. Dans ce contexte, une course effrénée s’est engagée entre les pionniers de l’IoT afin d’offrir des produits toujours plus performants. Smartphones, bracelets ou textile intelligent, tous intègrent un panel de capteurs multifonctionnels. Il est envisageable alors d’implémenter dans ces produits des solutions permettant de mesurer les signaux physiologiques en continu. En effet, ces signaux émis par le corps humain représentent une source riche d’informations que peut exploiter le corps médical pour le diagnostic ou la prévention d’une pathologie. Les maladies cardiovasculaires, étant la première cause de mortalité dans le monde, le diagnostic précoce de ces maladies est important et des solutions peuvent être apportées par les nouvelles technologies. Ainsi, les pathologies liées aux troubles du rythme cardiaque peuvent être décelées par une analyse inter-battements cardiaques en continu. En effet, l’analyse de la variabilité de la fréquence cardiaque représente un indicateur pertinent sur le fonctionnement cardiovasculaire. Or, cette pertinence dépend en grande partie de l’intelligibilité de l’information mesurée. La pertinence des algorithmes utilisés n’ayant pas été étudiée dans la littérature en fonction du niveau de bruit, la détection des battements cardiaques constitue donc un défi de taille lorsque celle-ci est effectuée en environnement non-maitrisé à partir de dispositifs embarqués et ce travail de thèse a essayé d’apporter des réponses concrètes à cette problématique. / Activities related to the development of connected objects with on-board intelligence have undergone considerable growth in recent years, especially for medical applications. In this context, a frantic race has begun between the pioneers of the IoT in order to offer ever moreefficient and intelligent products. Smartphones, wristbands or smart textiles all incorporate a panel of multifunctional sensors. According to the predictions of the Allied Market Research, the annual growth rate for sensors will reach 11.3% by 2022. The vital signs emitted by thehuman body represent a rich source of information that can be exploited by the medical corps for the diagnosis or prevention of a pathology of interest. Cardiovascular disease, being the second cause of death in the world, reminds us of the importance of a rigorous diagnosis.Pathologies related to heart rhythm disorders are generally detected by cardiac cross-heartbeat analysis. The detection of these beats is one of the most important axes of research in the field of electrocardiogram treatment. Indeed, the analysis of heart rate variability is a relevantindicator of cardiovascular functioning. This relevance depends, in large part, on the intelligibility of the measured information and the signal-to-noise ratio of the parameter of interest. The detection of heartbeats is a daunting challenge when it is carried out from onboarddevices especially in noisy environments.

ECG is used clinically to detect a multitude of medical conditions, such as heart-problems like arrhythmias and heart failure, and to give a good general image of the function of the heart with a quick and harmless exam. In many clinical cases, normal ECG measurements cannot be taken, such as with fetuses where ECG signals from the mother’s own body hinder the measurement. This paper examines using machine learning algorithms to be able to simulate ECG graphs from ultrasound data alone. These algorithms are trained on ultrasound and ECG data acquired from the same patient simultaneously. The data used in the training of the algorithms is taken from samples acquired from 100 adult patients. The results found using this method to simulate an ECG indicate good possibilities for future usefulness, where machine learning to acquire simulated ECG can help facilitate clinicians in evaluating fetal heart function, as well as in other cases where ECG cannot be measured normally. / EKG används kliniskt för att upptäcka en mängd olika åkommor, så som hjärtsvikt och arytmier, men också för att ge en generell bild av hjärtfunktionen med en snabb och harmlös undersökning. I många kliniska fall kan dock inte normal EKG mätning ske, så som för foster då EKG signaler från moderns egna kropp hindrar EKG-mätningen. I detta papper undersöks användandet av maskininlärningsalgoritmer för att kunna simulera EKG grafer från enbart ultraljuds data. Dessa algoritmer är tränade på ultraljud och EKG data som simultant fåtts från samma undersökning av en patient. I detta papper har ultraljudsdatan som använts kommit från 100 mätningar från olika vuxna patienter. Resultaten funna från undersökningen av EKG simulerings metoden indikerar goda möjligheter för framtida användbarhet, då maskininlärningsalgoritmer för att simulera EKG kan underlätta när kliniker ska utvärdera hjärtfunktionen hos foster, eller i andra fall då EKG inte kan mätas normalt.

Le réseau sans fil sur le corps humain ou « wireless body area network (WBAN) » est une nouvelle technologie de réseau sans fil dédié à la surveillance des paramètres physiologiques d’une personne. Le réseau est composé de dispositifs électroniques miniatures, appelés nœuds, disposés aux alentours ou à l’intérieur du corps humain. Chaque nœud est doté d’un ou plusieurs capteurs mesurant les paramètres physiologiques de la personne, comme l’électrocardiogramme ou bien la température du corps, et les caractéristiques de l’environnement qui l’entoure. Ces nœuds sont surtout soumis à une contrainte énergétique importante puisque la miniaturisation a réduit les dimensions de leurs batteries. Puisque les nœuds consomment la majorité de l’énergie pour transmettre les données, une solution pour diminuer leur consommation consisterait à compresser les données avant la transmission. Les méthodes classiques de compression ne sont pas adaptées pour le WBAN particulièrement à cause de la puissance de calcul requise et la consommation qui en résulterait. Dans cette thèse, pour contourner ces problèmes, nous utilisons une méthode à base de l’acquisition comprimée pour compresser et reconstruire les données provenant des nœuds. Nous proposons un encodeur simple et facile à mettre en œuvre pour compresser les signaux. Nous présentons aussi un algorithme permettant de réduire la complexité de la phase de reconstruction des signaux. Un travail collaboratif avec l’entreprise TEA (Technologie Ergonomie Appliquées) nous a permis de valider expérimentalement une version numérique de l’encodeur et l’algorithme de reconstruction. Nous avons aussi développé et validé une version analogique de l’encodeur en utilisant des composants standards. / A wireless body area network (WBAN) is a new class of wireless networks dedicated to monitor human physiological parameters. It consists of small electronic devices, also called nodes, attached to or implanted in the human body. Each node comprises one or many sensors which measure physiological signals, such as electrocardiogram or body heat, and the characteristics of the surrounding environment. These nodes are mainly subject to a significant energy constraint due to the fact that the miniaturization has reduced the size of their batteries. A solution to minimize the energy consumption would be to compress the sensed data before wirelessly transmitting them. Indeed, research has shown that most of the available energy are consumed by the wireless transmitter. Conventional compression methods are not suitable for WBANs because they involve a high computational power and increase the energy consumption. To overcome these limitations, we use compressed sensing (CS) to compress and recover the sensed data. We propose a simple and efficient encoder to compress the data. We also introduce a new algorithm to reduce the complexity of the recovery process. A partnership with TEA (Technologie Ergonomie Appliquées) company allowed us to experimentally evaluate the performance of the proposed method during which a numeric version of the encoder has been used. We also developed and validated an analog version of the encoder.

Submitted by Maria Aparecida (cidazen@gmail.com) on 2017-04-17T11:57:59Z No. of bitstreams: 1 Alfredo Costa Oliveira Júnior.pdf: 789446 bytes, checksum: c5c9858983f5e6384177bda8d1ae2a0a (MD5) / Made available in DSpace on 2017-04-17T11:57:59Z (GMT). No. of bitstreams: 1 Alfredo Costa Oliveira Júnior.pdf: 789446 bytes, checksum: c5c9858983f5e6384177bda8d1ae2a0a (MD5) Previous issue date: 2017-02-16 / Atrial fibrilation(AF) is one of the most common cardiac arrhythmias worldwide. Thus, there are ample efforts to implement AF diagnosis systems. The main noninvasive way to assess cardiac health is through electrocardiogram (ECG) signal analysis, which represents the electrical activity of the cardiac muscle, and has characteristic temporal markings: P, Q, R, S and T waves. Some authors use filtering techniques, statistical analysis and even neural networks for detecting AF based on the RR interval, that is given by the temporal difference between the peaks of the R wave. However, analises of the RR interval allows for evaluating changes occurring only in the R wave of the ECG signal, not allowing to assess, for example, variations in the P wave provoked by the AF. In face of that, we propose characterize the ECG signal amplitude aiming at classifying both healthy and AF patients. The ECG signal was analyzed in the proposed methodology through the following statistics: variance, asymmetry, and kurtosis. Herein, we use the MIT-BIH Atrial Fibrillation and MIT-BIH Normal Sinus Rhythm database signals to evaluate AF and normal heartbeat intervals. Our study shown that kurtosis outperfomed variance and asymmetry with respect to sensibility (Se = 100%), specificity (Sp = 88.33%) and accuracy (Ac = 91.33%). The results were expected since kurtosis is a non-Gaussian measure and the ECG signal has sparse distribution. The proposed methodology also requires a lower number of pre-processing stages, and its simplicity allows for implementations in imbedded systems supporting the clinical diagnosis. / A Fibrilação atrial (FA) é uma das arritmias cardíacas mais comuns em todo o mundo. Por isso, amplos são os esforços para implementar sistemas que apoiem o diagnóstico de FA. A principal forma não invasiva de avaliar a saúde cardíaca, é através da análise do sinal de eletrocardiograma (ECG), o qual representa a atividade elétrica do músculo cardíaco, e possui marcações temporais características: as ondas P, Q, R, S e T. Alguns autores utilizaram técnicas de filtragem, análise estatística e até redes neurais para detectar FA com base no intervalo RR, que é dado pela diferença temporal entre os picos da onda R. Entretanto, a análise do intervalo RR permite avaliar apenas as variações que ocorrem na onda R do sinal de ECG, não permitindo avaliar, por exemplo, as alterações na onda P, provocadas pela FA. Diante disso, propõe-se caracterizar a amplitude do sinal de ECG, a fim de classificar pacientes com FA e saudáveis. Na metodologia proposta, o sinal de ECG, foi analisado por meio das seguintes estatísticas: variância, assimetria e curtose. Para avaliar o classificador proposto, usou-se sinais obtidos das bases de dados MIT-BIH Atrial Fibrillation e MIT-BIH Normal Sinus Rhythm referentes aos pacientes com FA e com ritmo cardíaco normal, respectivamente. Dentre as estatísticas analidadas, a curtose foi a que apresentou resultados superiores em termos de sensibilidade (Se = 100%), especificidade (Sp = 88, 33%) e acurácia (Ac = 91, 33%). Esses resultados são de se esperar pelo fato de que a curtose é uma medida de não-gaussianidade e que o sinal de ECG possui distribuição esparsa. A metodologia proposta também requer um número menor de etapas de pré-processamento, e sua simplicidade permite implementações em sistemas embarcados que apoiarão o diagnóstico clínico.

Em Biomedicina, a detecção e a quanticação de anormalidades presentes num sinal são desejáveis. Uma estratégia de codicação baseada em extração de características, tais como picos ou frequências, pode não capturar todas as irregularidades. Assim, uma representação baseada em funções de base denidas com conhecimento a priori do sinal pode ser mais precisa para aplicações biomédicas. A escolha das funções base depende da natureza siológica do sinal e de suas peculiaridades. Sinais de eletrocardiograma (ECG) e eletroencefalograma (EEG) exibem características bem denidas. ECG, por exemplo, é um sinal elétrico composto de uma forma de onda especíca (P, QRS e T). Se as características de um sinal a ser sintetizado são bem compreendidas, é possível derivar uma assinatura para o sinal. Uma codicação apropriada permite a extração de parâmetros relevantes para sua análise, tais como anormalidades num ciclo cardíaco representadas por uma alteração no sinal de ECG, ou então uma excitação das ondas cerebrais representada por uma modicação no sinal de EEG. O objetivo deste projeto é introduzir uma nova técnica de codicação de sinais, que representa um sinal pela soma de funções sigmoides para aproximar iterativamente o sinal medido, com foco em aplicações biomédicas. Funções sigmoides tendem a reproduzir bem as grandes variações presentes em sinais biomédicos, daí a escolha de usá-las na codicação deste tipo de sinal. Serão explorados o nível de compressão dos dados, bem como a taxa de convergência. A técnica desenvolvida será comparada com técnicas convencionais de codicação e sua robustez será avaliada. Uma estratégia de codicação ótima pode trazer benefícios não só para a compressão, mas também na criação de assinaturas de sinais representando tanto condições siológicas normais como patológicas. / In Biomedicine, detection and quantication of abnormalities present in a signal are desired. An encoding strategy based on feature extraction, such as peaks or frequencies, may not capture all irregularities. Thus, a function-based representation, constructed using a priori knowledge of signal characteristics, may be more accurate for biomedical applications. The choice of the basis function depends on the physiological nature of the signal and its specic features. Electrocardiogram (ECG) and electroencephalogram (EEG) signals exhibit well-dened characteristics. ECG, for instance, is an electrical signal composed of specic waveform (P, QRS, and T). If the characteristics of a signal to be synthesized are well understood, its possible to derive a signal signature. An appropriate encoding allows the extraction of parameters relevant for its analysis, such as, abnormalities in a cardiac cycle represented by an alteration in the ECG signal, or an excitation of the brain waves represented by a modication of the EEG. The objective of this project is to introduce a novel signal encoding technique that represents a signal by a sum of sigmoidal functions to iteratively approximate the measured signal, targeted at biomedical applications. Sigmoidal functions tend to reproduce well large variations in biomedical signals, hence their use for coding this type of signal. We explore the data compression level as well as the convergence rate. We also compare it to conventional encoding techniques and assess the robustness of this model. An optimal encoding strategy may bring not only benets in compression, but also in the creation of signatures for signals representing both physiological and pathological conditions.

Made available in DSpace on 2016-08-15T20:31:29Z (GMT). No. of bitstreams: 1 FernandaAS_DISSERT.pdf: 1414995 bytes, checksum: e7e006bb87888d5b4777f2f259d7afd8 (MD5) Previous issue date: 2015-09-29 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Ovarian xenografting is an auxiliary reproductive technique that allows the conservation of germplasm of high value livestock or endangered species. The use of exogenous gonadotropins assists in developing these xenografted tissues and obtaining viable follicles for in vitro embryo production (IVEP), however this use has not been reported in xenograftings of cats ovaries with C57BL/6 SCID female mice as recipients. Thus, the aim of this study was to evaluate the response of xenografting of domestic cat ovaries to equine chorionic gonadotropin (eCG) when grafted into C57BL/6 SCID female mice. Therefore, domestic cats ovarian cortex fragments were grafted under the kidney capsule of fifteen C57BL/6 SCID mice after bilateral ovariectomy. At the end of 45 days, the female mice were divided into two groups and those who did not receive hormone induction (eCG ) were euthanized at the time of induction. Females who received hormonal induction (eCG +) were euthanized after 48 hours. All collected tissues were taken for histologic processing. The proportions between the different ovarian follicles were compared by the chi-square test. The morphometric analysis of the follicles were compared between the experimental groups by the Tukey test (primordial follicles, primary and secondary) and Kruskal-Wallis (antral follicles). Macroscopically, it was possible to observe a low number (16%) of antral follicles with more than 1mm in transplants treated with eCG. In the microscopic analysis, follicles from all categories were observed in transplants and all had normal morphology and morphometry for the studied species (Felis catus), being however observed larger primordial and primary follicles in those eCG + transplants. There was a decrease in primordial follicles percentages and an increase in subsequent categories, mainly in antral follicles of eCG + group, and this condition is proposed here characterized as Follicular Right Shift (FRS). Luteinized follicles were also observed in transplants treated with eCG. Thus, it is concluded that the treatment with eCG is effective when it comes to follicular development, but it did not show a good superovulatory response / Xenotransplante ovariano é uma técnica reprodutiva auxiliar que permite a conservação do germoplasma de espécies de alto valor zootécnico ou em perigo de extinção. O uso de gonadotrofinas exógenas auxilia no desenvolvimento desses tecidos xenotransplantados e na obtenção de folículos viáveis para produção in vitro de embriões (PIVE), entretanto esse uso não foi relatado em xenotransplante de ovários de gatas com fêmeas C57BL/6 SCID como receptora. Dessa forma, o objetivo desse trabalho foi avaliar a resposta do xenotransplante ovariano de gata doméstica à gonadotrofina coriônica equina (eCG) quando transplantados em fêmeas C57BL/6 SCID. Para tanto, fragmentos de córtex ovariano de gatas domésticas foram transplantados sob a cápsula renal de quinze camundongas C57BL/6 SCID após ovariectomia bilateral. Ao final de 45 dias, as fêmeas foram divididas em dois grupos e aquelas que não receberam indução hormonal (eCG ) foram eutanasiadas no momento da indução. As fêmeas que receberam indução hormonal (eCG +) foram eutanasiadas 48h após. Todos os tecidos colhidos foram levados para processamento histológico. As proporções entre os diferentes folículos ovarianos foram comparadas pelo teste de qui-quadrado. A análise morfométrica dos folículos foi comparada entre os grupos experimentais pelo teste de Tukey (folículos primordial, primário e secundário) e Kruskal-Wallis (folículo antral). Macroscopicamente foi possível observar um baixo número (16%) de folículos antrais com mais de 1mm nos transplantes tratados com eCG. Na análise microscópica, folículos de todas as categorias foram observados nos transplantes e todos apresentaram morfologia e morfometria normais para a espécie estudada (Felis catus), sendo, porém observado folículos primordiais e primários maiores naqueles transplantes eCG +. Houve uma redução nas porcentagens de folículos primordiais e aumento nas categorias subsequentes, principalmente nas de folículos antrais do grupo eCG +, sendo essa condição caracterizada como Follicular Right Shift (FRS). Folículos luteinizados também foram observados nos transplantes tratados com eCG. Dessa maneira, conclui-se que o tratamento com eCG foi eficaz em se tratando de desenvolvimento folicular, mas não apresentou boa resposta superovulatória

First part of this thesis presents the design, validation, and implementation of an Orthogonal Frequency Division Multiplexing (OFDM) transmitter and receiver on a Cyclone II FPGA chip using DSP builder and Quartus II high level design tools. The resources in terms of logical elements (LE) including combinational functions and logic registers allocated by the model have been investigated and addressed. The result shows that implementing the basic OFDM transceiver allocates about 14% (equivalent to 6% at transmitter and 8% at receiver) of the available LE resources on an Altera Cyclone II EP2C35F672C6 FPGA chip, largely taken up by the FFT, IFFT and soft decision encoder. Secondly, a new wavelet-based OFDM system based on FDPP-DA based channel estimation is proposed as a reliable ECG Patient Monitoring System, a Personal Wireless telemedicine application. The system performance for different wavelet mothers has been investigated. The effects of AWGN and multipath Rayleigh fading channels have also been studied in the analysis. The performances of FDPP-DA and HDPP-DA-based channel estimations are compared based on both DFT-based OFDM and wavelet-based OFDM systems. The system model was studied using MATLAB software in which the average BER was addressed for randomized data. The main error differences that reflect the quality of the received ECG signals between the reconstructed and original ECG signals are established. Finally a DA-based architecture for 1-D iDWT/DWT based on an OFDM model is implemented for an ECG-PMS wireless telemedicine application. In the portable wireless body transmitter unit at the patient site, a fully Serial-DA-based scheme for iDWT is realized to support higher hardware utilization and lower power consumption; whereas a fully Parallel-DA-based scheme for DWT is applied at the base unit of the hospital site to support a higher throughput. It should be noted that the behavioural level of HDL models of the proposed system was developed and implemented to confirm its correctness in simulation. Then, after the simulation process the design models were synthesised and implemented for the target FPGA to confirm their validation.

No description available.

En dépit du développement rapide de la médecine, les maladies cardiovasculaires restent la première cause de mortalité dans le monde. En France, chaque année, plus de 50 000 personnes meurent subitement en raison d'arythmies cardiaques. L'identification des patients à risque élevé de décès soudain est toujours un défi. Pour détecter les arythmies cardiaques, actuellement Holter est généralement utilisé pour enregistrer les signaux électrocardiogramme (ECG) à 1~3 dérivations pendant 24h à 72h. Cependant l'utilisation de Holter est limitée parmi la population en raison de son encombrement (pas convivial) et de son coût. Un Holter mono puce portable nommé SoC-Holter qui permet d'enregistrer 1 à 4 dérivations est introduit. Le déploiement d'un réseau de capteurs sans fil exige que chaque SoC-Holter soit peu encombrant et peu cher, et consomme peu d’énergie. Afin de minimiser la consommation d'énergie et le coût du système, la technologie Complementary Metal Oxide Semiconductor (CMOS) (0.35μm) est utilisée pour la première implémentation de SoC-Holter. Puis une nouvelle méthode de détection basée sur Acquisition Comprimée (CS) est introduite pour résoudre les problèmes de consommation d'énergie et de capacité de stockage de SoC-Holter. Le principe premier de cette plate-forme est d'échantillonner les signaux ECG sous la fréquence de Nyquist ‘sub-Nyquist’ et par la suite de classer directement les mesures compressées en états normal et anormal. Minimiser le nombre de fils qui relient les électrodes à la plate-forme peut rendre l’utilisateur de SoC-Holter plus confortable, car deux électrodes sont très proches sur la surface du corps. La différence ECG enregistrée est analysée à l'aide de Vectocardiogramme (VCG). Les résultats expérimentaux montrent qu'une approche intégrée, à faible coût et de faible encombrement (SoC-Holter) est faisable. Le SoC-Holter consomme moins de 10mW en fonctionnement. L'estimation des paramètres du signal acquis est effectuée directement à partir de mesures compressées, éliminant ainsi l'étape de la reconstruction et réduisant la complexité et le volume des calculs. En outre, le système fournit les signaux ECG compressés sans perte d'information, de ce fait il réduit significativement la consommation d'énergie pour l'envoi de message et l’espace de stockage mémoire. L'effet de placement des électrodes est évalué sur la QRS complexe lorsqu'il a enregistré avec deux électrodes adjacentes. La méthode est basée sur l'algorithme de ‘QRS-VCG loop alignment’. La méthode moindre carré est utilisée pour estimer la corrélation entre une boucle VCG observée et une boucle de référence en respectant les transformations de rotation et la synchronisation du temps. Les emplacements d'électrodes les moins sensibles aux interférences sont étudiés. / According to the figures released by World Health Organization (WHO), cardiovascular disease is the number one cause of death in the world. In France every year more than 50,000 people die suddenly due cardiac arrhythmias. Identification of high risk sudden death patients is still a challenge. To detect cardiac arrhythmias, currently Holter is generally used to record 1~4 leads electrocardiogram (ECG) signals during 24h to 72h. However the use of Holter is limited among the population due to its form factor (not user-friendly) and cost. An integrated single chip wearable Holter named SoC-Holter that enables to record 1 to 4 leads ECG is introduced. Deployment of wireless sensor network requires each SoC-Holter with less power consumption, low-cost charging system and less die area.To minimize energy consumption and system cost, Complementary Metal Oxide Semiconductor (CMOS) technology (0.35μm) is used to prototype the first implementation of SoC-Holter. Then a novel method based on Compressed Sensing (CS) technique is introduced for solving the problems of power consumption and storage capacity of SoC-Holter. The main principle underlying this framework is to sample analog signals at sub-Nyquist rate and to classify directly compressed measurement into normal and abnormal state. Minimizing the wire connected electrodes to the platform can make the carrier more comfortable because two electrodes are attached closely on the surface of the body. Recording difference ECG is analyzed using Vectorcardiogram (VCG) theory. Experimental results show that an integrated, low cost, and user-friendly SoC-Holter is feasible. SoC-Holter consumes less than 10mW while the device is operating. It takes advantage of estimating parameters directly from compressed measurements, thereby eliminating the reconstruction stage and reducing the computational complexity on the platform. In addition, the framework provides compressed ECG signals without loss of information, reducing significantly the power consumption for message sending and memory storage space. The effect of electrode placement is evaluated by estimating QRS complex in recorded ECG signals by two adjacent electrodes. The method is based on the QRS-VCG loop alignment algorithm that estimates Least Square (LS) between an observed VCG loop and a reference loop with respect to the transformations of rotation and time synchronization. The electrode location with less sensitive to interference is investigated.

La fibrillation atriale (FA) est une complication fréquente de la phase aiguë de l’infarctus (IDM) avec un moins bon pronostic des patients. Sa forme silencieuse pourrait être fréquente après un IDM. Cependant, toutes les études ayant porté sur la FA se sont focalisées sur les formes symptomatique, paroxystique ou persistante. De plus, la réduction de la biodisponibilité du •NO et la dysfonction endothéliale peuvent altérer le pronostic des patients en FA. Or, l’asymétrique diméthylarginine (ADMA) en inhibant de façon endogène l’action des NO synthases peut conduire à une dysfonction endothéliale, une inflammation ou encore à un stress oxydatif, qui sont impliqués dans de nombreuses pathologies cardiovasculaires. Cependant, au-cune étude n’a évalué la relation potentielle entre le taux plasmatique d’ADMA et la survenue d’une FA après un IDM.Notre objectif a été d’évaluer dans le cadre d’une étude prospective le pronos-tic hospitalier et à un an des patients présentant de la FA silencieuse en phase ai-guë d’IDM, et évaluer le lien potentiel entre les dimethylarginines et l’apparition d’une FA. Notre première étude prospective montre pour la première fois que la FA si-lencieuse est plus fréquente que la FA symptomatique et est associée à un moins bon pronostic après un IDM.Notre second travail, démontre que l’impact négatif de la FA silencieuse sur le pronostic des patients se maintient à un an après l’IDM.Notre troisième travail montre également, que l’ADMA est associée de ma-nière indépendante à la survenue d’une FA symptomatique après un IDM. Ces données suggèrent qu’un dépistage et qu’une prise en charge spécifiques de la FA après un IDM pourraient améliorer le pronostic des patients. L’ADMA pourrait ainsi être utilisée comme un marqueur de risque de passage en FA après un IDM. / Atrial fibrillation (AF) is a frequent complication of acute myocardial infarction (AMI) with a poorer prognosis. Silent atrial fibrillation has been suggested to be frequent after AMI. However, most part of the studies has targeted only paroxysmal or persistent AF. Thus, Reduced Nitric Oxide availability and endothelial dysfunction has been recently recognized as a possible contributor to altered prognosis in AF. Asymmetric dimethylarginine (ADMA) can inhibit nitric oxide synthase and leads to endothelial dysfunction, inflammation and oxidative stress in multiple cardiovascular diseases. However, any study has addressed the relationship between ADMA levels and the occurrence of AF in AMI.We aimed to assess in-hospital and 1-year prognosis in patients experiencing silent AF in AMI and evaluate the potential relationship between dimethylarginines plasma levels and the occurrence AF after acute myocardial infarction.Our first prospective study shows for the first time that silent AF is more frequent than symptomatic AF after AMI and is associated with a worse prognosis.Our second work confirms the impact of silent AF on prognosis, with a prognosis that remains worse one year after the acute phase of MI. Our third work proved that ADMA is independently associated with symptomatic AF after AMI and strengthen the capacity to estimate symptomatic AF occurrence. In conclusion our studies highlight that AF is not a negligible event after AMI, in particular silent AF. That suggests that systematic screening and specific management should be investigated in order to improve outcomes of patients. ADMA appears to be a potential predictor of AF after AMI, because of its significant association.