Entwicklungsanalyse für das Medium Print am Beispiel der BILD-Zeitung

nicht vorhandenIn recent years the degree of sophistication regarding news outlets’ distribution channels has grown exponentially, due to the advancement of current technologies. This bachelor thesis takes aim at these alterations in the media landscape, in order to analyze the economic and social impact on the print newspaper market. As a subject of my analysis I will utilize the Bild Zeitung and will attempt to highlight the evolution at the core of that segment. Subse-quently I will dissect future consumption potential of non digital media alongside its digital substitutional goods

Weighted Brain Network Analysis on Different Stages of Clinical Cognitive Decline

This study addresses brain network analysis over different clinical severity stages of cognitive dysfunction using electroencephalography (EEG). We exploit EEG data of subjective cognitive impairment (SCI) patients, mild cognitive impairment (MCI) patients and Alzheimer’s disease (AD) patients. We propose a new framework to study the topological networks with a spatiotemporal entropy measure for estimating the connectivity. Our results show that functional connectivity and graph analysis are frequency-band dependent, and alterations start at the MCI stage. In delta, the SCI group exhibited a decrease of clustering coefficient and an increase of path length compared to MCI and AD. In alpha, the opposite behavior appeared, suggesting a rapid and high efficiency in information transmission across the SCI network. Modularity analysis showed that electrodes of the same brain region were distributed over several modules, and some obtained modules in SCI were extended from anterior to posterior regions. These results demonstrate that the SCI network was more resilient to neuronal damage compared to that of MCI and even more compared to that of AD. Finally, we confirm that MCI is a transitional stage between SCI and AD, with a predominance of high-strength intrinsic connectivity, which may reflect the compensatory response to the neuronal damage occurring early in the disease process

Etude topologique de l'organisation fonctionnelle cérébrale aux stades précoces de la maladie d'Alzheimer par électroencéphalographie

Electroencephalography (EEG) is still considered nowadays as a convenient neuroimaging technique in clinical applications, suitable for cognitively and physically disabled patients, as well as for serial tests. In fact, EEG is a non-invasive, cost-effective, and mobile technology. It is characterized by a high temporal resolution, which is crucial for the analysis of fast brain functional dynamics.There is a rich literature addressing the use of EEG to investigate brain activity alterations due to neurodegenerative diseases, especially Alzheimer's disease (AD). AD is a chronic neurodegenerative disease that leads to progressive decline of cognitive functions along with behavioral disorders and insidious loss of autonomy in daily living activities. We observe a growing interest in the earlier stages of the disease since curative treatments are still lacking. The preclinical stage of AD is asymptomatic, but the brain lesions due to AD are present. At this phase, the term of subjective cognitive impairment (SCI) has been recently defined. In the prodromal stage, mild cognitive impairment (MCI) patients show measurable memory impairments but their functional capacity is maintained. SCI and MCI patients are at high risk of developing AD.This thesis investigates the early diagnosis of AD at preclinical and prodromal stages using resting-state EEG, and addresses brain network analysis by studying the functional connectivity over several clinical stages of cognitive decline (SCI, MCI and Mild AD). To this end, we conduct a retrospective study using a clinical database that contains EEG signals recorded in real-life conditions.We first propose to exploit an entropy measure, termed “epoch-based entropy” (EpEn), as a measure of functional connectivity, that relies on a refined statistical modeling of EEG signals based on Hidden Markov Models. This measure characterizes the spatiotemporal changes in EEG signals by quantifying the information content of EEG signals, both at the time and spatial levels.Furthermore, we conduct a topological brain network analysis over the three stages of cognitive decline by employing the Graph Theory. The novelty of our work is twofold. Actually, this is the first work that: (i) addresses EEG brain network analysis over SCI, MCI and Mild AD stages simultaneously, and (ii) combines EpEn to Graph Theory since we have shown its effectiveness in quantifying the complete spatiotemporal alteration due to AD.In this thesis, we decided to invest the largest amount of EEG information for brain network analysis, by exploiting several frequency ranges (delta, theta, alpha, beta), several electrodes locations (instead of regions), and several network density scales (multiple graph thresholding). Therefore, another issue tackled in this thesis concerns the identification of relevant EEG markers to discriminate automatically between SCI, MCI and AD patients in the context of graph analysis framework. To this end, we propose an automatic hierarchical method for EEG analysis, which allows the extraction of relevant markers from large amount of information based on a single EEG connectivity measure.Finally, we also assess the correlation between the relevant EEG markers and the clinical markers at our disposal (MMSE, RL/RI-16, BREF).L'électroencéphalographie (EEG) est encore considérée de nos jours comme une technique de neuroimagerie très utile dans les applications cliniques, adaptée aux patients souffrant de troubles cognitifs et physiques, ainsi qu'aux tests à grande échelle. L'EEG est une technologie non invasive, peu coûteuse et facilement accessible. Elle se caractérise par une haute résolution temporelle, ce qui est crucial pour le suivi de la dynamique cérébrale.Plusieurs travaux dans la littérature ont exploité l'EEG pour étudier les altérations de l'activité cérébrale liées aux maladies neurodégénératives, notamment la maladie d'Alzheimer (MA). La MA est une maladie neurodégénérative chronique qui entraîne un déclin progressif des fonctions cognitives, ainsi que des troubles du comportement et une perte insidieuse d'autonomie au quotidien. En l'absence de traitements curatifs, nous observons un intérêt croissant à la caractérisation de l'activité cérébrale aux stades précoces de la maladie. Le stade préclinique de la MA est asymptomatique, mais les lésions cérébrales dues à la MA sont présentes. A ce stade, on parle de troubles cognitifs subjectifs (subjective cognitive impairments, SCI). Au stade prodromal, les patients atteints de troubles cognitifs légers (mild cognitive impairment, MCI) présentent des troubles de la mémoire mesurables, mais leur capacité fonctionnelle est maintenue. Les patients atteints de troubles subjectifs ou légers présentent un risque élevé de développer la MA.Cette thèse s'intéresse au diagnostic précoce de la MA aux stades préclinique et prodromal en utilisant l'EEG au repos, et aborde l'analyse des réseaux cérébraux en étudiant la connectivité fonctionnelle à différents stades cliniques du déclin cognitif (SCI, MCI et MA au stade léger). Pour cela, nous avons mené une étude rétrospective en exploitant une base de données clinique qui contient des signaux EEG enregistrés en conditions réelles.En premier lieu, nous avons proposé d'exploiter une mesure d'entropie, appelée "Epoch-based Entropy" (EpEn), pour quantifier la connectivité fonctionnelle. Cette mesure repose sur une modélisation statistique fine des signaux EEG avec des modèles de Markov cachés. Cette mesure caractérise les changements spatio-temporels des signaux EEG en quantifiant le contenu d'information dans les signaux au niveau temporel et spatial.Par la suite, nous avons effectué une analyse topologique du réseau cérébral cortical de manière différentielle, en exploitant la théorie des graphes. La contribution de notre travail est double. En effet, il s'agit du premier travail qui : (i) aborde l'analyse du réseau cérébral chez les patients ayant des troubles subjectifs, des troubles légers et la MA au stade léger, et (ii) combine la mesure d'entropie à la théorie des graphes puisque nous avons démontré son efficacité à quantifier les changements spatio-temporels liés à la MA.Dans cette thèse, nous avons aussi abordé le problème de la grande quantité d'information extraite des signaux EEG, analysés sur plusieurs bandes de fréquences (delta, theta, alpha, beta), plusieurs électrodes, et plusieurs échelles de densité de réseau (seuillages multiples des graphes). Par conséquent, une autre contribution à ce travail de thèse concerne l'extraction de marqueurs EEG les plus pertinents pour discriminer automatiquement les trois groupes de patients. Ainsi, nous avons proposé une méthode hiérarchique pour l'analyse des signaux EEG, permettant d'identifier les descripteurs les plus pertinents à partir d'une grande quantité d'information issue d'une seule mesure de connectivité fonctionnelle. Enfin, nous avons évalué la corrélation entre les marqueurs numériques extraits des signaux EEG et les marqueurs cliniques à notre disposition (MMSE, RL/RI-16, BREF)

Etude topologique de l'organisation fonctionnelle cérébrale aux stades précoces de la maladie d'Alzheimer par électroencéphalographie

L'électroencéphalographie (EEG) est encore considérée de nos jours comme une technique de neuroimagerie très utile dans les applications cliniques, adaptée aux patients souffrant de troubles cognitifs et physiques, ainsi qu'aux tests à grande échelle. L'EEG est une technologie non invasive, peu coûteuse et facilement accessible. Elle se caractérise par une haute résolution temporelle, ce qui est crucial pour le suivi de la dynamique cérébrale.Plusieurs travaux dans la littérature ont exploité l'EEG pour étudier les altérations de l'activité cérébrale liées aux maladies neurodégénératives, notamment la maladie d'Alzheimer (MA). La MA est une maladie neurodégénérative chronique qui entraîne un déclin progressif des fonctions cognitives, ainsi que des troubles du comportement et une perte insidieuse d'autonomie au quotidien. En l'absence de traitements curatifs, nous observons un intérêt croissant à la caractérisation de l'activité cérébrale aux stades précoces de la maladie. Le stade préclinique de la MA est asymptomatique, mais les lésions cérébrales dues à la MA sont présentes. A ce stade, on parle de troubles cognitifs subjectifs (subjective cognitive impairments, SCI). Au stade prodromal, les patients atteints de troubles cognitifs légers (mild cognitive impairment, MCI) présentent des troubles de la mémoire mesurables, mais leur capacité fonctionnelle est maintenue. Les patients atteints de troubles subjectifs ou légers présentent un risque élevé de développer la MA.Cette thèse s'intéresse au diagnostic précoce de la MA aux stades préclinique et prodromal en utilisant l'EEG au repos, et aborde l'analyse des réseaux cérébraux en étudiant la connectivité fonctionnelle à différents stades cliniques du déclin cognitif (SCI, MCI et MA au stade léger). Pour cela, nous avons mené une étude rétrospective en exploitant une base de données clinique qui contient des signaux EEG enregistrés en conditions réelles.En premier lieu, nous avons proposé d'exploiter une mesure d'entropie, appelée "Epoch-based Entropy" (EpEn), pour quantifier la connectivité fonctionnelle. Cette mesure repose sur une modélisation statistique fine des signaux EEG avec des modèles de Markov cachés. Cette mesure caractérise les changements spatio-temporels des signaux EEG en quantifiant le contenu d'information dans les signaux au niveau temporel et spatial.Par la suite, nous avons effectué une analyse topologique du réseau cérébral cortical de manière différentielle, en exploitant la théorie des graphes. La contribution de notre travail est double. En effet, il s'agit du premier travail qui : (i) aborde l'analyse du réseau cérébral chez les patients ayant des troubles subjectifs, des troubles légers et la MA au stade léger, et (ii) combine la mesure d'entropie à la théorie des graphes puisque nous avons démontré son efficacité à quantifier les changements spatio-temporels liés à la MA.Dans cette thèse, nous avons aussi abordé le problème de la grande quantité d'information extraite des signaux EEG, analysés sur plusieurs bandes de fréquences (delta, theta, alpha, beta), plusieurs électrodes, et plusieurs échelles de densité de réseau (seuillages multiples des graphes). Par conséquent, une autre contribution à ce travail de thèse concerne l'extraction de marqueurs EEG les plus pertinents pour discriminer automatiquement les trois groupes de patients. Ainsi, nous avons proposé une méthode hiérarchique pour l'analyse des signaux EEG, permettant d'identifier les descripteurs les plus pertinents à partir d'une grande quantité d'information issue d'une seule mesure de connectivité fonctionnelle. Enfin, nous avons évalué la corrélation entre les marqueurs numériques extraits des signaux EEG et les marqueurs cliniques à notre disposition (MMSE, RL/RI-16, BREF).Electroencephalography (EEG) is still considered nowadays as a convenient neuroimaging technique in clinical applications, suitable for cognitively and physically disabled patients, as well as for serial tests. In fact, EEG is a non-invasive, cost-effective, and mobile technology. It is characterized by a high temporal resolution, which is crucial for the analysis of fast brain functional dynamics.There is a rich literature addressing the use of EEG to investigate brain activity alterations due to neurodegenerative diseases, especially Alzheimer's disease (AD). AD is a chronic neurodegenerative disease that leads to progressive decline of cognitive functions along with behavioral disorders and insidious loss of autonomy in daily living activities. We observe a growing interest in the earlier stages of the disease since curative treatments are still lacking. The preclinical stage of AD is asymptomatic, but the brain lesions due to AD are present. At this phase, the term of subjective cognitive impairment (SCI) has been recently defined. In the prodromal stage, mild cognitive impairment (MCI) patients show measurable memory impairments but their functional capacity is maintained. SCI and MCI patients are at high risk of developing AD.This thesis investigates the early diagnosis of AD at preclinical and prodromal stages using resting-state EEG, and addresses brain network analysis by studying the functional connectivity over several clinical stages of cognitive decline (SCI, MCI and Mild AD). To this end, we conduct a retrospective study using a clinical database that contains EEG signals recorded in real-life conditions.We first propose to exploit an entropy measure, termed “epoch-based entropy” (EpEn), as a measure of functional connectivity, that relies on a refined statistical modeling of EEG signals based on Hidden Markov Models. This measure characterizes the spatiotemporal changes in EEG signals by quantifying the information content of EEG signals, both at the time and spatial levels.Furthermore, we conduct a topological brain network analysis over the three stages of cognitive decline by employing the Graph Theory. The novelty of our work is twofold. Actually, this is the first work that: (i) addresses EEG brain network analysis over SCI, MCI and Mild AD stages simultaneously, and (ii) combines EpEn to Graph Theory since we have shown its effectiveness in quantifying the complete spatiotemporal alteration due to AD.In this thesis, we decided to invest the largest amount of EEG information for brain network analysis, by exploiting several frequency ranges (delta, theta, alpha, beta), several electrodes locations (instead of regions), and several network density scales (multiple graph thresholding). Therefore, another issue tackled in this thesis concerns the identification of relevant EEG markers to discriminate automatically between SCI, MCI and AD patients in the context of graph analysis framework. To this end, we propose an automatic hierarchical method for EEG analysis, which allows the extraction of relevant markers from large amount of information based on a single EEG connectivity measure.Finally, we also assess the correlation between the relevant EEG markers and the clinical markers at our disposal (MMSE, RL/RI-16, BREF)

Enhancing Security on Touch-Screen Sensors with Augmented Handwritten Signatures

We aim at enhancing personal identity security on mobile touch-screen sensors by augmenting handwritten signatures with specific additional information at the enrollment phase. Our former works on several available and private data sets acquired on different sensors demonstrated that there are different categories of signatures that emerge automatically with clustering techniques, based on an entropy-based data quality measure. The behavior of such categories is totally different when confronted to automatic verification systems in terms of vulnerability to attacks. In this paper, we propose a novel and original strategy to reinforce identity security by enhancing signature resistance to attacks, assessed per signature category, both in terms of data quality and verification performance. This strategy operates upstream from the verification system, at the sensor level, by enriching the information content of signatures with personal handwritten inputs of different types. We study this strategy on different signature types of 74 users, acquired in uncontrolled mobile conditions on a largely deployed mobile touch-screen sensor. Our analysis per writer category revealed that adding alphanumeric (date) and handwriting (place) information to the usual signature is the most powerful augmented signature type in terms of verification performance. The relative improvement for all user categories is of at least 93% compared to the usual signature

Online signature analysis for characterizing early stage Alzheimer’s disease: a feasibility study

International audienceWe aimed to explore the online signature modality for characterizing early-stage Alzheimer’s disease (AD). A few studies have explored this modality, whereas many on online handwriting have been published. We focused on the analysis of raw temporal functions acquired by the digitizer on signatures produced during a simulated check-filling task. Sample entropy was exploited to measure the information content in raw time sequences. We show that signatures of early-stage AD patients have lower information content than those of healthy persons, especially in the time sequences of pen pressure and pen altitude angle with respect to the tablet. The combination of entropy values on two signatures for each person was classified with two linear classifiers often used in the literature: support vector machine and linear discriminant analysis. The improvements in sensitivity and specificity were significant with respect to the a priori group probabilities in our population of AD patients and healthy subjects. We show that altitude angle, when combined with pen pressure, conveys crucial information on the wrist-hand-finger system during signature production for pathology detection

Weighted Brain Network Analysis on Different Stages of Clinical Cognitive Decline

This study addresses brain network analysis over different clinical severity stages of cognitive dysfunction using electroencephalography (EEG). We exploit EEG data of subjective cognitive impairment (SCI) patients, mild cognitive impairment (MCI) patients and Alzheimer’s disease (AD) patients. We propose a new framework to study the topological networks with a spatiotemporal entropy measure for estimating the connectivity. Our results show that functional connectivity and graph analysis are frequency-band dependent, and alterations start at the MCI stage. In delta, the SCI group exhibited a decrease of clustering coefficient and an increase of path length compared to MCI and AD. In alpha, the opposite behavior appeared, suggesting a rapid and high efficiency in information transmission across the SCI network. Modularity analysis showed that electrodes of the same brain region were distributed over several modules, and some obtained modules in SCI were extended from anterior to posterior regions. These results demonstrate that the SCI network was more resilient to neuronal damage compared to that of MCI and even more compared to that of AD. Finally, we confirm that MCI is a transitional stage between SCI and AD, with a predominance of high-strength intrinsic connectivity, which may reflect the compensatory response to the neuronal damage occurring early in the disease process

A comparative study of functional connectivity measures for brain network analysis in the context of AD detection with EEG

International audienceThis work addresses brain network analysis considering different clinical severity stages of cognitive dysfunction, based on resting-state electroencephalography (EEG). We use a cohort acquired in real-life clinical conditions, which contains EEG data of subjective cognitive impairment (SCI) patients, mild cognitive impairment (MCI) patients, and Alzheimer’s disease (AD) patients. We propose to exploit an epoch-based entropy measure to quantify the connectivity links in the networks. This entropy measure relies on a refined statistical modeling of EEG signals with Hidden Markov Models, which allow a better estimation of the spatiotemporal characteristics of EEG signals. We also propose to conduct a comparative study by considering three other measures largely used in the literature: phase lag index, coherence, and mutual information. We calculated such measures at different frequency bands and computed different local graph parameters considering different proportional threshold values for a binary network analysis. After applying a feature selection procedure to determine the most relevant features for classification performance with a linear Support Vector Machine algorithm, our study demonstrates the effectiveness of the statistical entropy measure for analyzing the brain network in patients with different stages of cognitive dysfunction