Contexte : Chaque année de multiples enfants guérissent des cancers pédiatriques, principalement de la leucémie lymphoblastique aiguë (LLA), le cancer le plus important chez les enfants. Ces enfants sont à risque de plusieurs comorbidités à la suite des traitements contre le cancer qui peuvent nuire à leur qualité de vie. La cardiotoxicité est la comorbidité la plus fréquente. Les maladies cardiaques découlant de cette cardiotoxicité sont la cause de décès non cancéreuse la plus fréquente chez les enfants survivants de cancer. La cardiotoxicité est une maladie hétérogène avec de multiples présentations cliniques et des mécanismes physiopathologiques qui ne sont pas encore totalement compris. L’exposition du cœur et des tissus adjacents à la radiothérapie et à la chimiothérapie peut induire des lésions cardiaques en entraînant une dégénérescence des cardiomyocytes qui mène éventuellement à la mort des cardiomyocytes. La perte progressive des cardiomyocytes fonctionnels entraîne un remodelage ventriculaire pathologique, dont les symptômes peuvent ne pas se manifester immédiatement. À un certain stade, les cardiomyocytes restants ne peuvent plus compenser pour les demandes cardiaques, ce qui cause l’apparition initiale de la dysfonction diastolique et de l’apparition ultérieure de la dysfonction systolique et de l’insuffisance cardiaque. La détection précoce des lésions cardiaques est donc devenue une nécessité pour réduire le risque et le fardeau de la cardiotoxicité chez les survivants de LLA pédiatrique. Il est possible que les paramètres de dépistage actuels sous-estiment le risque chez les survivants du cancer et qu'une évaluation cardiaque plus large soit nécessaire. L’électrocardiogramme est un test utilisé pour évaluer la fréquence et le rythme cardiaques et permet de montrer des signes précoces de dommages cardiaques sous cliniques. Il peut montrer des arythmies, des changements plus subtiles qui précèdent les arythmies ou des changements ischémiques. L'évaluation cardiaque en situation de stress peut également révéler un dysfonctionnement myocardique. Ce qui nous mène à nous poser les questions suivantes : Quelle est la prévalence des anomalies à l’électrocardiogramme à la suite des traitements contre le cancer chez les survivants de LLA pédiatrique ? Est-ce que la cardiotoxicité entraîne des anomalies de la fréquence cardiaque à l’effort ? Est-ce que l’électrocardiogramme à l’effort permet d’identifier des anomalies de la repolarisation ventriculaire qui n’apparaissent pas au repos ? L’objectif est de détecter les anomalies à l’électrocardiogramme au repos et à l’effort pour améliorer la détection précoce de la cardiotoxicité. Résultats principaux : Nous avons confirmé l'apparition d'anomalies de l’électrocardiogramme, notamment des anomalies de la fréquence cardiaque, des intervalles QTc prolongés, des modifications de l'onde ST-T, des ondes Q pathologiques, des arythmies, des troubles de la conduction et des voltage QRS bas, qui étaient associés au traitement de la LLA pédiatrique. Les anomalies de la fréquence cardiaque pendant l’effort et à l’effort maximal sont fréquentes dans notre cohorte de 251 survivants de LLA pédiatrique. Nous avons montré que la majorité des survivants de LLA pédiatrique ont une fréquence cardiaque anormale au maximum de leur effort. Certains participants présentaient aussi de l’incompétence chronotrope, ce qui est une condition anormale pour de jeunes adultes. Nous avons identifié des allongements de l’intervalle QT au repos, mais davantage à l’effort. Nous avons également comparé l’utilisation de différentes approches pour la correction de l’intervalle QT afin d’avoir une évaluation précise de l’intervalle QTc. Conclusions : L’épreuve d’effort avec un électrocardiogramme est un moyen non invasif et rentable d'évaluer l'activité électrique cardiaque, permettant de détecter des changements subtils qui peuvent indiquer une cardiotoxicité sous-jacente. Cette recherche est cruciale pour s'assurer que les survivants de LLA pédiatrique vivent non seulement plus longtemps, mais qu'ils mènent également une vie saine et épanouie. / Backgrounds: Every year, many children recover from pediatric cancers, especially acute lymphoblastic leukemia (ALL), the most common childhood cancer. These children are at risk of several comorbidities following cancer treatment, which can adversely affect their quality of life. Cardiotoxicity is the most frequent comorbidity. Heart disease is the most common non-cancerous cause of death in childhood cancer survivors. Cardiotoxicity is a heterogeneous disease with multiple clinical presentations and often poorly understood pathophysiological mechanisms. It can lead to electrophysiological dysfunction and morphological and functional alterations. Exposure of the heart and surrounding tissues to radiotherapy and chemotherapy can induce cardiac lesions, leading to cardiomyocyte degeneration and eventually leading to cardiomyocyte death. Continued loss of functional cardiomyocytes leads to pathological ventricular remodeling, the symptoms of which may not be immediately apparent. At a certain stage, the remaining cardiomyocytes can no longer compensate for cardiac demands. This leads to the initial onset of diastolic dysfunction and the subsequent development of systolic dysfunction and heart failure. Early detection of cardiac damage has therefore become a key to reducing the risk and burden of cardiotoxicity in pediatric ALL survivors. It is possible that current screening parameters underestimate risk in pediatric cancer survivors and that a broader cardiac evaluation is required. An electrocardiogram is a test used to assess heart rate and rhythm and may show early signs of subclinical cardiac damage in pediatric cancer survivors who have received cardiotoxic treatments. The electrocardiogram can show cardiotoxicity through arrhythmias, the more subtle changes that precede arrhythmias, or ischemic changes. Stress cardiac evaluation can also reveal myocardial dysfunction. This leads us to ask the following questions: What is the prevalence of electrocardiogram abnormalities following cancer treatment in pediatric ALL survivors? Does cardiotoxicity lead to heart rate abnormalities during exercise? Does the exercise electrocardiogram identify ventricular repolarization abnormalities caused by cardiotoxicity that do not appear at rest? The aim is to detect electrocardiogram abnormalities at rest and during exercise to improve early detection of cardiotoxicity. Main results: We confirmed the occurrence of electrocardiogram abnormalities, including heart rate abnormalities, prolonged QTc intervals, ST-T wave changes, pathological Q waves, arrhythmias, conduction disturbances and low QRS voltage, which were associated with treatment of pediatric ALL. Heart rate abnormalities during exercise and at maximal effort are common in our cohort of 251 pediatric ALL survivors. We showed that the majority of pediatric ALL survivors have an abnormal heart rate at peak exertion. Some participants also exhibited chronotropic incompetence, which is an abnormal condition for young adults. We identified QT interval prolongations at rest, but more so during exercise. We also compared the use of different approaches to QT correction in order to obtain an accurate assessment of the QTc interval. Conclusions: Exercise testing with an electrocardiogram is a non-invasive and cost-effective means of assessing cardiac electrical activity, allowing detection of subtle changes that may indicate underlying cardiotoxicity. This research is crucial to ensuring that pediatric ALL survivors not only live longer, but also lead healthy, fulfilling lives.

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Atrial fibrillation is one of the most common cardiac rhythm disorders characterized by ever-increasing prevalence and incidence in the Czech Republic and abroad. The incidence of atrial fibrillation is reported at 2-4 % of the population, but due to the often asymptomatic course, the real prevalence is even higher. The aim of this work is to design an algorithm for automatic detection of atrial fibrillation in the ECG record. In the practical part of this work, an algorithm for the detection of atrial fibrillation is proposed. For the detection itself, the k-nearest neighbor method, the support vector method and the multilayer neural network were used to classify ECG signals using features indicating the variability of RR intervals and the presence of the P wave in the ECG recordings. The best detection was achieved by a model using a multilayer neural network classification with two hidden layers. Results of success indicators: Sensitivity 91.23 %, Specificity 99.20 %, PPV 91.23 %, F-measure 91.23 % and Accuracy 98.53 %.

The thesis deals with one of the non-linear methods for signal processing - dynamic time warping (DTW). The method observes shape changes, which should be used in biomedical signals processing. The thesis involves the method description and consecution for finding DTW optimal way. The method is applied on the number series in the edutainment program, on the group of simulated signals and real electrocardiograms (ECG). ECG recordings were gained by performing experiments on the Masaryk University and their aim was clarifying the influence of voltage-sensitive dye on the heart tissue. One-lead ECG was described in time domain, frequency domain, time-frequency domain and subsequently remitted to DTW algorithm. The method outcomes evaluates the diversity rate of ECG signals obtained in each experiment stages. During the data evaluation were followed up the changes in process of the tension-sensible paint application and the stage of scouring toward control. The difference of elaborating signals group was verified in the time domain (37,5 %), in the frequency domain (75 %) and in the time-frequency domain (25 %). However, due to the small data group was not possible to explicitly approve the activity of voltage-sensitive dye on the heart tissue and to determinate limiting value of minimum algorithm way DTW for each heart round electrocardiogram classification. In the more data group analysis it is supposed to manifest the trend of growth heart round ECG differences in the stage of staining toward the stage of scouring.

This thesis deals with analysis of T-wave Alternans (TWA), periodical changes of T wave in ECG signal. Presence of these alternans may predict higher risk of sudden cardiac death. From the several possible ways of TWA classification, the training algorithms of self organizing maps are used in this thesis. Result of the thesis is a program, which in the first step detects QRS complexes in the signal. Then, in the next step, gained reference points are used for T-waves detection. Detected waves are represented by a vector of significant points, which is used as an input for artificial neural network. Final output of the program is a decision about presence of TWA in the signal and its rate.

This thesis discusses the utilization of the artificial neural networks (ANN) for detection of coronary artery disease (CAD) in frequency area. The first part of this thesis is orientated towards the theoretical knowledge. Describes the issue of ECG pathological changes. ECQ are converted to frequency area. Described statistical methods and methods for automatic detection of CAD and MI. Explained the issue of the perceptron and ANN. The second deals with use of Neural Network Toolbox MATLAB®. This part focuses on counting and finding suitable parameters and making connection of band. At the end of the thesis UNS is used to detect ischemic parameters and the results are discussed. Average values for the best settings are 100% accuracy.

Measures of workload have been developed on the basis of the various definitions, some are designed to capture the multi-dimensional aspects of a unitary resource pool (Kahneman, 1973) while others are developed on the basis of multiple resource theory (Wickens, 2002). Although many theory based workload measures exist, others have often been constructed to serve the purpose of specific experimental tasks. As a result, it is likely that not every workload measure is reliable and valid for all tasks, much less each domain. To date, no single measure, systematically tested across experimental tasks, domains, and other measures is considered a universal measure of workload. Most researchers would argue that multiple measures from various categories should be applied to a given task to comprehensively assess workload. The goal for Study 1 to establish task load manipulations for two theoretically different tasks that induce distinct levels of workload assessed by both subjective and performance measures was successful. The results of the subjective responses support standardization and validation of the tasks and demands of that task for investigating workload. After investigating the use of subjective and objective measures of workload to identify a universal and comprehensive measure or set of measures, based on Study 2, it can only be concluded that not one or a set of measures exists. Arguably, it is not to say that one will never be conceived and developed, but at this time, one does not reside in the psychometric catalog. Instead, it appears that a more suitable approach is to customize a set of workload measures based on the task. The novel approach of assessing the sensitivity and comprehensive ability of conjointly utilizing subjective, performance, and physiological workload measures for theoretically different tasks within the same domain contributes to the theory by laying the foundation for improving methodology for researching workload. The applicable contribution of this project is a stepping-stone towards developing complex profiles of workload for use in closed-loop systems, such as human-robot team iv interaction. Identifying the best combination of workload measures enables human factors practitioners, trainers, and task designers to improve methodology and evaluation of system designs, training requirements, and personnel selection

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The use of autonomous robots in our society is increasing every day and a robot is no longer seen as a tool but as a team member. The robots are now working side by side with us and provide assistance during dangerous operations where humans otherwise are at risk. This development has in turn increased the need of robots with more human-awareness. Therefore, this master thesis aims at contributing to the enhancement of human-aware robotics. Specifically, we are investigating the possibilities of equipping autonomous robots with the capability of assessing and detecting activities in human teams. This capability could, for instance, be used in the robot's reasoning and planning components to create better plans that ultimately would result in improved human-robot teamwork performance. we propose to improve existing teamwork activity recognizers by adding intangible features, such as stress, motivation and focus, originating from human behavior models. Hidden markov models have earlier been proven very efficient for activity recognition and have therefore been utilized in this work as a method for classification of behaviors. In order for a robot to provide effective assistance to a human team it must not only consider spatio-temporal parameters for team members but also the psychological.To assess psychological parameters this master thesis suggests to use the body signals of team members. Body signals such as heart rate and skin conductance. Combined with the body signals we investigate the possibility of using System Dynamics models to interpret the current psychological states of the human team members, thus enhancing the human-awareness of a robot. / Användningen av autonoma robotar i vårt samhälle ökar varje dag och en robot ses inte längre som ett verktyg utan som en gruppmedlem. Robotarna arbetar nu sida vid sida med oss och ger oss stöd under farliga arbeten där människor annars är utsatta för risker. Denna utveckling har i sin tur ökat behovet av robotar med mer människo-medvetenhet. Därför är målet med detta examensarbete att bidra till en stärkt människo-medvetenhet hos robotar. Specifikt undersöker vi möjligheterna att utrusta autonoma robotar med förmågan att bedöma och upptäcka olika beteenden hos mänskliga lag. Denna förmåga skulle till exempel kunna användas i robotens resonemang och planering för att ta beslut och i sin tur förbättra samarbetet mellan människa och robot. Vi föreslår att förbättra befintliga aktivitetsidentifierare genom att tillföra förmågan att tolka immateriella beteenden hos människan, såsom stress, motivation och fokus. Att kunna urskilja lagaktiviteter inom ett mänskligt lag är grundläggande för en robot som ska vara till stöd för laget. Dolda markovmodeller har tidigare visat sig vara mycket effektiva för just aktivitetsidentifiering och har därför använts i detta arbete. För att en robot ska kunna ha möjlighet att ge ett effektivt stöd till ett mänskligtlag måste den inte bara ta hänsyn till rumsliga parametrar hos lagmedlemmarna utan även de psykologiska. För att tyda psykologiska parametrar hos människor förespråkar denna masteravhandling utnyttjandet av mänskliga kroppssignaler. Signaler så som hjärtfrekvens och hudkonduktans. Kombinerat med kroppenssignalerar påvisar vi möjligheten att använda systemdynamiksmodeller för att tolka immateriella beteenden, vilket i sin tur kan stärka människo-medvetenheten hos en robot. / <p>The thesis work was conducted in Stockholm, Kista at the department of Informatics and Aero System at Swedish Defence Research Agency.</p>