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The increased masses of data confronting us, originate a pressing need for the creation of a user interface for better handling and extracting knowledge from it. In this work we developed such a tool for the analysis of Heart Rate Variability (HRV). The analysis of HRV in patients with neuromuscular diseases, sleep disorders and cardiorespiratory problems has a strong impact on clinical practice. It has been widely used for monitoring the autonomic nervous system (ANS), whose regulatory effect controls the cardiac activity. These patients need to be continuously monitored, which originates data with huge sizes. Our interactive tool can perform a fast analysis of HRV from such data. It provides the analysis of HRV in time and frequency domains, and from non-linear methods. The tool is suitable to be run in a web environment, rendering it highly portable. It includes a programming feature, which enables the user to perform additional analysis of the data by giving direct access to the signals in a signal processing programming environment. We also added a report generation functionality, which is extremely important from a clinical standpoint, on which the evolution in time of relevant HRV parameters is depicted.
The increased masses of data confronting us, originate a pressing need for the creation of a user interface for better handling and extracting knowledge from it. In this work we developed such a tool for the analysis of Heart Rate Variability (HRV). The analysis of HRV in patients with neuromuscular diseases, sleep disorders and cardiorespiratory problems has a strong impact on clinical practice. It has been widely used for monitoring the autonomic nervous system (ANS), whose regulatory effect controls the cardiac activity. These patients need to be continuously monitored, which originates data with huge sizes. Our interactive tool can perform a fast analysis of HRV from such data. It provides the analysis of HRV in time and frequency domains, and from non-linear methods. The tool is suitable to be run in a web environment, rendering it highly portable. It includes a programming feature, which enables the user to perform additional analysis of the data by giving direct access to the signals in a signal processing programming environment. We also added a report generation functionality, which is extremely important from a clinical standpoint, on which the evolution in time of relevant HRV parameters is depicted.
perpétuo e sem limites geográficos, de arquivar e publicar esta dissertação através de exemplares impressos reproduzidos em papel ou de forma digital, ou por qualquer outro meio conhecido ou que venha a ser inventado, e de a divulgar através de repositórios científicos e de admitir a sua cópia e distribuição com objectivos educacionais ou de investigação, não comerciais, desde que seja dado crédito ao autor e editor. Gamboa, for the opportunity he gave me. I am very grateful for his encouragement, guidance and for the opportunity of working with his research team. I am also very thankful to my co-adviser, Professor Ricardo Matias, for all the knowledge and enthusiasm which allowed me to achieve my goals.The opportunity to participate in this project helped me growing and it is rewarding to know that I could contribute for the research in such an important area. AbstractBenefits of long-term monitoring have drawn considerable attention in healthcare.Since the acquired data provides an important source of information to clinicians and researchers, the choice for long-term monitoring studies has become frequent.However, long-term monitoring can result in massive datasets, which makes the analysis of the acquired biosignals a challenge. In this case, visualization, which is a key point in signal analysis, presents several limitations and the annotations handling in which some machine learning algorithms depend on, turn out to be a complex task.In order to overcome these problems a novel web-based application for biosignals visualization and annotation in a fast and user friendly way was developed. This was possible through the study and implementation of a visualization model. The main process of this model, the visualization process, comprised the constitution of the domain problem, the abstraction design, the development of a multilevel visualization and the study and choice of the visualization techniques that better communicate the information carried by the data. In a second process, the visual encoding variables were the study target. Finally, the improved interaction exploration techniques were implemented where the annotation handling stands out.Three case studies are presented and discussed and a usability study supports the reliability of the implemented work. ResumoOs benefícios da monitorização de longa duração têm recebido uma atenção considerável na área da saúde. Uma vez que os dados recolhidos constituem uma importante fonte de informação para médicos e investigadores, a escolha deste tipo de estudos tem--se tornado frequente.No entanto, este tipo de monitorização pode resultar em conjuntos de dados de grandes dimensões o que torna num desafio a análise dos biosinais adquiridos. Neste caso, a visualização que é um ponto-chave na análise de sinais, apresenta muitas limitações e a manipulação de anotações da qual dependem alguns algoritmos de machine learning, torna-se uma tarefa complexa.Por forma a superar estes problemas uma inovadora aplicação baseada nas tecnologias Web para a visualização e an...
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