| Summary: | Benefits 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 projects are presented and discussed, and a usability study supports the
reliability of the implemented work.
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