Visual Analysis for Narcolepsy
P. Bhaskar (TU Delft - Electrical Engineering, Mathematics and Computer Science)
A Vianova – Mentor (TU Delft - Computer Graphics and Visualisation)
Michel Westenberg – Mentor (Eindhoven University of Technology)
WP Brinkman – Graduation committee member (TU Delft - Interactive Intelligence)
Thomas Höllt – Graduation committee member (TU Delft - Computer Graphics and Visualisation)
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Abstract
Narcolepsy is a chronic neurological condition that results from the dysregulation of the sleep-wake cycle occurring in an early stage, specifically in adolescence. Patients with narcolepsy experience excessive daytime sleepiness, cataplexy, hypnagogic hallucinations, sleep paralysis and disturbed nocturnal sleep and these symptoms together form the narcolepsy symptom pentad. However, the symptoms related to narcolepsy are not limited to the pentad and cover a broad range of other symptoms, some of which are not directly related to sleep, like, increase in weight, binge eating, anxiety, agitation. Therefore, researchers from sleep medicine center, Kempenhaeghe wanted to understand how a selected set of 20 symptoms are related to narcolepsy.
In this thesis we present a visual analytics framework to help the researchers understand these symptoms in relation to narcolepsy and identify patterns among them. Using relevant attributes interesting population subsets are formed and the results are compared. The thesis comprises of three main tasks, the first being visualizing the distribution of individual attributes of patients in which a symptom is present and not present and for severity level. Then, pairs of symptoms are visualized based on their agreement and association to identify groups of related symptoms and trends present. Lastly, multiple symptoms are visualized to identify patterns amongst them.
The visual analytics framework was implemented and evaluated through a user study. The study showed that the visualization developed was useful in gaining new insights to form interesting hypothesis along with possible extensions for future development. This is the first step towards an extensive visual analytics framework in the study of narcolepsy symptom spectrum.