  Common medical conditions are often associated with sleep abnormalities.
Patients with medical disorders often suffer from poor sleep quality compared
to healthy individuals, which in turn may worsen the symptoms of the disorder.
Accurate detection of sleep/wake patterns is important in developing
personalized digital markers, which can be used for objective measurements and
efficient disease management. Big Data technologies and advanced analytics
methods hold the promise to revolutionize clinical research processes, enabling
the effective blending of digital data into clinical trials. Actigraphy, a
non-invasive activity monitoring method is heavily used to detect and evaluate
activities and movement disorders, and assess sleep/wake behavior. In order to
study the connection between sleep/wake patterns and a cluster headache
disorder, activity data was collected using a wearable device in the course of
a clinical trial. This study presents two novel modeling schemes that utilize
Deep Convolutional Neural Networks (CNN) to identify sleep/wake states. The
proposed methods are a sequential CNN, reminiscent of the bi-directional CNN
for slot filling, and a Multi-Task Learning (MTL) based model. Furthermore, we
expand standard "Sleep" and "Wake" activity states space by adding the "Falling
asleep" and "Siesta" states. We show that the proposed methods provide
promising results in accurate detection of the expanded sleep/wake states.
Finally, we explore the relations between the detected sleep/wake patterns and
onset of cluster headache attacks, and present preliminary observations.
