Authors:
Maria Tsiobra
1
;
Georgios Nikolis
1
;
Christos Diamantakis
1
;
Matthew Salanitro
2
;
Ilias Kalamaras
1
;
Vasilis Lwlis
1
;
Thomas Penzel
2
;
Konstantinos Votis
1
and
Dimitrios Tzovaras
1
Affiliations:
1
Centre for Research and Technology Hellas (CERTH), Thessaloniki, Greece
;
2
Charité - Universitätsmedizin, Berlin
Keyword(s):
Sleep Analysis, Visual Analytics, Spindle Detection, Healthcare, Sleep Stage Classification, Interactive Visualization, Sleep Monitoring.
Abstract:
Monitoring the quality of sleep in patients of sleep disorders is often a time-consuming process, where the clinician manually navigates through large volumes of recorded polysomnography data in an effort to visually detect sleep patterns, such as sleep spindles, sleep stages and hints of disorders. We propose an application that provides healthcare professionals with advanced tools for sleep analysis and spindle detection through visual analytics for pattern detection, AI-based sleep scoring, and an interactive user interface. The system processes multiple physiological signals and provides both raw data visualization, advanced feature analysis capabilities, and a two-dimensional embedding of sleep intervals. By combining signal processing, spindle detection, sleep stage identification and interactive visualization tools, this work helps researchers to efficiently identify, validate, and analyze sleep and spindle characteristics with higher precision than traditional methods.