Paper Info
Title
Detecting Chronic Diseases From Sleep-Wake Behaviour And Clinical Features
Abstract
Many chronic diseases show evidence of correlations with sleep-wake behaviour, and there is an increasing interest in making use of such correlations for early warning systems. This research presents an approach towards early chronic disease detection by mining sleep-wake measurements using deep learning. Specifically, a Long-Short-Term-Memory network is applied on actigraph data enriched with clinical history of patients. Experiments and analysis are performed targeting detection at an early and advanced disease stage based on different clinical data features. The results show for disease detection an averaged accuracy of 0.62, 0.73, 0.81, 0.77 for hypertension, diabetes, sleep apnea and chronic kidney disease, respectively. Early detection performs with an averaged accuracy of 0.49 for sleep apnea and 0.56 for diabetes. Nevertheless, compared to existing work, our approach shows an improvement in performance and demonstrates that predicting chronic diseases from sleep-wake behavior is feasible, though further investigation will be needed for early prediction.
Year
DOI
Venue
2018
10.1109/ICSAI.2018.8599388
2018 5TH INTERNATIONAL CONFERENCE ON SYSTEMS AND INFORMATICS (ICSAI)
Keywords
Field
DocType
Deep Learning, Chronic Disease Detection, Sleep Monitoring
Warning system,Early detection,Diabetes mellitus,Disease,Sleep apnea,Computer science,Intensive care medicine,Control engineering,Sleep monitoring,Kidney disease,Chronic disease
Conference
ISSN
Citations 
PageRank 
2474-0217
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Sarah Fallmann101.35
Liming Chen22607201.71