Paper Info
Title
Actigraphic Sleep Detection For Real-World Data Of Healthy Young Adults And People With Alzheimer'S Disease
Abstract
Actigraphy can be used to examine the sleep pattern of patients during the course of the day in their common environment. However, conventional sleep detection algorithms may not be appropriate for real-world daytime sleep detection, since they tend to overestimate the sleep duration and have only been validated for nighttime sleep in a laboratory setting. Therefore, we evaluated the performance of a set of new sleep detection algorithms based on machine learning methods in a real-world setting and compared them to two conventional sleep detection algorithms (Cole's algorithm and Sadeh's algorithm). For that, we performed two studies with (1) healthy young adults and (2) nursing home residents with Alzheimer's dementia. The conventional algorithms performed poorly for these real-world data sets, because they are imbalanced with respect to sensitivity and specificity. A more balanced Hidden Markov Model-based algorithm surpassed the conventional algorithms for both data sets. Using this algorithm leads to an improved accuracy of 4.1 percent points (pp) and 23.5 pp, respectively, compared to the conventional algorithms. The Youden-Index improved by 7.3 and 7.7, respectively. Overall, for a real-world setting, the HMM-based algorithm achieved a performance similar to conventional algorithms in a laboratory environment.
Year
DOI
Venue
2017
10.5220/0006158801850192
PROCEEDINGS OF THE 10TH INTERNATIONAL JOINT CONFERENCE ON BIOMEDICAL ENGINEERING SYSTEMS AND TECHNOLOGIES, VOL 4: BIOSIGNALS
Keywords
Field
DocType
Sleep Detection, Actigraphy, Hidden Markov Model, Machine Learning, Dementia
Computer vision,Gerontology,Disease,Computer science,Young adult,Artificial intelligence
Conference
Citations 
PageRank 
References 
0
0.34
0
Authors
5
Name
Order
Citations
PageRank
Stefan Lüdtke135.15
Albert Hein2316.51
Frank Krüger35310.43
Sebastian Bader45114.66
Thomas Kirste511725.44