Actigraphic Sleep Detection for Real-World Data of Healthy Young Adults and People with Alzheimer’s Disease

Stefan Lüdtke, Albert Hein, Frank Krüger, Sebastian Bader, Thomas Kirste


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.


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Paper Citation

in Harvard Style

Lüdtke S., Hein A., Krüger F., Bader S. and Kirste T. (2017). Actigraphic Sleep Detection for Real-World Data of Healthy Young Adults and People with Alzheimer’s Disease . In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, (BIOSTEC 2017) ISBN 978-989-758-212-7, pages 185-192. DOI: 10.5220/0006158801850192

in Bibtex Style

author={Stefan Lüdtke and Albert Hein and Frank Krüger and Sebastian Bader and Thomas Kirste},
title={Actigraphic Sleep Detection for Real-World Data of Healthy Young Adults and People with Alzheimer’s Disease},
booktitle={Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, (BIOSTEC 2017)},

in EndNote Style

JO - Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, (BIOSTEC 2017)
TI - Actigraphic Sleep Detection for Real-World Data of Healthy Young Adults and People with Alzheimer’s Disease
SN - 978-989-758-212-7
AU - Lüdtke S.
AU - Hein A.
AU - Krüger F.
AU - Bader S.
AU - Kirste T.
PY - 2017
SP - 185
EP - 192
DO - 10.5220/0006158801850192