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Authors: Stefan Lüdtke ; Albert Hein ; Frank Krüger ; Sebastian Bader and Thomas Kirste

Affiliation: University of Rostock, Germany

ISBN: 978-989-758-212-7

Keyword(s): Sleep Detection, Actigraphy, Hidden Markov Model, Machine Learning, Dementia.

Related Ontology Subjects/Areas/Topics: Applications and Services ; Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computer Vision, Visualization and Computer Graphics ; Data Manipulation ; Devices ; Health Engineering and Technology Applications ; Health Information Systems ; Human-Computer Interaction ; Medical Image Detection, Acquisition, Analysis and Processing ; Methodologies and Methods ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Soft Computing ; Wearable Sensors and Systems

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 thi s 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. (More)

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Paper citation in several formats:
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

@conference{biosignals17,
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)},
year={2017},
pages={185-192},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006158801850192},
isbn={978-989-758-212-7},
}

TY - CONF

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

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