Authors:
Tomoko Kamimura
1
;
Risa Otsuka
1
;
Asaka Domoto
1
;
Hikofumi Suzuki
2
and
Mamino Tokita
3
Affiliations:
1
Department of Health Sciences, Graduate School of Medicine, Shinshu University, 3-1-1 Asahi, Matsumoto, Japan
;
2
Department of Cyber Science Infrastructure Development, National Institute of Informatics, Tokyo, Japan
;
3
Department of Global Centre for Advanced Research on Logic and Sensibility, Keio University Tokyo, Japan
Keyword(s):
Sleep Disturbance, Total Sleep Time, Cognitive Impairment, Alzheimer’s Disease, Self-Organizing Map.
Abstract:
Bed sensor systems are useful for measuring sleep states in cognitively impaired older adults because they can measure unrestrained individuals. However, there are no criteria for identifying sleep abnormalities using them. We developed a method to determine sleep abnormalities by analysing data collected by a bed sensor system using a self-organizing map (SOM). In this study, the sleep states were measured in two cognitively impaired care-facility residents. These recordings were used to calculate total nocturnal sleep time, wake time after sleep onset, frequency of leaving the bed, and frequency of awakening in the bed for each day. The data from these four variables were used to draw an SOM for each individual’s sleep state to identify normal or abnormal sleep days. We visually determined whether a main cluster was formed in the SOM. If a main cluster was formed, the days included in the main cluster were defined as the individual’s normal days, while other days were defined as th
e individual’s abnormal days. The above parameters were independently compared between the two groups, as determined by the SOM. The characteristics of abnormal sleep days identified by SOM could be explained using these four variables, suggesting the effectiveness of identifying abnormal days by SOM.
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