Modeling Sleep Quality Depending on Objective Actigraphic Indicators Based on Machine Learning Methods

Olga Vl. Bitkina, Jaehyun Park () and Jungyoon Kim
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Olga Vl. Bitkina: Department of Industrial and Management Engineering, Incheon National University (INU), Academy-ro 119, Incheon 22012, Korea
Jaehyun Park: Department of Industrial and Management Engineering, Incheon National University (INU), Academy-ro 119, Incheon 22012, Korea
Jungyoon Kim: Department of Computer Science, Kent State University, Kent, OH 44240, USA

IJERPH, 2022, vol. 19, issue 16, 1-14

Abstract: According to data from the World Health Organization and medical research centers, the frequency and severity of various sleep disorders, including insomnia, are increasing steadily. This dynamic is associated with increased daily stress, anxiety, and depressive disorders. Poor sleep quality affects people’s productivity and activity and their perception of quality of life in general. Therefore, predicting and classifying sleep quality is vital to improving the quality and duration of human life. This study offers a model for assessing sleep quality based on the indications of an actigraph, which was used by 22 participants in the experiment for 24 h. Objective indicators of the actigraph include the amount of time spent in bed, sleep duration, number of awakenings, and duration of awakenings. The resulting classification model was evaluated using several machine learning methods and showed a satisfactory accuracy of approximately 80–86%. The results of this study can be used to treat sleep disorders, develop and design new systems to assess and track sleep quality, and improve existing electronic devices and sensors.

Keywords: actigraphy; sleep quality; machine learning; support vector machine; k-nearest neighbors; naïve Bayes (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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