Sleep quality prediction in caregivers using physiological signals
Open accessAbstract
Most caregivers of people with dementia (CPWD) experience a high degree of stress due to the demands of providing care, especially when addressing unpredictable behavioral and psychological symptoms of dementia. Such challenging responsibilities make caregivers susceptible to poor sleep quality with detrimental effects on their overall health. Hence, monitoring caregivers' sleep quality can provide important CPWD stress assessment. Most current sleep studies are based on polysomnography, which is expensive and potentially disrupts the caregiving routine. To address these issues, we propose a clinical decision support system to predict sleep quality based on trends of physiological signals in the deep sleep stage. This system utilizes four raw physiological signals using a wearable device (E4 wristband): heart rate variability, electrodermal activity, body movement, and skin temperature. To evaluate the performance of the proposed method, analyses were conducted on a two-week period of sleep monitored on eight CPWD. The best performance is achieved using the random forest classifier with an accuracy of 75% for sleep quality, and 73% for restfulness, respectively. We found that the most important features to detect these measures are sleep efficiency (ratio of amount of time asleep to the amount of time in bed) and skin temperature. The results from our sleep analysis system demonstrate the capability of using wearable sensors to measure sleep quality and restfulness in CPWD.
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Sadeghi et al. proposed a CDSS that used a Random Forest to predict the outcome, and the authors stated that the use of time-domain features leads to a transparent and explainable CDSS, but there is not sufficient information towards this claim.
“Sadeghi et al. [117] proposed a CDSS that used a Random Forest to predict the outcome.The authors stated that the use of time-domain features leads to a transparent and explainable CDSS, but there is not sufficient information towards this claim.”
From Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review (2021) · cites this paper as [117]
In Sadeghi et al.'s CDSS, time-domain features were used to make the system transparent and explainable.
“Time-domain features were used to make their system transparent and explainable.”
From Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review (2021) · cites this paper as [117]
Sadeghi et al. [117] implemented a CDSS to predict sleep quality based on physiological signal trends in deep sleep state.
“Sadeghi et al. [117] describe the implementation of a CDSS to predict sleep quality based on physiological signal trends in deep sleep state.”
From Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review (2021) · cites this paper as [117]
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- Authors
- Sadeghi, R., Banerjee, T., Hughes, J. C., & Lawhorne, L. W.
- Affiliations
- Wright State University (US)
- Corresponding author
- Sadeghi, Reza
- Venue
- Computers in Biology and Medicine
- Publisher
- Elsevier BV
- Published
- 2019-07
- Volume
- 110
- Pages
- 276-288
- ISSN
- 0010-4825 (ISSN-L)
- PMC
- PMC6655554
- OpenAlex
- W2946005651
- Cited by
- 50
- References
- 97
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 8 · 2023: 11 · 2022: 8 · 2021: 6 · 2020: 9
- FWCI
- 6.78
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Computers in Biology and Medicine (acceptedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Journal of Bioresource Management (submittedVersion, cc-by)
- Funders
- National Institutes of Health
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Sleep and related disorders
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Keywords
- sleep quality prediction, dementia caregivers, caregiver stress, physiological signals, wearable sensors, heart rate variability, electrodermal activity, body movement, skin temperature, sleep efficiency, restfulness, clinical decision support systems, random forest classification
- MeSH terms
- Wearable Electronic Devices, Dementia, Female, Humans, Male, Monitoring, Physiologic, Sleep, Caregivers, Polysomnography
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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