ArticleSensors (Basel, Switzerland)2022
Performance Evaluation of a Smart Bed Technology against Polysomnography.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 32 citations in OpenAlex.
- Unconstrained deep learning-based sleep stage classification using cardiorespiratory and body movement activities in adults with suspected sleep apnea.Proceedings of the Japan Academy. Series B, Physical and biological sciences · 2025Article
- Estimating the Burden of Undiagnosed Heart Failure With Preserved Ejection Fraction and Sleep Abnormalities in the Community.Mayo Clinic proceedings · 2025Article
- Under the Covers: The Effect of a Temperature-Controlled Mattress Cover on Sleep and Perceptual Measures in Healthy Adults.Clocks & sleep · 2025Article
- Article
- Validating a smart bed against polysomnography for sleep apnea detection.Scientific reports · 2025Article
- RFID-embedded mattress for sleep disorder detection for athletes in sports psychology.Scientific reports · 2025Article
- Unobtrusive Bed Monitor State of the Art.Sensors (Basel, Switzerland) · 2025Review
- Sleep and cardiorespiratory function assessed by a smart bed over 10 weeks post COVID-19 infection.Scientific reports · 2025Article
- What radio waves tell us about sleep!Sleep · 2025Article
- Prediction of ECG signals from ballistocardiography using deep learning for the unconstrained measurement of heartbeat intervals.Scientific reports · 2025Article
- A ballistocardiogram dataset with reference sensor signals in long-term natural sleep environments.Scientific data · 2024Article
- Unobtrusive Skin Temperature Estimation on a Smart Bed.Sensors (Basel, Switzerland) · 2024Article
- Fine tuned personalized machine learning models to detect insomnia risk based on data from a smart bed platform.Frontiers in neurology · 2024Article
- Feasibility of Unobtrusively Estimating Blood Pressure Using Load Cells under the Legs of a Bed.Sensors (Basel, Switzerland) · 2023Article
- A comprehensive study on the efficacy of a wearable sleep aid device featuring closed-loop real-time acoustic stimulation.Scientific reports · 2023Article
- Article
- Prediction of Sleep Stages Via Deep Learning Using Smartphone Audio Recordings in Home Environments: Model Development and Validation.Journal of medical Internet research · 2023Article
- Recent Progress in Long-Term Sleep Monitoring Technology.Biosensors · 2023Review
- Investigating Cardiorespiratory Interaction Using Ballistocardiography and Seismocardiography-A Narrative Review.Sensors (Basel, Switzerland) · 2022Review
- Noncontact Longitudinal Respiratory Rate Measurements in Healthy Adults Using Radar-Based Sleep Monitor (Somnofy): Validation Study.JMIR biomedical engineering · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The Sleep Number smart bed uses embedded ballistocardiography, together with network connectivity, signal processing, and machine learning, to detect heart rate (HR), breathing rate (BR), and sleep vs. wake states. This study evaluated the performance of the smart bed relative to polysomnography (PSG) in estimating epoch-by-epoch HR, BR, sleep vs. wake, mean overnight HR and BR, and summary sleep variables. Forty-five participants (aged 22-64 years; 55% women) slept one night on the smart bed with standard PSG. Smart bed data were compared to PSG by Bland-Altman analysis and Pearson correlation for epoch-by-epoch HR and epoch-by-epoch BR. Agreement in sleep vs. wake classification was quantified using Cohen's kappa, ROC analysis, sensitivity, specificity, accuracy, and precision. Epoch-by-epoch HR and BR were highly correlated with PSG (HR: r = 0.81, |bias| = 0.23 beats/min; BR: r = 0.71, |bias| = 0.08 breaths/min), as were estimations of mean overnight HR and BR (HR: r = 0.94, |bias| = 0.15 beats/min; BR: r = 0.96, |bias| = 0.09 breaths/min). Calculated agreement for sleep vs. wake detection included kappa (prevalence and bias-adjusted) = 0.74 ± 0.11, AUC = 0.86, sensitivity = 0.94 ± 0.05, specificity = 0.48 ± 0.18, accuracy = 0.86 ± 0.11, and precision = 0.90 ± 0.06. For all-night summary variables, agreement was moderate to strong. Overall, the findings suggest that the Sleep Number smart bed may provide reliable metrics to unobtrusively characterize human sleep under real life-conditions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.