ArticleNature and science of sleep2021
It is All in the Wrist: Wearable Sleep Staging in a Clinical Population versus Reference Polysomnography.
Article in Nature and science of sleep, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Accuracy of Fitbit Charge 4, Garmin Vivosmart 4, and WHOOP Versus Polysomnography: Systematic Review.JMIR mHealth and uHealth · 2024Pooled it
- Article
- Effect of a grace period on false alarm rates of smartwatch-based out-of-hospital cardiac arrest detection systems: a pilot study.Resuscitation plus · 2026Article
- Predicting Sleep and Sleep Stage in Children Using Actigraphy and Heartrate via a Long Short-Term Memory Deep Learning Algorithm: A Performance Evaluation.Journal of sleep research · 2026Article
- An explainable deep learning approach for sleep staging in sleep apnea patients across all age subgroups from pulse oximetry signals.Engineering applications of artificial intelligence · 2025Article
- A lightGBM-based method for the signal quality assessment of wrist photoplethysmography.Physical and engineering sciences in medicine · 2025Article
- Wearables as Translational Physiomarkers and Clinical Endpoints in Insomnia Research: Can Sleep Research Advance Psychiatry?Journal of sleep research · 2025Review
- Hypnogram and Hypnodensity Analysis of REM Sleep Behaviour Disorder Using Both EEG and HRV-Based Sleep Staging Models.Journal of sleep research · 2025Article
- Detecting arousals and sleep from respiratory inductance plethysmography.Sleep & breathing = Schlaf & Atmung · 2025Article
- Enhancing artificial intelligence-driven sleep apnea diagnosis: The critical importance of input signal proficiency with a focus on mandibular jaw movements.Journal of prosthodontics : official journal of the American College of Prosthodontists · 2025Article
- Sleep Compensation on Work-Free days Is Associated with Better Sleep Quality.Nature and science of sleep · 2025Article
- Feature extraction tool using temporal landmarks in arterial blood pressure and photoplethysmography waveforms.NPJ cardiovascular health · 2025Article
- Multi-Night Home Assessment of Total Sleep Time Misperception in Obstructive Sleep Apnea with and Without Insomnia Symptoms.Clocks & sleep · 2024Article
- Overnight Sleep Staging Using Chest-Worn Accelerometry.Sensors (Basel, Switzerland) · 2024Article
- Performance of cardiorespiratory-based sleep staging in patients using beta blockers.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2024Article
- Performance Evaluation of a New Sport Watch in Sleep Tracking: A Comparison against Overnight Polysomnography in Young Adults.Sensors (Basel, Switzerland) · 2024Article
- Timing of Deep and REM Sleep Based on Fitbit Sleep Staging in Young Healthy Adults under Real-Life Conditions.Brain sciences · 2024Article
- Multisensor Integrated Platform Based on MEMS Charge Variation Sensing Technology for Biopotential Acquisition.Sensors (Basel, Switzerland) · 2024Article
- InsightSleepNet: the interpretable and uncertainty-aware deep learning network for sleep staging using continuous Photoplethysmography.BMC medical informatics and decision making · 2024Article
- Automated sleep staging on reduced channels in children with epilepsy.Frontiers in neurology · 2024Article
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThere is great interest in unobtrusive long-term sleep measurements using wearable devices based on reflective photoplethysmography (PPG). Unfortunately, consumer devices are not validated in patient populations and therefore not suitable for clinical use. Several sleep staging algorithms have been developed and validated based on ECG-signals. However, translation from these techniques to data derived by wearable PPG is not trivial, and requires the differences between sensing modalities to be integrated in the algorithm, or having the model trained directly with data obtained with the target sensor. Either way, validation of PPG-based sleep staging algorithms requires a large dataset containing both gold standard measurements and PPG-sensor in the applicable clinical population. Here, we take these important steps towards unobtrusive, long-term sleep monitoring.
methodsWe developed and trained an algorithm based on wrist-worn PPG and accelerometry. The method was validated against reference polysomnography in an independent clinical population comprising 244 adults and 48 children (age: 3 to 82 years) with a wide variety of sleep disorders.
resultsThe classifier achieved substantial agreement on four-class sleep staging with an average Cohen's kappa of 0.62 and accuracy of 76.4%. For children/adolescents, it achieved even higher agreement with an average kappa of 0.66 and accuracy of 77.9%. Performance was significantly higher in non-REM parasomnias (kappa = 0.69, accuracy = 80.1%) and significantly lower in REM parasomnias (kappa = 0.55, accuracy = 72.3%). A weak correlation was found between age and kappa (
conclusionThis study shows the feasibility of automatic wearable sleep staging in patients with a broad variety of sleep disorders and a wide age range. Results demonstrate the potential for ambulatory long-term monitoring of clinical populations, which may improve diagnosis, estimation of severity and follow up in both sleep medicine and research.
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