SynthesisSensors (Basel, Switzerland)2021
A Systematic Review of Sensing Technologies for Wearable Sleep Staging.
Synthesis in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 67 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
67 citing papers in PubMed, 2 syntheses or guidelines pooled it, 160 citations in OpenAlex.
- A systematic review of the performance of actigraphy in measuring sleep stages.Journal of sleep research · 2024Pooled it
- Neuroscience meets behavior: A systematic literature review on magnetic resonance imaging of the brain combined with real-world digital phenotyping.Human brain mapping · 2024Pooled it
- Effects of optimal timed automatic awakening from a short daytime nap on cognitive performance, alertness, and fatigue.Scientific reports · 2025Trial
- Sleep Stage Classification During CPAP Therapy from CPAP-Airflow and Wearable Fingertip Signals.Sensors (Basel, Switzerland) · 2026Article
- The Impact of Sunlight and Artificial Light at Night on Sleep Stages: Evidence From a 7-Day Sensor-Based Observational Study.JMIR mHealth and uHealth · 2026Observational
- Optimising Sleep Stage Detection Using a Minimal Non-EEG Physiological Signal Set and Deep Learning.Journal of sleep research · 2026Article
- Article
- Evaluation of a Contactless Sleep Monitoring Device for Sleep Stage Detection at Home in a Healthy Population: Prospective Study in Free-Living Conditions.JMIR human factors · 2026Article
- Cerebral and Peripheral Hemodynamics Across Wakefulness and NREM Sleep.Journal of sleep research · 2026Article
- Review
- Contactless Sleep Staging With Radar: A Transfer Learning Approach.IEEE open journal of engineering in medicine and biology · 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
- SE-Res-U-Net: an improved U-Net architecture for efficient sleep state detection and classification.Scientific reports · 2025Article
- Mapping EEG Metrics to Human Affective and Cognitive Models: An Interdisciplinary Scoping Review from a Cognitive Neuroscience Perspective.Biomimetics (Basel, Switzerland) · 2025Review
- Beyond the Sleep Lab: A Narrative Review of Wearable Sleep Monitoring.Bioengineering (Basel, Switzerland) · 2025Review
- MultiScaleSleepNet: A Hybrid CNN-BiLSTM-Transformer Architecture with Multi-Scale Feature Representation for Single-Channel EEG Sleep Stage Classification.Sensors (Basel, Switzerland) · 2025Article
- Evaluating the performance of wearable EEG sleep monitoring devices: a meta-analysis approach.npj biomedical innovations · 2025Article
- Patient Driven EKG Device Performance in Adults with Fontan Palliation.Pediatric cardiology · 2025Article
- Article
- Roles and features of smart control and sensing applications for sleep quality improvement: a scoping review.BMJ open · 2025Article
7 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Designing wearable systems for sleep detection and staging is extremely challenging due to the numerous constraints associated with sensing, usability, accuracy, and regulatory requirements. Several researchers have explored the use of signals from a subset of sensors that are used in polysomnography (PSG), whereas others have demonstrated the feasibility of using alternative sensing modalities. In this paper, a systematic review of the different sensing modalities that have been used for wearable sleep staging is presented. Based on a review of 90 papers, 13 different sensing modalities are identified. Each sensing modality is explored to identify signals that can be obtained from it, the sleep stages that can be reliably identified, the classification accuracy of systems and methods using the sensing modality, as well as the usability constraints of the sensor in a wearable system. It concludes that the two most common sensing modalities in use are those based on electroencephalography (EEG) and photoplethysmography (PPG). EEG-based systems are the most accurate, with EEG being the only sensing modality capable of identifying all the stages of sleep. PPG-based systems are much simpler to use and better suited for wearable monitoring but are unable to identify all the sleep stages.
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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.