ReviewPhysiological measurement2014
A review of signals used in sleep analysis.
Review in Physiological measurement, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
53 citing papers in PubMed.
- The DYNAM-O Toolbox: Characterizing Individualized Neural Signatures in Sleep EEG.bioRxiv : the preprint server for biology · 2026Article
- Ensemble learning approaches for early prediction of chronic kidney disease based on polysomnographic phenotype analysis.NPJ digital medicine · 2025Article
- Roles and features of smart control and sensing applications for sleep quality improvement: a scoping review.BMJ open · 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 Apnea Events Recognition Based on Polysomnographic Recordings: A Large-Scale Multi-Channel Machine Learning approach.IEEE open journal of engineering in medicine and biology · 2025Article
- A ballistocardiogram dataset with reference sensor signals in long-term natural sleep environments.Scientific data · 2024Article
- A Review on Automated Sleep Study.Annals of biomedical engineering · 2024Review
- [Wearables in rheumatology].Zeitschrift fur Rheumatologie · 2024Review
- Evaluating Accuracy in Five Commercial Sleep-Tracking Devices Compared to Research-Grade Actigraphy and Polysomnography.Sensors (Basel, Switzerland) · 2024Article
- Personalized interpretable prediction of perceived sleep quality: Models with meaningful cardiovascular and behavioral features.PloS one · 2024Article
- Article
- Prediction of obstructive sleep apnea using deep learning in 3D craniofacial reconstruction.Journal of thoracic disease · 2023Article
- Device-based and subjective measurements of sleep in children with cerebral palsy: a comparison of sleep diary, actigraphy, and bed sensor data.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2023Article
- Review
- A Comprehensive Review: Computational Models for Obstructive Sleep Apnea Detection in Biomedical Applications.BioMed research international · 2022Review
- Obstructive Sleep Apnea Detection Based on Sleep Sounds via Deep Learning.Nature and science of sleep · 2022Article
- Clinical and polysomnographic characteristics in adults referred to the sleep laboratory: a single-center study.Qatar medical journal · 2022Article
- Emergence and fragmentation of the alpha-band driven by neuronal network dynamics.PLoS computational biology · 2021Article
- Deconstructing Commercial Wearable Technology: Contributions toward Accurate and Free-Living Monitoring of Sleep.Sensors (Basel, Switzerland) · 2021Review
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
This article presents a review of signals used for measuring physiology and activity during sleep and techniques for extracting information from these signals. We examine both clinical needs and biomedical signal processing approaches across a range of sensor types. Issues with recording and analysing the signals are discussed, together with their applicability to various clinical disorders. Both univariate and data fusion (exploiting the diverse characteristics of the primary recorded signals) approaches are discussed, together with a comparison of automated methods for analysing sleep.
Indexed as
Identifiers
What OpenQuestion holds
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.