ArticleBiosensors2023
Machine Learning Assisted Wearable Wireless Device for Sleep Apnea Syndrome Diagnosis.
Article in Biosensors, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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.
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
11 citing papers in PubMed, 2 syntheses or guidelines pooled it, 38 citations in OpenAlex.
- Wearable Sensors and Artificial Intelligence for Sleep Apnea Detection: A Systematic Review.Journal of medical systems · 2025Pooled it
- Detection of Sleep Apnea Using Wearable AI: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2024Pooled it
- Transparent artificial intelligence-enabled interpretable and interactive sleep apnea assessment across flexible monitoring scenarios.Nature communications · 2025Article
- A bibliometric analysis of wearable device research in sleep science: Trends and hotspots.Medicine · 2025Review
- Revealing sleep and pain reciprocity with wearables and machine learning.Communications medicine · 2025Review
- Wearable Artificial Intelligence for Sleep Disorders: Scoping Review.Journal of medical Internet research · 2025Article
- Diagnostic Modalities in Sleep Disordered Breathing: Current and Emerging Technology and Its Potential to Transform Diagnostics.Respirology (Carlton, Vic.) · 2025Review
- Feature extraction tool using temporal landmarks in arterial blood pressure and photoplethysmography waveforms.NPJ cardiovascular health · 2025Article
- Developing probabilistic ensemble machine learning models for home-based sleep apnea screening using overnight SpO2 data at varying data granularity.Sleep & breathing = Schlaf & Atmung · 2024Article
- Patent and Bibliometric Analysis of the Scientific Landscape of the Use of Pulse Oximeters and Their Prospects in the Field of Digital Medicine.Healthcare (Basel, Switzerland) · 2023Article
- 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
9 authors at 2 institutions in 1 country.
Funding
Abstract
Sleep apnea syndrome (SAS) is a common but underdiagnosed health problem related to impaired quality of life and increased cardiovascular risk. In order to solve the problem of complicated and expensive operation procedures for clinical diagnosis of sleep apnea, here we propose a small and low-cost wearable apnea diagnostic system. The system uses a photoplethysmography (PPG) optical sensor to collect human pulse wave signals and blood oxygen saturation synchronously. Then multiscale entropy and random forest algorithms are used to process the PPG signal for analysis and diagnosis of sleep apnea. The SAS determination is based on the comprehensive diagnosis of the PPG signal and blood oxygen saturation signal, and the blood oxygen is used to exclude the error induced by non-pathological factors. The performance of the system is compared with the Compumedics Grael PSG (Polysomnography) sleep monitoring system. This simple diagnostic system provides a feasible technical solution for portable and low-cost screening and diagnosis of SAS patients with a high accuracy of over 85%.
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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.