ArticleSensors (Basel, Switzerland)2022
Automated Detection of Sleep Apnea-Hypopnea Events Based on 60 GHz Frequency-Modulated Continuous-Wave Radar Using Convolutional Recurrent Neural Networks: A Preliminary Report of a Prospective Cohort Study.
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 12 papers.
What it found
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Who cites it
12 citing papers in PubMed, 34 citations in OpenAlex.
- Sleep-Timing Types and Weekend Catch-Up Sleep Patterns Associated With Above-Threshold Anxiety Symptoms in College Students: 28-Day Objective Monitoring.Stress and health : journal of the International Society for the Investigation of Stress · 2026Article
- Digital Technology for Sleep Symptoms in Parkinson's Disease: A Scoping Review.Journal of movement disorders · 2026Article
- Cross-Modality Transfer Learning from PSG to FMCW Radar for Event-Level Apnea-Hypopnea Segmentation.Bioengineering (Basel, Switzerland) · 2026Article
- Clinical study of an integrated sensor system for detection and classification of obstructive sleep apnea (OSA).Scientific reports · 2025Article
- Radar-Based Detection of Obstructive Sleep Apnea: A Systematic Review and Network Meta-Analysis of Diagnostic Accuracy Across Frequency Bands.Diagnostics (Basel, Switzerland) · 2025Review
- Event-Level Identification of Sleep Apnea Using FMCW Radar.Bioengineering (Basel, Switzerland) · 2025Article
- Unobtrusive Bed Monitor State of the Art.Sensors (Basel, Switzerland) · 2025Review
- Utilizing a Wireless Radar Framework in Combination With Deep Learning Approaches to Evaluate Obstructive Sleep Apnea Severity in Home-Setting Environments.Journal of multidisciplinary healthcare · 2025Article
- Combining a wireless radar sleep monitoring device with deep machine learning techniques to assess obstructive sleep apnea severity.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2024Article
- Convolutional Neural Networks for the Real-Time Monitoring of Vital Signs Based on Impulse Radio Ultrawide-Band Radar during Sleep.Sensors (Basel, Switzerland) · 2023Article
- Sleep Apnea Detection Using Wavelet Scattering Transformation and Random Forest Classifier.Entropy (Basel, Switzerland) · 2023Article
- Sensors, technologies, and classification algorithms for monitoring and diagnosis of sleep apnea.Digital healthReview
Corrections and comments
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Authors and funding
11 authors at 2 institutions in 1 country.
Funding
Abstract
Radar is a promising non-contact sensor for overnight polysomnography (PSG), the gold standard for diagnosing obstructive sleep apnea (OSA). This preliminary study aimed to demonstrate the feasibility of the automated detection of apnea-hypopnea events for OSA diagnosis based on 60 GHz frequency-modulated continuous-wave radar using convolutional recurrent neural networks. The dataset comprised 44 participants from an ongoing OSA cohort, recruited from July 2021 to April 2022, who underwent overnight PSG with a radar sensor. All PSG recordings, including sleep and wakefulness, were included in the dataset. Model development and evaluation were based on a five-fold cross-validation. The area under the receiver operating characteristic curve for the classification of 1-min segments ranged from 0.796 to 0.859. Depending on OSA severity, the sensitivities for apnea-hypopnea events were 49.0-67.6%, and the number of false-positive detections per participant was 23.4-52.8. The estimated apnea-hypopnea index showed strong correlations (Pearson correlation coefficient = 0.805-0.949) and good to excellent agreement (intraclass correlation coefficient = 0.776-0.929) with the ground truth. There was substantial agreement between the estimated and ground truth OSA severity (kappa statistics = 0.648-0.736). The results demonstrate the potential of radar as a standalone screening tool for OSA.
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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 Socratic graph.