ArticleNature and science of sleep2022
Fusion of Whole Night Features and Desaturation Segments Combined with Feature Extraction for Event-Level Screening of Sleep-Disordered Breathing.
Article in Nature and science of sleep, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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Who cites it
4 citing papers in PubMed, 6 citations in OpenAlex.
- Artificial intelligence for sleep-disordered breathing: a taxonomy of methods, data modalities, and clinical applications.Frontiers in sleep · 2026Review
- A Dual-Modal Wearable Pulse Detection System Integrated with Deep Learning for High-Accuracy and Low-Power Sleep Apnea Monitoring.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- A comparative study in class imbalance mitigation when working with physiological signals.Frontiers in digital health · 2024Article
- Artifacts classification and apnea events detection in neck photoplethysmography signals.Medical & biological engineering & computing · 2022Article
Corrections and comments
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Authors and funding
14 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: Misdiagnosis and missed diagnosis of sleep-disordered breathing (SDB) is common because polysomnography (PSG) is time-consuming, expensive, and uncomfortable. The use of recording methods based on the oxygen saturation (SpO2) signals detected by wearable devices is impractical and inaccurate for extracting signal features and detecting apnoeic events. We propose a method to automatically detect the apnoea-based SpO Patients and Methods: First, apnoea-related desaturation segments in raw SpO Results: The model was trained on 500 individuals and tested on 8131 individuals from two public hospitals and one private centre. In the testing data, the classification accuracy for apnoea-related segments was 84.3%. Individuals with SDB (AHI 15) were identified with a mean accuracy of 88.95%. Conclusion: Using automatic SDB detection based on SpO
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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.