ArticleSleep medicine2024
Deep learning of sleep apnea-hypopnea events for accurate classification of obstructive sleep apnea and determination of clinical severity.
Article in Sleep medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.
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Who cites it
13 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Performance of AI-Based Screening Tools for Obstructive Sleep Apnea Across Apnea-Hypopnea Index Thresholds: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Multichannel machine learning for polysomnographic diagnosis of obstructive sleep apnea: a Bayesian meta-analysis.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026Pooled it
- Revolutionizing Sleep Medicine: The Impact of Machine Learning on Diagnosis, Treatment, and Personalized Care.Health science reports · 2026Article
- Obstructive Sleep Apnea and Cardiovascular Disease: Mechanisms, Diagnostics, and Emerging Therapeutic Approaches.Biomedicines · 2026Review
- Machine Learning-Based Multidimensional Oximetry for Obstructive Sleep Apnea Screening: Development and External Validation.JMIR medical informatics · 2026Article
- Detection of apnea and hypopnea events using a wireless Abdomen-Worn sensor with SpO₂ integration.Sleep & breathing = Schlaf & Atmung · 2026Article
- A Multi-Task Deep Learning Approach for Simultaneous Sleep Staging and Apnea Detection for Elderly People.Interdisciplinary sciences, computational life sciences · 2026Article
- Multimodal physiological signal learning for clinical sleep respiratory event recognition.Frontiers in neurology · 2026Article
- Artificial intelligence in surgical planning and outcome prediction for obstructive sleep apnea: emerging hype or the future standard?Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2025Review
- AI-driven bone mineral density prediction from chest x-rays and its association with obstructive sleep apnea.PloS one · 2025Article
- Long-Term Management of Sleep Apnea-Hypopnea Syndrome: Efficacy and Challenges of Continuous Positive Airway Pressure Therapy-A Narrative Review.Medical sciences (Basel, Switzerland) · 2024Review
- Predicting Therapeutic Response to Hypoglossal Nerve Stimulation Using Deep Learning.The Laryngoscope · 2024Article
- Sensors, technologies, and classification algorithms for monitoring and diagnosis of sleep apnea.Digital healthReview
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
background/Objective: Automatic apnea/hypopnea events classification, crucial for clinical applications, often faces challenges, particularly in hypopnea detection. This study aimed to evaluate the efficiency of a combined approach using nasal respiration flow (RF), peripheral oxygen saturation (SpO2), and ECG signals during polysomnography (PSG) for improved sleep apnea/hypopnea detection and obstructive sleep apnea (OSA) severity screening.
methodsAn Xception network was trained using main features from RF, SpO2, and ECG signals obtained during PSG. In addition, we incorporated demographic data for enhanced performance. The detection of apnea/hypopnea events was based on RF and SpO2 feature sets, while the screening and severity categorization of OSA utilized predicted apnea/hypopnea events in conjunction with demographic data.
resultsUsing RF and SpO2 feature sets, our model achieved an accuracy of 94 % in detecting apnea/hypopnea events. For OSA screening, an exceptional accuracy of 99 % and an AUC of 0.99 were achieved. OSA severity categorization yielded an accuracy of 93 % and an AUC of 0.91, with no misclassification between normal and mild OSA versus moderate and severe OSA. However, classification errors predominantly arose in cases with hypopnea-prevalent participants.
conclusionsThe proposed method offers a robust automatic detection system for apnea/hypopnea events, requiring fewer sensors than traditional PSG, and demonstrates exceptional performance. Additionally, the classification algorithms for OSA screening and severity categorization exhibit significant discriminatory capacity.
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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.