ArticleIEEE transactions on bio-medical engineering2022
Automated Scoring of Respiratory Events in Sleep With a Single Effort Belt and Deep Neural Networks.
Article in IEEE transactions on bio-medical engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- SleepJEPA: Learning the latent world of sleep with at-home sleep data to estimate disease risk.medRxiv : the preprint server for health sciences · 2026Article
- Detection of apnea and hypopnea events using a wireless Abdomen-Worn sensor with SpO₂ integration.Sleep & breathing = Schlaf & Atmung · 2026Article
- Expert-level probabilistic breathing event detector informs phenotyping of sleep apnea.Nature communications · 2026Article
- A multimodal sleep foundation model for disease prediction.Nature medicine · 2026Article
- Automated sleep stage and event detection algorithms using quality-controlled polysomnography annotations.Sleep advances : a journal of the Sleep Research Society · 2026Article
- EvoApneaFormer: an IoT and prognostic evolutionary deep learning-based framework for real-time multi-event sleep apnea disorder detection and remote monitoring.Frontiers in bioengineering and biotechnology · 2026Article
- Artificial intelligence for sleep-disordered breathing: a taxonomy of methods, data modalities, and clinical applications.Frontiers in sleep · 2026Review
- CAISR: achieving human-level performance in automated sleep analysis across all clinical sleep metrics.Sleep · 2025Article
- Event-Level Identification of Sleep Apnea Using FMCW Radar.Bioengineering (Basel, Switzerland) · 2025Article
- Overview of Respiratory Sensor Solutions to Support Patient Diagnosis and Monitoring.Sensors (Basel, Switzerland) · 2025Review
- A Multimodal Sleep Foundation Model Developed with 500K Hours of Sleep Recordings for Disease Predictions.medRxiv : the preprint server for health sciences · 2025Article
- Weakly Supervised Deep Learning for Monitoring Sleep Apnea Severity Using Coarse-grained Labels.IEEE transactions on automation science and engineering : a publication of the IEEE Robotics and Automation Society · 2025Article
- Deep learning of sleep apnea-hypopnea events for accurate classification of obstructive sleep apnea and determination of clinical severity.Sleep medicine · 2024Article
- Prediction of Sleep Apnea Events Using a CNN-Transformer Network and Contactless Breathing Vibration Signals.Bioengineering (Basel, Switzerland) · 2023Article
- The sleep and wake electroencephalogram over the lifespan.Neurobiology of aging · 2023Article
- Sleep disorder and apnea events detection framework with high performance using two-tier learning model design.PeerJ. Computer science · 2023Article
- 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.Sensors (Basel, Switzerland) · 2022Article
- Apnea Detection in Polysomnographic Recordings Using Machine Learning Techniques.Diagnostics (Basel, Switzerland) · 2021Article
- A deep learning algorithm model to automatically score and grade obstructive sleep apnea in adult polysomnography.Digital healthArticle
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Authors and funding
8 authors.
Funding
Abstract
objectiveAutomatic detection and analysis of respiratory events in sleep using a single respiratoryeffort belt and deep learning.
methodsUsing 9,656 polysomnography recordings from the Massachusetts General Hospital (MGH), we trained a neural network (WaveNet) to detect obstructive apnea, central apnea, hypopnea and respiratory-effort related arousals. Performance evaluation included event-based analysis and apnea-hypopnea index (AHI) stratification. The model was further evaluated on a public dataset, the Sleep-Heart-Health-Study-1, containing 8,455 polysomnographic recordings.
resultsFor binary apnea event detection in the MGH dataset, the neural network obtained a sensitivity of 68%, a specificity of 98%, a precision of 65%, a F1-score of 67%, and an area under the curve for the receiver operating characteristics curve and precision-recall curve of 0.93 and 0.71, respectively. AHI prediction resulted in a mean difference of 0.41 ± 7.8 and a r
conclusionOur fully automated method can detect respiratory events and assess the AHI accurately. Differentiation of event types is more difficult and may reflect in part the complexity of human respiratory output and some degree of arbitrariness in the criteria used during manual annotation. SIGNIFICANCE: The current gold standard of diagnosing sleep-disordered breathing, using polysomnography and manual analysis, is time-consuming, expensive, and only applicable in dedicated clinical environments. Automated analysis using a single effort belt signal overcomes these limitations.
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