Automated Scoring of Respiratory Events in Sleep with a Single Effort Belt and Deep Neural Networks

Thijs Enagnon Nassi, Wolfgang Ganglberger, Haoqi Sun, Abigail Anne Bucklin, Siddharth Biswal, Michel Van Putten, Robert Thomas, Brandon Westover

Research output: Contribution to journalArticleAcademicpeer-review

10 Citations (Scopus)
131 Downloads (Pure)


Objective: Automatic detection and analysis of respiratory events in sleep using a single respiratory effort belt and deep learning. Methods: Using 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. Results: For 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 0.417.8 and a r<sup>2</sup> of 0.90. For the multiclass task, we obtained varying performances: 84% of all labeled central apneas were correctly classified, whereas this metric was 51% for obstructive apneas, 40% for respiratory effort related arousals and 23% for hypopneas. Conclusion: Our 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.

Original languageEnglish
Article number9656654
Pages (from-to)2094-2104
Number of pages11
JournalIEEE transactions on biomedical engineering
Issue number6
Early online date20 Dec 2021
Publication statusPublished - Jun 2022


  • Apnea Hypopnea Index
  • Belts
  • Convolutional neural networks
  • Deep learning
  • Indexes
  • Nose
  • Polysomnography
  • Respiratory effort
  • Respiratory event detection
  • Sleep apnea
  • Task analysis
  • 22/1 OA procedure


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