ArticleJournal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine2018
A Novel Artificial Neural Network Based Sleep-Disordered Breathing Screening Tool.
Article in Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Advancements, challenges, and prospects of explainable AI in sleep disordered breathing.Sleep & breathing = Schlaf & Atmung · 2026Review
- Barriers of artificial intelligence implementation in the diagnosis of obstructive sleep apnea.Journal of otolaryngology - head & neck surgery = Le Journal d'oto-rhino-laryngologie et de chirurgie cervico-faciale · 2022Review
- Status quo and future prospects of artificial neural network from the perspective of gastroenterologists.World journal of gastroenterology · 2021Review
- Tracheal Sound Analysis Using a Deep Neural Network to Detect Sleep Apnea.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2019Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
STUDY
objectivesThis study evaluated a novel artificial neural network (ANN) based sleep-disordered breathing (SDB) screening tool incorporating nocturnal pulse oximetry with demographic, anatomic, and clinical data. The tool was compatible with 6 categories of apnea-hypopnea index (AHI) with 4% oxyhemoglobin desaturation threshold, ≥ 5, 10, 15, 20, 25, and 30 events/h.
methodsUsing a general population dataset, the training set included 2,280 subjects, whereas the test set included 470 subjects. The input of this tool was a set of 22 variables. The tool had six neural network models for each AHI threshold. Several metrics were explored to evaluate the performance of the tool: area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value, negative predictive value, and 95% confidence interval (CI).
resultsThe AUC was 0.904, 0.912, 0.913, 0.926, 0.930, and 0.954, respectively, with models of AHI ≥ 5, 10, 15, 20, 25, and 30 events/h thresholds. The sensitivities of all neural network models were higher than 95%. The AHI ≥ 30 events/h model had the maximum sensitivity: 98.31% (95% CI: 95.01%-100%).
conclusionsThe results of this study suggested that the ANN based SDB screening tool can be used to identify the presence or absence of SDB. Future validation should be performed in other populations to determine the practicability of this screening tool in sleep clinics and other at-risk populations.
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