ArticleDigital health
Novel method combining multiscale attention entropy of overnight blood oxygen level and machine learning for easy sleep apnea screening.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Investigating the Effects of Paralinguistic Configuration in Background Human Speech on Attention: Protocol for a Within-Subject Study.JMIR research protocols · 2026Article
- Development of Non-Invasive Continuous Glucose Prediction Models Using Multi-Modal Wearable Sensors in Free-Living Conditions.Sensors (Basel, Switzerland) · 2025Article
- Detection and Severity Classification of Sleep Apnea Using Continuous Wearable SpOSensors (Basel, Switzerland) · 2025Article
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Authors and funding
1 author.
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No grant is acknowledged in the PubMed record.
Abstract
Objective: Sleep apnea is a common sleep disorder affecting a significant portion of the population, but many apnea patients remain undiagnosed because existing clinical tests are invasive and expensive. This study aimed to develop a method for easy sleep apnea screening. Methods: Three supervised machine learning algorithms, including logistic regression, support vector machine, and light gradient boosting machine, were applied to develop apnea screening models at two apnea-hypopnea index cutoff thresholds: Results: For cutoff Conclusions: Our models achieved better performance than existing methods and have the potential to be integrated with home-use pulse oximeters.
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Registered trials
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