AI Article Synopsis
- Sleep apnea is a prevalent sleep disorder that often goes undiagnosed due to the high cost and invasiveness of current tests; this study aimed to create a more accessible screening method.
- Three machine learning algorithms—logistic regression, support vector machine, and light gradient boosting machine—were used to analyze SpO2 recordings and demographic data to develop screening models at different apnea-hypopnea index thresholds.
- The results showed that the models performed better than existing screening methods, indicating potential for integration with home pulse oximeters for easier diagnosis.
Article 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: 5 and 30 events/hours. The SpO2 recordings of the Sleep Heart Health Study database ( = 5786) were used for model training, validation, and test. Multiscale entropy analysis was performed to derive a set of multiscale attention entropy features from the SpO2 recordings. Demographic features including age, sex, body mass index, and blood pressure were also used. The dependency among the multiscale attention entropy features were handled with the independent component analysis.
Results: For cutoff 5/hours, logistic regression model achieved the highest Matthew's correlation coefficient (0.402) and area under the curve (0.747), and reasonably good sensitivity (75.38%), specificity (74.02%), and positive predictive value (92.94%). For cutoff 30/hours, support vector machine model achieved the highest Matthew's correlation coefficient (0.545) and area under the curve (0.823), and good sensitivity (82.00%), specificity (82.69%), and negative predictive value (95.53%).
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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|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10627021 | PMC |
| http://dx.doi.org/10.1177/20552076231211550 | DOI Listing |
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