Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Aug 5, 2023
Date Accepted: Oct 8, 2024
Efficient Screening in Obstructive Sleep Apnea: Sequential Machine Learning Models Using Questionnaires and Pulse Oximetry Signals
ABSTRACT
Background:
Obstructive sleep apnea (OSA) is a prevalent sleep disorder characterized by frequent pauses or shallow breathing during sleep. Polysomnography, the gold standard for OSA assessment, is time-consuming and labor-intensive, thus limiting diagnostic efficiency.
Objective:
This study aims to develop two sequential machine learning models to efficiently screen and differentiate OSA.
Methods:
We utilized two datasets comprising 8,444 cases from the Sleep Heart Health Study and 1,229 cases from Taipei Veterans General Hospital (TVGH). The Model-Questionnaire was designed to distinguish OSA from primary insomnia using demographic information and Pittsburgh Sleep Quality Index (PSQI) questionnaires, while Model-Saturation categorized OSA severity based on multiple SpO2 parameters. The performance of the sequential machine learning models in screening and assessing the severity of OSA was evaluated using an independent test set derived from TVGH.
Results:
Model-Questionnaire achieved an F1 score of 0.86, incorporating demographic data and the PSQI. Model-Saturation training by the SHHS dataset displayed an F1 score of 0.82 when utilizing the power spectrum of SpO2 signals and reached the highest F1 score of 0.85 when considering all saturation-related parameters. Model-Saturation training by the TVGH dataset displayed an F1 score of 0.82. The independent test set showed stable results for Model-Questionnaire and Model-Saturation training by the TVGH dataset, but with a slightly decreased F1 score (0.78) in Model-Saturation training by the SHHS dataset. Despite reduced model accuracy across different datasets, precision remained at 0.89 for screening moderate to severe OSA.
Conclusions:
Although a composite model employing multiple saturation parameters exhibits higher accuracy, optimizing this model by identifying key factors is essential. Both models demonstrated adequate at-home screening capabilities for sleep disorders, particularly for patients unsuitable for in-lab sleep studies.
Citation
Per the author's request the PDF is not available.
Copyright
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