Predicting the Apnea-Hypopnea index for the diagnosis of obstructive sleep apnea with cardiovascular and cancer comorbidity using wearable sleep-tracking devices.

Hon Keung Tony Ng, Uyen Nguyen, Yusuf Akbulut, Thuan Tran, Toan Vo, Harun Pirim, Arveity Setty, Trung Le

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Article in Sleep & breathing = Schlaf & Atmung, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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8 authors.

Hon Keung Tony NgDepartment of Mathematical Sciences, Bentley University, 175 Forest St., Waltham, MA, 02452, USA.

Uyen NguyenDepartment of Industrial and Management Systems Engineering, University of South Florida, 4202 E. Fowler Avenue, Tampa, FL, 33620, USA.

Yusuf AkbulutDepartment of Industrial and Manufacturing Systems Engineering, North Dakota State University, 1315 Centennial Boulevard, Fargo, ND, 58102, USA.

Thuan TranDepartment of Industrial and Management Systems Engineering, University of South Florida, 4202 E. Fowler Avenue, Tampa, FL, 33620, USA.

Toan VoDepartment of Industrial and Management Systems Engineering, University of South Florida, 4202 E. Fowler Avenue, Tampa, FL, 33620, USA.

Harun PirimDepartment of Industrial and Manufacturing Systems Engineering, North Dakota State University, 1315 Centennial Boulevard, Fargo, ND, 58102, USA. harun.pirim@ndsu.edu.

Arveity SettySanford Health, University of North Dakota, 5225 23rd Ave S, Fargo, ND, 58104, USA.

Trung LeDepartment of Industrial and Management Systems Engineering, University of South Florida, 4202 E. Fowler Avenue, Tampa, FL, 33620, USA. tqle@usf.edu.ORCID http://orcid.org/0000-0002-4169-9941

Funding

National Institute of General Medical Sciences of the National Institutes of Health U54GM128729

6 · The paper itself

Abstract

introductionObstructive Sleep Apnea (OSA) is a highly prevelance sleep breathing disorder that imposes, significant public health and economic burdens through its untreated associated comorbidities. The gold standard for diagnosing OSA, polysomnography (PSG), retains several limitations because it is cumbersome to conduct in the sleep lab and requires labor-intensive efforts to annotate the measurement. Furthermore, current research focuses on the alternative of PSG on the general OSA population without specifically considering the OSA comorbid conditions, such as cardiovascular and cancer diseases. There is a need for methods to monitor OSA using features derived from consumer sleep technologies (CSTs), considering comorbid conditions associated with OSA. METHODOLOGY: In this paper, we identify the features that can be collected from CSTs to predict the patient's Apnea-Hypopnea Index (AHI) with the consideration of the comorbid chronic diseases, including cardiovascular disease and cancer. These sleep characteristics include the total recording time, the sleep period time, total sleep time, the onset of sleep, the efficiency of sleep, the wakefulness after the onset of sleep, the percentages of N1, N2, N3, REM sleep stages, the latency of REM from sleep onset and the latency of REM from lights off.

resultsBased on these features, we build stepwise regression, LASSO, and XGBoost models to evaluate the risk of a patient having OSA and classify individuals as normal versus OSA, achieving classification accuracies of 68.76%, 69.26%, and 70.4%, respectively. The proposed method has been validated based on the Wisconsin Sleep Cohort Database. To test the generalization of the method, the model was validated on the IRB-approved Sanford Obstructive Sleep Apnea Cancer dataset. XGBoost demonstrated superior performance with the highest Recall (0.9136) and F1 Score (0.7647), while regression and Lasso excel in interpreting the features with the Precision (0.865).

conclusionSuch performance highlights their effectiveness in identifying OSA cases with comorbidity. Our proposed solutions introduce better interpretability of algorithms and improve the accuracy of OSA detection using CSTs while addressing the limitations of PSG.

Indexed as

Cardiovascular DiseasesNeoplasmsSleep Apnea, ObstructiveWearable Electronic DevicesAdultComorbidityFemaleHumansMaleMiddle AgedPolysomnographyClassificationConsumer sleep technologiesMachine learningRegression models

Identifiers

PMID41653357

DOI10.1371/journal.pone.0011356

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