Noninvasive detection of diabetes in obstructive sleep apnea based on overnight SpO signal and deep learning.
Journal:
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
PMID:
40039742
Abstract
The prevalence of obstructive sleep apnea comorbid with diabetes is high while the awareness of diabetes is low. There is a strong need for new diagnostic biomarkers to detect diabetes at an early stage. Therefore, we aimed to establish an automatic, deep-learning based model that could be applied to assess diabetes risks using overnight SpO signals. The samples were derived from the Sleep Heart Health Study including 5,021 middle-aged and older adults (6.9% diabetes). The deep-learning models were established to identify diabetes solely from SpO or in combination with clinical factors (gender, age, and BMI). Class Activation Map (CAM) was utilized to determine the models' effects. By adding SpO to clinical factors, the prediction performance was significantly improved from 0.646 ± 0.011 to 0.751 ± 0.006 in terms of the area under the receiver operator characteristic (AUC) after 10-fold cross-validation. CAM results showed significant subsequences for the classification decision were the hypoxic events. The findings suggest that the SpO signals could provide substantial information. The deep learning model could be used to evaluate diabetes risks, which is beneficial for long-term health management monitoring.