Developing and Validating Ensemble Classifiers for At-Home Sleep Apnea Screening

Sleep apnea is a common sleep disorder that affects many people around the world. There is need for developing new screening method to improve the accessibility and affordability of apnea screening. In this paper, we developed ensemble classifiers with overnight blood oxygen saturation signals (SpO2) for the purpose of accurate classification between people with sleep apnea and healthy people. The ensemble classifiers (ECLF) are built on top of 5 base classifiers, including logistic regression (LR), random forest (RF), support vector machine (SVM), linear discrimination analysis (LDA), and light gradient boosting machine (LGMB). The output of the ECLF is weighted voting of each classifier. Performance evaluation analysis showed that when heavier weights were assigned to the LR and SVM classifiers, the ECLF achieved a better balance between sensitivity (0.81 ± 0.02) and specificity (0.80 ± 0.02) while maintaining the overall performance as measured by AUC (0.81 ± 0.01). RF and LDA achieved high sensitivity (> 0.95) at the sacrifice of specificity, while LR and SVM achieved high specificity (> 0.80) at the sacrifice of sensitivity. LGMB demonstrated mediocre performance on both sensitivity and specificity