A Novel Artificial Neural Network Based Sleep-Disordered Breathing Screening Tool

Abstract

Study Objectives: This study evaluated a novel artificial neural network (ANN) based sleep disordered breathing (SDB) screening tool incorporating nocturnal pulse oximetry with demographic, anatomic and clinical data. The tool was compatible with 6 categories of apnea hypopnea index (AHI) with 4% oxyhemoglobin desaturation threshold, ≥ 5/hour, 10/hour, 15/hour, 20/hour, 25/hour, and 30/hour. Methods: Using a general population dataset, the training set included 2,280 subjects, while the test set included 470 subjects. The input of this tool was a set of 22 variables. The tool had six multilayer perceptron (MLP) neural network models for each AHI threshold. Several criteria were explored to evaluate the accuracy of the tool: area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and 95% confidence intervals (CI). Results: The AUCs were 0.904, 0.912, 0.913, 0.926, 0.930, and 0.954 respectively, with models of AHI ≥ 5/hour, 10/hour, 15/hour, 20/hour, 25/hour, and 30/hour thresholds. The sensitivities of all MLP neural network models were higher than 95%. The AHI ≥ 30/hour model had the maximum sensitivity: 98.31% (95% CI: 95.01% - 100%). Conclusions: The results of this study suggested that the ANN based SDB screening tool can be used to identify the presence or absence of SDB. Future validation should be performed in other populations to determine the practicability of this screening tool in sleep clinics and other at risk populations.