Prediction of Obstructive Sleep Apnea Based on Respiratory Sounds Recorded Between Sleep Onset and Sleep Offset

Abstract

Objectives: To develop a simple algorithm for prescreening of obstructive sleep apnea (OSA) on the basis of respiratory sounds recorded during polysomnography during all sleep stages between sleep onset and offset.

Methods: Patients who underwent attended, in-laboratory, full-night polysomnography were included. For all patients, audio recordings were performed with an air-conduction microphone during polysomnography. Analyses included all sleep stages (i.e., N1, N2, N3, rapid eye movement, and waking). After noise reduction preprocessing, data were segmented into 5-s windows and sound features were extracted. Prediction models were established and validated with 10-fold cross-validation by using simple logistic regression. Binary classifications were separately conducted for three different threshold criteria at apnea hypopnea index (AHI) of 5, 15, or 30. Prediction model characteristics, including accuracy, sensitivity, specificity, positive predictive value (precision), negative predictive value, and area under the curve (AUC) of the receiver operating characteristic were computed.

Results: A total of 116 subjects were included; their mean age, body mass index, and AHI were 50.4 years, 25.5 kg/m2 , and 23.0/hr, respectively. A total of 508 sound features were extracted from respiratory sounds recorded throughout sleep. Accuracies of binary classifiers at AHIs of 5, 15, and 30 were 82.7%, 84.4%, and 85.3%, respectively. Prediction performances for the classifiers at AHIs of 5, 15, and 30 were AUC, 0.83, 0.901, and 0.91; sensitivity, 87.5%, 81.6%, and 60%; and specificity, 67.8%, 87.5%, and 94.1%. Respective precision values of the classifiers were 89.5%, 87.5%, and 78.2% for AHIs of 5, 15, and 30.

Conclusion: This study showed that our binary classifier predicted patients with AHI of ≥15 with sensitivity and specificity of >80% by using respiratory sounds during sleep. Since our prediction model included all sleep stage data, algorithms based on respiratory sounds may have a high value for prescreening OSA with mobile devices.

Keywords: Machine Learning; Obstructive Sleep Apnea; Polysomnography; Respiratory Sounds.

Fig. 1.

A bed for polysomnography and a microphone (inset) on the ceiling.

Fig. 2.

Study framework. Sound data were acquired, followed by noise cancelling, and feature selection. From these inputs and with labeled result from the polysomnography of the same patient, machine learning had been performed. OSA, obstructive sleep apnea; PPV, positive predictive value; NPV, negative predictive value.

Fig. 3.

Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of binary classifiers at apnea hypopnea index (AHI) of 5, 15, and 30 for prescreening of obstructive sleep apnea.