Assessment of simulated snoring sounds with artificial intelligence for the diagnosis of obstructive sleep apnea.

Abstract

Background: Performing simulated snoring (SS) is a commonly used method to evaluate the source of snoring in obstructive sleep apnea (OSA). SS sounds is considered as a potential biomarker for OSA. SS sounds can be easily recorded, which is a cost-effective method for prescreening purposes.

Objective: This study aimed to validate the performance of artificial intelligence (AI) models using SS sounds for OSA diagnosis.

Methods: All participants underwent full-night polysomnography (PSG) monitoring at the sleep center. SS sounds of the participants were recorded during the laryngoscopy procedure. The audio data were processed via Python, and relevant features were extracted. OSA diagnostic models were developed using three machine learning (ML) algorithms and one deep learning (DL) algorithm. The diagnostic performance was evaluated by multiple indicators.

Results: A total of 465 participants were included. For the support vector machine algorithm, the accuracy values at apnea-hypopnea index (AHI) levels of 5, 15, and 30 per hour were 0.914, 0.887, and 0.807, respectively. For the K-nearest neighbor algorithm, the accuracy values at AHI levels of 5, 15, and 30 per hour were 0.896, 0.872, and 0.756, respectively. For the random forest algorithm, the accuracy values at AHI levels of 5, 15, and 30 per hour were 0.905, 0.881, and 0.804, respectively. For the audio spectrogram transformer algorithm, the accuracy values at AHI levels of 5, 15, and 30 per hour were 0.926, 0.887, and 0.830, respectively.

Conclusions: Our study demonstrates that DL models can effectively screen and identify OSA with commendable performance. In addition, the identification ability of the DL models was better than that of any of the ML models.