Deep learning-based multi-level airway collapse site localization on obstructive sleep apnea patients based on snoring sounds

2026-10-21 · ArXiv

arXiv:2408.16030v3 Announce Type: replace-cross

Abstract: Objective This study evaluated deep learning models, specifically ResNet-50 and Audio Spectrogram Transformer (AST), to identify multi-level upper airway collapses in obstructive sleep apnea (OSA) patients using snoring sounds. Methods Snoring recordings from 37 subjects undergoing drug-induced sleep endoscopy (DISE) between 2020 and 2021 were used to train models for two multi-label tasks: identifying obstructions at the Velum (V), Oropharynx (O), Tongue-base (T), and Epiglottis (E) levels, and the retropalatal (RP) vs. retroglossal (RG) levels. Results The AST model significantly outperformed ResNet-50 (P

原文链接：https://arxiv.org/abs/2408.16030

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