The sound of snoring: AI-based acoustic phenotyping of upper airway obstruction in Obstructive Sleep Apnea

Background: Drug-Induced Sleep Endoscopy (DISE) is the current reference standard for identifying the anatomical site of upper airway obstruction in obstructive sleep apnea (OSA), but its invasiveness and limited availability restrict its routine use. Because snoring is generated by vibration of the obstructing upper-airway structures, its acoustic characteristics may provide a non-invasive biomarker of the anatomical site of obstruction. This study investigated whether machine learning could reliably distinguish palatal from epiglottic snoring using acoustic information alone. Methods: A retrospective analysis was performed on 159 DISE recordings obtained from adult patients with moderate, non-positional OSA. Snoring events were independently identified and anatomically classified by two blinded expert examiners. Only events with complete inter-observer agreement were included. The corresponding audio segments were extracted and characterized using spectral, cepstral, temporal and harmonic acoustic features. Feature selection was performed using recursive feature elimination. Support Vector Machine (SVM) and Multilayer Perceptron (MLP) classifiers were developed using a patient-independent nested cross-validation framework. Results: The final dataset comprised 1,759 snoring events, including 1,054 palatal and 705 epiglottic recordings. Both classifiers demonstrated robust discrimination between the two anatomical classes under patient-independent validation, achieving ROC AUC values of 0.90 +/- 0.03 (SVM) and 0.90 +/- 0.03 (MLP), with balanced accuracies of 0.83 and 0.81, respectively. The most informative predictors were spectral features, particularly spectral energy distribution, spectral flux, Mel-Frequency Cepstral Coefficients (MFCCs), and spectral flatness, whereas fundamental frequency contributed minimally to classification. Conclusions: Snoring contains reproducible acoustic information reflecting the anatomical origin of upper-airway obstruction. By combining high-confidence DISE-derived anatomical labels with a rigorous machine learning framework, this study demonstrates the feasibility of non-invasive acoustic phenotyping of clinically relevant obstruction sites. Rather than proposing a novel artificial intelligence algorithm, our work establishes a clinically oriented framework that may support future decision-support tools for patient selection, treatment planning, and multicentre development of comprehensive acoustic phenotyping models.