Multicenter Validation of an AI-Based Snoring Model vs. Wearables for Diagnosing Sleep Apnea

Sleep apnea-hypopnea syndrome (SAHS) has high prevalence and cardiovascular/cerebrovascular comorbidities. Polysomnography (PSG) is the diagnostic gold standard but limited by complexity, cost, and accessibility; smart wearables are convenient but less accurate. This study validated an AI-based snoring-sound model and compared its diagnostic performance with a wearable, using PSG as reference.

Adults with suspected SAHS (n = 134) were included and underwent overnight PSG, with simultaneous snoring sound and wearable signal recording. The snoring algorithm used short-time Fourier transform (STFT) spectrograms and a multiscale encoder-attention-decoder neural network for classification. Model performance was assessed using correlation analysis, intraclass correlation coefficient (ICC), Bland-Altman plots, and receiver operating characteristic (ROC) analyses. Accuracy, sensitivity, specificity, and area under the curve (AUC) values were computed for both models.

Disease severity was associated with variation in BMI (p < 0.001) and decreasing oxygen saturation (p < 0.001). The snoring model showed stronger correlation and higher agreement with PSG-derived AHI (r = 0.79, ICC = 0.753) than the wearable (r = 0.68). Although the snoring model yielded consistently higher AUCs, particularly in mild SAHS (AUC = 0.83 vs. 0.72, p = 0.07), the differences between methods were not statistically significant (all p > 0.05). Both models achieved excellent discriminative performance in severe SAHS (AUC ≥ 0.90).

The AI-driven snoring-sound model demonstrated comparable overall performance and numerically better performance in certain settings, particularly in identifying mild SAHS.