Respiratory care · 2026
Unsupervised Clustering Analysis of Snoring Identifies Acoustic Biomarkers for the Severity of Obstructive Sleep Apnea.
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Yang Shih-Hsing, Lan Chou-Chin, Yang Mei-Chen, Kuo Hsien-Chi, Chen Hui-Ching, Huang Yuh-Chin T, Wu Chin-Pyng
Аннотация
BACKGROUND: Obstructive sleep apnea (OSA) is a prevalent public health concern linked to cardiovascular, metabolic, and neurocognitive consequences. Although polysomnography (PSG) is the diagnostic standard, the availability can be limited with long wait time. Snoring is a common symptom of OSA, but all individuals with snoring do not have OSA. In this study, we hypothesized the characteristics of snoring may be used to detect OSA. We applied an unsupervised clustering method to classify snoring patterns and examine their correlations with OSA parameters.
METHODS: We enrolled 287 adults with snoring symptoms who underwent overnight PSG at 2 teaching hospitals in Taiwan. Snoring sounds were recorded, and the dominant frequency and loudness were analyzed. A K-means clustering model was first developed using a training cohort (n = 50) and applied to classify frequency patterns into 5 clusters. Each cluster was further divided into 2 subclusters based on a loudness threshold (70 dB). An independent validation cohort (n = 237) was used to develop an apnea-hypopnea index (AHI) prediction model and evaluate discriminative performance using receiver operating characteristic analysis.
RESULTS: Clusters 1, 2, and 5 were significantly associated with higher AHI, oxygen desaturation index, and apnea index. In contrast, clusters 3 and 4 showed little or no association. An AHI prediction model was developed using age, BMI, and significant acoustic clusters. For PSG-defined OSA thresholds of AHI ≥ 5, ≥15, and ≥30 events/h, the AUC values were 0.90, 0.83, and 0.82, respectively, with sensitivity ranging from 70.5% to 77.5% and specificity ranging from 73.7% to 100%.
CONCLUSIONS: Our study demonstrates an AI-based clustering analysis of snoring patterns can be used to evaluate OSA severity. The snoring pattern analysis can also be an additional screening tool for OSA.
METHODS: We enrolled 287 adults with snoring symptoms who underwent overnight PSG at 2 teaching hospitals in Taiwan. Snoring sounds were recorded, and the dominant frequency and loudness were analyzed. A K-means clustering model was first developed using a training cohort (n = 50) and applied to classify frequency patterns into 5 clusters. Each cluster was further divided into 2 subclusters based on a loudness threshold (70 dB). An independent validation cohort (n = 237) was used to develop an apnea-hypopnea index (AHI) prediction model and evaluate discriminative performance using receiver operating characteristic analysis.
RESULTS: Clusters 1, 2, and 5 were significantly associated with higher AHI, oxygen desaturation index, and apnea index. In contrast, clusters 3 and 4 showed little or no association. An AHI prediction model was developed using age, BMI, and significant acoustic clusters. For PSG-defined OSA thresholds of AHI ≥ 5, ≥15, and ≥30 events/h, the AUC values were 0.90, 0.83, and 0.82, respectively, with sensitivity ranging from 70.5% to 77.5% and specificity ranging from 73.7% to 100%.
CONCLUSIONS: Our study demonstrates an AI-based clustering analysis of snoring patterns can be used to evaluate OSA severity. The snoring pattern analysis can also be an additional screening tool for OSA.