基于机器学习的阻塞性睡眠呼吸暂停筛查：利用摄影颅面特征与语音衍生声学特征

文献信息

中文摘要

背景： 阻塞性睡眠呼吸暂停（OSA）是一种常见疾病，可及的非侵入性筛查方法仍受到关注。本研究评估了摄影颅面描述符和语音衍生声学特征用于呼吸暂停低通气指数（AHI）估计和二元OSA筛查的价值。方法：使用未分段和时间分段语音表示对109名成年人的数据进行分析。采用嵌套交叉验证评估了七种颅面、声学及联合特征配置。结果：对于回归，使用分段连续语音特征结合随机森林的最低MAE为14.242事件/小时。对于分类，使用未分段颅面特征结合多层感知器分类器的最高AUC-ROC为0.766，而最佳分段结果为0.756，使用颅面加连续语音特征。分段表示中仅颅面特征达到相似的AUC-ROC 0.753，表明添加声学特征没有明显的判别优势。若干分类器显示高敏感性但特异性相对较低。排除AHI最高四名参与者的敏感性分析显示相似的分类模式，尽管回归性能对队列组成更敏感。结论：总体而言，颅面描述符提供了最一致的预测贡献，而声学特征对OSA筛查的价值取决于模型和表示方式。

英文摘要

Obstructive sleep apnea (OSA) is a prevalent disorder for which accessible non-invasive screening approaches remain of interest. This study evaluated photographic craniofacial descriptors and speech-derived acoustic features for Apnea-Hypopnea Index (AHI) estimation and binary OSA screening. Data from 109 adults were analyzed using unsegmented and temporally segmented speech representations. Seven craniofacial, acoustic, and combined feature configurations were evaluated using nested cross-validation. For regression, the lowest MAE was 14.242 events/h using segmented continuous-speech features with Random Forest. For classification, the highest AUC-ROC was 0.766 using unsegmented craniofacial features with a multilayer perceptron classifier, while the best segmented result was 0.756 using craniofacial plus continuous-speech features. Craniofacial-only features in the segmented representation achieved a similar AUC-ROC of 0.753, indicating no clear discriminative advantage from adding acoustic features. Several classifiers showed high sensitivity but comparatively low specificity. Sensitivity analysis excluding the four participants with the highest AHI values showed similar classification patterns, although regression performance was more sensitive to cohort composition. Overall, craniofacial descriptors provided the most consistent predictive contribution, whereas acoustic features showed model- and representation-dependent value for OSA screening.