标题： 从夜间呼吸声音估计呼吸努力用于阻塞性睡眠呼吸暂停筛查 显示英文标题

标题： Estimating Respiratory Effort from Nocturnal Breathing Sounds for Obstructive Sleep Apnoea Screening

摘要： 阻塞性睡眠呼吸暂停（OSA）是一种普遍的疾病，具有显著的健康后果，但由于夜间多导睡眠图的复杂性和成本，许多患者仍未被诊断。基于声学的筛查提供了一个可扩展的替代方案，但其性能受到环境噪声和缺乏生理背景的限制。呼吸努力是临床评分中用于OSA事件的关键信号，但目前的方法需要额外的接触式传感器，这降低了可扩展性和患者的舒适度。本文首次研究了直接从夜间音频估计呼吸努力，从而仅从声音中恢复生理背景。我们提出了一种潜在空间融合框架，将估计的努力嵌入与声学特征结合用于OSA检测。使用来自103名参与者在家庭环境中记录的157个夜晚的数据集，我们的呼吸努力估计器达到了0.48的一致性相关系数，捕捉到了有意义的呼吸动态。将努力和音频融合可以提高灵敏度和AUC，特别是在低呼吸暂停-低通气指数阈值下。所提出的方法在测试时只需要智能手机音频，这使得无需传感器、可扩展且长期的OSA监测成为可能。 显示英文摘要

摘要： Obstructive sleep apnoea (OSA) is a prevalent condition with significant health consequences, yet many patients remain undiagnosed due to the complexity and cost of over-night polysomnography. Acoustic-based screening provides a scalable alternative, yet performance is limited by environmental noise and the lack of physiological context. Respiratory effort is a key signal used in clinical scoring of OSA events, but current approaches require additional contact sensors that reduce scalability and patient comfort. This paper presents the first study to estimate respiratory effort directly from nocturnal audio, enabling physiological context to be recovered from sound alone. We propose a latent-space fusion framework that integrates the estimated effort embeddings with acoustic features for OSA detection. Using a dataset of 157 nights from 103 participants recorded in home environments, our respiratory effort estimator achieves a concordance correlation coefficient of 0.48, capturing meaningful respiratory dynamics. Fusing effort and audio improves sensitivity and AUC over audio-only baselines, especially at low apnoea-hypopnoea index thresholds. The proposed approach requires only smartphone audio at test time, which enables sensor-free, scalable, and longitudinal OSA monitoring.