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.

翻译：阻塞性睡眠呼吸暂停（OSA）是一种普遍存在且对健康有重大影响的疾病，然而由于夜间多导睡眠监测的复杂性和高昂成本，许多患者未能得到诊断。基于声音的筛查提供了一种可扩展的替代方案，但其性能受限于环境噪声和生理背景信息的缺失。呼吸努力度是临床评估OSA事件的关键信号，但现有方法需要额外的接触式传感器，这降低了可扩展性和患者舒适度。本文首次提出了直接从夜间音频中估计呼吸努力度的研究，实现了仅通过声音恢复生理背景信息。我们提出了一种潜在空间融合框架，将估计的努力度嵌入与声学特征相结合用于OSA检测。使用在家庭环境中记录的103名参与者共157晚的数据集，我们的呼吸努力度估计器取得了0.48的一致性相关系数，有效捕捉了有意义的呼吸动态特征。融合努力度与音频信息相较于仅使用音频的基线方法，显著提高了检测灵敏度和AUC，尤其是在低呼吸暂停低通气指数阈值下。所提出的方法在测试阶段仅需智能手机音频，为实现无传感器、可扩展且可长期进行的OSA监测提供了可能。