仅用鼾声音频估算呼吸努力，实现无感睡眠呼吸暂停筛查。
Estimating Respiratory Effort from Nocturnal Breathing Sounds for Obstructive Sleep Apnoea Screening
- 从夜间呼吸声中直接估计呼吸努力，无需额外传感器。
- 在157个家庭夜间的数据上，相关系数达0.48，捕捉到有效呼吸动态。
- 只需手机录音，适合长期、无感的睡眠呼吸暂停监测。
阻塞性睡眠呼吸暂停（OSA）是一种常见且危害严重的疾病，但因整夜多导睡眠图检测复杂且昂贵，许多患者未被诊断。基于声音的筛查具有可扩展性优势，但受环境噪声和缺乏生理背景影响。呼吸努力是临床评分中的关键信号，现有方法依赖额外接触式传感器，降低可扩展性和舒适度。本文首次提出仅从夜间音频估计呼吸努力的方法，实现仅凭声音恢复生理信息。我们构建了潜在空间融合框架，将估计的呼吸努力嵌入与声学特征融合用于OSA检测。基于103名参与者共157个夜晚的家庭录制数据，呼吸努力估计的一致性相关系数为0.48，能有效捕捉呼吸动态。融合努力与音频特征显著提升灵敏度与AUC，尤其在低呼吸暂停低通气指数阈值下表现更优。该方法仅需测试时的手机音频，实现无传感器、可扩展、长期的OSA监测。
原文摘要 · Abstract (English)
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.
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