AI 中文总结
该研究提出呼吸暂停负荷引导框架，结合PPG特征与SpO2，提升睡眠呼吸暂停表征效果，增强分布外泛化能力，低复杂度混合架构适用于家庭预防性监测
AI 中文摘要
睡眠呼吸暂停是一种常见的睡眠相关呼吸障碍，与显著的心血管和代谢风险相关。尽管夜间多导睡眠监测仍是诊断的参考标准，但其复杂性和成本限制了其在家庭中长期预防性监测中的适用性。在可穿戴系统中，动脉血氧饱和度（SpO2）通常被用作主要预测因子，而光体积描记（PPG）脉搏波的额外形态特征通常未被充分探索。本研究提出了一种新颖的基于呼吸暂停负荷预测的睡眠呼吸暂停严重程度评估框架，并研究了PPG特征对模型性能及分布外（OOD）泛化能力的影响。该框架首先将呼吸暂停负荷预测为连续度量，随后将其转换为临床相关的呼吸暂停低通气指数，以在分布内（ID）和分布外数据中对受试者进行四类严重程度分组。评估了三种人工神经网络架构，性能指标在五个具有不同固定随机种子的独立运行中取平均值。在分布外测试中，与仅使用SpO2相比，PPG特征与SpO2的结合使宏灵敏度提高约15.72%，宏准确率提高9.22%，宏F1分数提高16.01%，Cohen’s kappa提高11.08%，Matthews相关系数提高13.22%。低复杂度的卷积循环模型实现了最高的整体性能。结果表明，所提出的呼吸暂停负荷引导框架结合PPG特征与SpO2可改善睡眠呼吸暂停表征，在独立外部数据集上展现出令人鼓舞的分布外性能，且更简单的混合架构在稳健的家庭预防性监测中展现出强大潜力。
英文摘要
Sleep apnea is a common sleep-related breathing disorder associated with substantial cardiovascular and metabolic risk. Although overnight polysomnography remains the reference standard for diagnosis, its complexity and cost limit its suitability for long-term preventive monitoring at home. In wearable systems, arterial blood oxygen saturation (SpO2) is commonly used as the main predictor, whereas additional morphological features of the photoplethysmographic (PPG) pulse wave are usually underexplored. This study proposes a novel apnea burden prediction-based framework for sleep apnea severity assessment and investigates the influence of PPG features on model performance and out-of-distribution (OOD) generalization. The proposed framework first predicts apnea burden as a continuous measure, which is subsequently converted into the clinically relevant apnea-hypopnea index for subject-level classification into four severity groups in both in-distribution (ID) and OOD data. Three artificial neural network architectures were evaluated, and the performance metrics were averaged over five independent runs with different fixed random seeds. During OOD testing, the combination of PPG features and SpO2 led to increases of approximately 15.72% in macro-sensitivity, 9.22% in macro-accuracy, 16.01% in macro-F1-score, 11.08% in Cohen's kappa, and 13.22% in Matthews correlation coefficient, compared with using SpO2 alone. The low-complexity convolutional-recurrent models achieved the highest overall performance. The results indicated that the proposed apnea burden-guided framework, combined with PPG features and SpO2, improves sleep apnea characterization while showing encouraging OOD performance on an independent external dataset. Moreover, simpler hybrid architectures demonstrated strong potential for robust home-based preventive monitoring.
Comments16 pages, 13 figures, 4 tables, 53 references. Submitted to the IEEE Access journal
