Pneumology Department, Río Hortega University Hospital, Valladolid, Spain.
Biomedical Engineering Group, University of Valladolid, Valladolid, Spain.
Sci Rep. 2020 Mar 24;10(1):5332. doi: 10.1038/s41598-020-62223-4.
The most appropriate physiological signals to develop simplified as well as accurate screening tests for obstructive sleep apnoea (OSA) remain unknown. This study aimed at assessing whether joint analysis of at-home oximetry and airflow recordings by means of machine-learning algorithms leads to a significant diagnostic performance increase compared to single-channel approaches. Consecutive patients showing moderate-to-high clinical suspicion of OSA were involved. The apnoea-hypopnoea index (AHI) from unsupervised polysomnography was the gold standard. Oximetry and airflow from at-home polysomnography were parameterised by means of 38 time, frequency, and non-linear variables. Complementarity between both signals was exhaustively inspected via automated feature selection. Regression support vector machines were used to estimate the AHI from single-channel and dual-channel approaches. A total of 239 patients successfully completed at-home polysomnography. The optimum joint model reached 0.93 (95%CI 0.90-0.95) intra-class correlation coefficient between estimated and actual AHI. Overall performance of the dual-channel approach (kappa: 0.71; 4-class accuracy: 81.3%) significantly outperformed individual oximetry (kappa: 0.61; 4-class accuracy: 75.0%) and airflow (kappa: 0.42; 4-class accuracy: 61.5%). According to our findings, oximetry alone was able to reach notably high accuracy, particularly to confirm severe cases of the disease. Nevertheless, oximetry and airflow showed high complementarity leading to a remarkable performance increase compared to single-channel approaches. Consequently, their joint analysis via machine learning enables accurate abbreviated screening of OSA at home.
最适合开发简化且准确的阻塞性睡眠呼吸暂停（OSA）筛查测试的生理信号仍不清楚。本研究旨在评估通过机器学习算法联合分析家庭血氧仪和气流记录是否与单通道方法相比具有显著的诊断性能提高。连续的患者表现出中度至高度的 OSA 临床怀疑。未监督多导睡眠图的呼吸暂停低通气指数（AHI）是金标准。家庭多导睡眠图的血氧仪和气流通过 38 个时间、频率和非线性变量进行参数化。通过自动特征选择对两种信号之间的互补性进行了详尽的检查。回归支持向量机用于从单通道和双通道方法估计 AHI。共有 239 名患者成功完成了家庭多导睡眠图。最佳联合模型在估计和实际 AHI 之间达到了 0.93（95%CI 0.90-0.95）的组内相关系数。双通道方法的整体性能（kappa：0.71；4 类准确率：81.3%）明显优于单独的血氧仪（kappa：0.61；4 类准确率：75.0%）和气流（kappa：0.42；4 类准确率：61.5%）。根据我们的发现，单独的血氧仪能够达到显著的高准确性，尤其是可以确认疾病的严重病例。然而，血氧仪和气流显示出高度的互补性，与单通道方法相比，性能显著提高。因此，通过机器学习对其进行联合分析可在家中实现准确的 OSA 简化筛查。
