School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD, Australia.
Respirology. 2020 May;25(5):475-485. doi: 10.1111/resp.13635. Epub 2019 Jun 27.
Overnight pulse oximetry allows the relatively non-invasive estimation of peripheral blood haemoglobin oxygen saturations (SpO ), and forms part of the typical polysomnogram (PSG) for investigation of obstructive sleep apnoea (OSA). While the raw SpO signal can provide detailed information about OSA-related pathophysiology, this information is typically summarized with simple statistics such as the oxygen desaturation index (ODI, number of desaturations per hour). As such, this study reviews the technical methods for quantifying OSA-related patterns in oximetry data. The technical methods described in literature can be broadly grouped into four categories: (i) Describing the detailed characteristics of desaturations events; (ii) Time series statistics; (iii) Analysis of power spectral distribution (i.e. frequency domain analysis); and (d) Non-linear analysis. These are described and illustrated with examples of oximetry traces. The utilization of these techniques is then described in two applications. First, the application of detailed oximetry analysis allows the accurate automated classification of PSG-defined OSA. Second, quantifications which better characterize the severity of desaturation events are better predictors of OSA-related epidemiological outcomes than standard clinical metrics. Finally, methodological considerations and further applications and opportunities are considered.
整夜脉搏血氧饱和度仪可在一定程度上无创性地估算末梢血氧血红蛋白氧饱和度（SpO ），并构成阻塞性睡眠呼吸暂停（OSA）典型多导睡眠图（PSG）检查的一部分。尽管原始 SpO 信号可以提供有关 OSA 相关病理生理学的详细信息，但通常使用简单的统计信息（如每小时脱氧饱和度指数（ODI，每小时脱氧次数））来概括这些信息。因此，本研究综述了定量分析血氧仪数据中与 OSA 相关模式的技术方法。文献中描述的技术方法大致可分为四类：（i）描述脱氧事件的详细特征；（ii）时间序列统计；（iii）分析功率谱分布（即频域分析）；和（d）非线性分析。本文通过血氧仪轨迹的示例对这些技术进行了描述和说明。然后，描述了这些技术的两个应用。首先，详细的血氧分析的应用可以准确地自动分类 PSG 定义的 OSA。其次，与标准临床指标相比，更好地描述脱氧事件严重程度的量化指标可以更好地预测与 OSA 相关的流行病学结果。最后，考虑了方法学的注意事项以及进一步的应用和机会。
