Department of Applied Physics, University of Eastern Finland, Kuopio, Finland.
Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland.
Sleep. 2021 Oct 11;44(10). doi: 10.1093/sleep/zsab142.
To assess the relationship between obstructive sleep apnea (OSA) severity and sleep fragmentation, accurate differentiation between sleep and wakefulness is needed. Sleep staging is usually performed manually using electroencephalography (EEG). This is time-consuming due to complexity of EEG setup and the amount of work in manual scoring. In this study, we aimed to develop an automated deep learning-based solution to assess OSA-related sleep fragmentation based on photoplethysmography (PPG) signal.
A combination of convolutional and recurrent neural networks was used for PPG-based sleep staging. The models were trained using two large clinical datasets from Israel (n = 2149) and Australia (n = 877) and tested separately on three-class (wake/NREM/REM), four-class (wake/N1 + N2/N3/REM), and five-class (wake/N1/N2/N3/REM) classification. The relationship between OSA severity categories and sleep fragmentation was assessed using survival analysis of mean continuous sleep. Overlapping PPG epochs were applied to artificially obtain denser hypnograms for better identification of fragmented sleep.
Automatic PPG-based sleep staging achieved an accuracy of 83.3% on three-class, 74.1% on four-class, and 68.7% on five-class models. The hazard ratios for decreased mean continuous sleep compared to the non-OSA group obtained with Cox proportional hazards models with 5-s epoch-to-epoch intervals were 1.70, 3.30, and 8.11 for mild, moderate, and severe OSA, respectively. With EEG-based hypnograms scored manually with conventional 30-s epoch-to-epoch intervals, the corresponding hazard ratios were 1.18, 1.78, and 2.90.
PPG-based automatic sleep staging can be used to differentiate between OSA severity categories based on sleep continuity. The differences between the OSA severity categories become more apparent when a shorter epoch-to-epoch interval is used.
评估阻塞性睡眠呼吸暂停（OSA）严重程度与睡眠碎片化之间的关系，需要准确地区分睡眠和清醒状态。睡眠分期通常使用脑电图（EEG）进行手动操作。由于 EEG 设备的复杂性和手动评分的工作量，这是一项耗时的工作。在这项研究中，我们旨在开发一种基于光电容积脉搏波（PPG）信号的自动深度学习解决方案，以评估与 OSA 相关的睡眠碎片化。
使用卷积和递归神经网络的组合进行基于 PPG 的睡眠分期。模型使用来自以色列（n=2149）和澳大利亚（n=877）的两个大型临床数据集进行训练，并分别在三类（清醒/NREM/REM）、四类（清醒/N1+N2/N3/REM）和五类（清醒/N1/N2/N3/REM）分类中进行测试。使用平均连续睡眠的生存分析评估 OSA 严重程度类别与睡眠碎片化之间的关系。应用重叠的 PPG 时段，人为地获得更密集的睡眠图，以便更好地识别碎片化睡眠。
基于 PPG 的自动睡眠分期在三类、四类和五类模型中的准确率分别为 83.3%、74.1%和 68.7%。使用 Cox 比例风险模型，在 5 秒的时-时间隔，与非 OSA 组相比，平均连续睡眠时间减少的风险比分别为轻度、中度和重度 OSA 的 1.70、3.30 和 8.11。使用 EEG 基于的睡眠图，手动以常规的 30 秒时-时间隔评分，相应的风险比分别为 1.18、1.78 和 2.90。
基于 PPG 的自动睡眠分期可用于根据睡眠连续性区分 OSA 严重程度类别。当使用更短的时-时间隔时，OSA 严重程度类别的差异变得更加明显。
