Background: Sleep staging is a fundamental component in the diagnosis of sleep disorders and the management of sleep health. Traditionally, this analysis is conducted in clinical settings and involves a time-consuming scoring procedure. Recent data-driven algorithms for sleep staging, using the photoplethysmogram (PPG) time series, have shown high performance on local test sets but lower performance on external datasets due to data drift. Methods: This study aimed to develop a generalizable deep learning model for the task of four class (wake, light, deep, and rapid eye movement (REM)) sleep staging from raw PPG physiological time-series. Six sleep datasets, totaling 2,574 patients recordings, were used. In order to create a more generalizable representation, we developed and evaluated a deep learning model called SleepPPG-Net2, which employs a multi-source domain training approach.SleepPPG-Net2 was benchmarked against two state-of-the-art models. Results: SleepPPG-Net2 showed consistently higher performance over benchmark approaches, with generalization performance (Cohen's kappa) improving by up to 19%. Performance disparities were observed in relation to age, sex, and sleep apnea severity. Conclusion: SleepPPG-Net2 sets a new standard for staging sleep from raw PPG time-series.

翻译：背景：睡眠分期是睡眠障碍诊断及睡眠健康管理的基础环节。传统上，该分析在临床环境中进行，涉及耗时的评分流程。近期基于光电容积描记法（PPG）时间序列的数据驱动睡眠分期算法，在本地测试集上表现优异，但因数据漂移问题在外部数据集上性能下降。方法：本研究旨在开发一种可泛化的深度学习模型，用于从原始PPG生理时间序列进行四分类（清醒、浅睡、深睡、快速眼动（REM））睡眠分期。研究使用了六个睡眠数据集，共计2,574例患者记录。为了构建更具泛化性的表征，我们开发并评估了名为SleepPPG-Net2的深度学习模型，该模型采用多源域训练方法。将SleepPPG-Net2与两种最先进模型进行基准对比。结果：SleepPPG-Net2表现始终优于基准方法，泛化性能（Cohen's kappa）提升高达19%。在年龄、性别和睡眠呼吸暂停严重程度方面观察到性能差异。结论：SleepPPG-Net2为基于原始PPG时间序列的睡眠分期建立了新标准。