Boudabous Safa, Millet Juliette, Bacry Emmanuel
CEREMADE, CNRS-UMR 7534, Université Paris-Dauphine PSL, 75016 Paris, France.
Mitral, Apneal, 75013 Paris, France.
Diagnostics (Basel). 2024 Sep 19;14(18):2077. doi: 10.3390/diagnostics14182077.
Accurately detecting arousal events during sleep is essential for evaluating sleep quality and diagnosing sleep disorders, such as sleep apnea/hypopnea syndrome. While the American Academy of Sleep Medicine guidelines associate arousal events with electroencephalogram (EEG) signal variations, EEGs are often not recorded during home sleep testing (HST) using wearable devices or smartphone applications. The primary objective of this study was to explore the potential of alternatively relying on combinations of easily measurable physiological signals during HST for arousal detection where EEGs are not recorded. We conducted a data-driven retrospective study following an incremental device-agnostic analysis approach, where we simulated a limited-channel setting using polysomnography data and used deep learning to automate the detection task. During the analysis, we tested multiple signal combinations to evaluate their potential effectiveness. We trained and evaluated the model on the Multi-Ethnic Study of Atherosclerosis dataset. The results demonstrated that combining multiple signals significantly improved performance compared with single-input signal models. Notably, combining thoracic effort, heart rate, and a wake/sleep indicator signal achieved competitive performance compared with the state-of-the-art DeepCAD model using electrocardiogram as input with an average precision of 61.59% and an average recall of 56.46% across the test records. This study demonstrated the potential of combining easy-to-record HST signals to characterize the autonomic markers of arousal better. It provides valuable insights to HST device designers on signals that improve EEG-free arousal detection.
准确检测睡眠期间的觉醒事件对于评估睡眠质量和诊断睡眠障碍（如睡眠呼吸暂停/低通气综合征）至关重要。虽然美国睡眠医学学会的指南将觉醒事件与脑电图（EEG）信号变化相关联，但在使用可穿戴设备或智能手机应用程序进行家庭睡眠测试（HST）期间，通常不会记录EEG。本研究的主要目的是探索在未记录EEG的HST期间，转而依靠易于测量的生理信号组合进行觉醒检测的潜力。我们采用增量式设备无关分析方法进行了一项数据驱动的回顾性研究，其中我们使用多导睡眠图数据模拟有限通道设置，并使用深度学习实现检测任务的自动化。在分析过程中，我们测试了多种信号组合以评估其潜在有效性。我们在动脉粥样硬化多民族研究数据集上对模型进行了训练和评估。结果表明，与单输入信号模型相比，组合多种信号可显著提高性能。值得注意的是，将胸段用力、心率和清醒/睡眠指示信号相结合，与以心电图为输入的最先进的DeepCAD模型相比，具有竞争力的性能，在整个测试记录中平均精度为61.59%，平均召回率为56.46%。这项研究证明了组合易于记录的HST信号以更好地表征觉醒自主标志物的潜力。它为HST设备设计者提供了关于改善无EEG觉醒检测的信号的宝贵见解。
