Abstract: Sleep stage classification and sleep apnea-hypopnea detection are two key tasks for comprehensive assessment of sleep quality. However, the researchers implement the two tasks separately, without considering the intercorrelation between them. In fact, sleep stage and sleep apnea-hypopnea are associated. Sleep apnea-hypopnea syndrome is a kind of sleep fragmentation disorder which causes frequent changes in sleep stages and disrupts the normal sleep cycle. Meanwhile, sleep stage also affects the severity and duration of apnea and hypopnea. Considering the interrelationship, this paper proposes a multi-task framework for sleep staging and sleep apnea-hypopnea detection based on single-channel electroencephalogram (EEG) and single-lead electrocardiogram (ECG). The model utilizes a two-stream time-dependency learning module to extract the shared EEG and ECG features. An adaptive inter-task relevance learning module is used to capture the correlationship between sleep stage and sleep apnea-hypopnea and take advantage of the interaction to improve the performance. The performance of the proposed method was evaluated on two public datasets. The results show that the model can achieve the two tasks at the same time, with comparable performance on sleep staging and better performance of sleep apnea-hypopnea detection compared with the baseline methods. Especially, the proposed framework can detect more apneas and hypopneas, which is of great importance for primary screening of sleep apnea-hypopnea syndrome.

Key words: Keywords: sleep stage classification, sleep apnea-hypopnea detection, electroencephalogram, electrocardiogram, deep learning, multi-task learning

摘要： 睡眠分期与睡眠呼吸暂停-低通气检测是全面评估睡眠质量的两个关键任务。然而，现有方法分别完成这两项任务，未考虑二者之间的相关性。实际上，睡眠阶段和睡眠呼吸暂停-低通气是相互作用的。睡眠呼吸暂停-低通气是一种睡眠片段化疾病，会导致睡眠阶段频繁变化，扰乱正常的睡眠周期；睡眠分期也会影响睡眠呼吸暂停-低通气的严重程度和持续时间。针对该相关性，提出了一种多任务学习框架，基于单导脑电与单导心电进行睡眠分期和睡眠呼吸暂停-低通气检测。在模型方面，构造双流时间依赖学习模块，提取共享特征，设计自适应任务间关联性学习模块，建模任务间相关性，利用睡眠分期结果辅助睡眠呼吸暂停检测。在两个公开数据集上评估了所提方法的性能，实验结果表明，该模型可以同时完成睡眠分期与睡眠呼吸暂停-低通气检测，在睡眠分期任务上性能与基准方法持平，在睡眠呼吸暂停-低通气检测方面，所提方法能检测到更多的患病片段，对睡眠疾病初筛与睡眠质量评估有重要意义。

关键词: 睡眠分期, 睡眠呼吸暂停-低通气检测, 脑电图, 心电图, 深度学习, 多任务学习