Beijing Key Laboratory of Computational Intelligence and Intelligent System, Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China.
Beijing Laboratory of Advanced Information Networks, Beijing, 100124, China.
Med Biol Eng Comput. 2023 Sep;61(9):2291-2303. doi: 10.1007/s11517-023-02808-z. Epub 2023 Mar 31.
Sleep is crucial for human health. Automatic sleep stage classification based on polysomnogram (PSG) is meaningful for the diagnosis of sleep disorders, which has attracted extensive attention in recent years. Most existing methods could not fully consider the different transitions of sleep stages and fit the visual inspection of sleep experts simultaneously. To this end, we propose a temporal multi-scale hybrid attention network, namely TMHAN, to automatically achieve sleep staging. The temporal multi-scale mechanism incorporates short-term abrupt and long-term periodic transitions of the successive PSG epochs. Furthermore, the hybrid attention mechanism includes 1-D local attention, 2-D global attention, and 2-D contextual sparse multi-head self-attention for three kinds of sequence-level representations. The concatenated representation is subsequently fed into a softmax layer to train an end-to-end model. Experimental results on two benchmark sleep datasets show that TMHAN obtains the best performance compared with several baselines, demonstrating the effectiveness of our model. In general, our work not only provides good classification performance, but also fits the actual sleep staging processes, which makes contribution for the combination of deep learning and sleep medicine.
睡眠对人类健康至关重要。基于多导睡眠图（PSG）的自动睡眠阶段分类对睡眠障碍的诊断具有重要意义，近年来引起了广泛关注。大多数现有方法不能充分考虑睡眠阶段的不同转变，同时适应睡眠专家的视觉检查。为此，我们提出了一种时间多尺度混合注意力网络，即 TMHAN，用于自动实现睡眠分期。时间多尺度机制结合了连续 PSG 时段的短期突发和长期周期性转变。此外，混合注意力机制包括 1-D 局部注意力、2-D 全局注意力和 2-D 上下文稀疏多头自注意力，用于三种序列级表示。串联表示随后被馈送到 softmax 层中以训练端到端模型。在两个基准睡眠数据集上的实验结果表明，与几个基线相比，TMHAN 获得了最佳性能，证明了我们模型的有效性。总的来说，我们的工作不仅提供了良好的分类性能，而且还适应了实际的睡眠分期过程，这为深度学习和睡眠医学的结合做出了贡献。
