Kim Hyeong-Jin, Lee Minji, Lee Seong-Whan
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:3452-3455. doi: 10.1109/EMBC44109.2020.9176477.
Sleep disorder is one of many neurological diseases that can affect greatly the quality of daily life. It is very burdensome to manually classify the sleep stages to detect sleep disorders. Therefore, the automatic sleep stage classification techniques are needed. However, the previous automatic sleep scoring methods using raw signals are still low classification performance. In this study, we proposed an end-to-end automatic sleep staging framework based on optimal spectral-temporal sleep features using a sleep-edf dataset. The input data were modified using a bandpass filter and then applied to a convolutional neural network model. For five sleep stage classification, the classification performance 85.6% and 91.1% using the raw input data and the proposed input, respectively. This result also shows the highest performance compared to conventional studies using the same dataset. The proposed framework has shown high performance by using optimal features associated with each sleep stage, which may help to find new features in the automatic sleep stage method.Clinical Relevance- The proposed framework would help to diagnose sleep disorders such as insomnia by improving sleep stage classification performance.
睡眠障碍是众多会严重影响日常生活质量的神经疾病之一。手动分类睡眠阶段以检测睡眠障碍非常繁琐。因此，需要自动睡眠阶段分类技术。然而，以前使用原始信号的自动睡眠评分方法的分类性能仍然很低。在本研究中，我们使用睡眠-EDF数据集，提出了一种基于最优频谱-时间睡眠特征的端到端自动睡眠分期框架。输入数据使用带通滤波器进行修改，然后应用于卷积神经网络模型。对于五个睡眠阶段的分类，使用原始输入数据和所提出的输入时，分类性能分别为85.6%和91.1%。与使用相同数据集的传统研究相比，该结果也显示出最高的性能。所提出的框架通过使用与每个睡眠阶段相关的最优特征表现出了高性能，这可能有助于在自动睡眠阶段方法中找到新的特征。临床相关性——所提出的框架将通过提高睡眠阶段分类性能来帮助诊断失眠等睡眠障碍。
