论文标题
使用光谱时期睡眠功能的端到端自动睡眠阶段分类
End-to-End Automatic Sleep Stage Classification Using Spectral-Temporal Sleep Features
论文作者
论文摘要
睡眠障碍是许多可能影响日常生活质量的神经系统疾病之一。手动对睡眠阶段进行分类以检测睡眠障碍非常繁重。因此，需要自动睡眠阶段分类技术。但是，使用原始信号的先前自动睡眠评分方法仍然是低分类性能。在这项研究中，我们提出了一个使用Sleep-EDF数据集的最佳频谱时间睡眠特征的端到端自动睡眠分级框架。使用带通滤波器修改输入数据，然后应用于卷积神经网络模型。对于五个睡眠阶段分类，分类性能分别使用原始输入数据和提议的输入分别为85.6％和91.1％。与使用相同数据集的常规研究相比，该结果还显示出最高的性能。提出的框架通过使用与每个睡眠阶段相关的最佳功能显示出高性能，这可能有助于在自动睡眠阶段方法中找到新功能。
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.
