IEEE J Biomed Health Inform. 2018 Mar;22(2):375-385. doi: 10.1109/JBHI.2017.2668993. Epub 2017 Feb 17.
Automatic identification of sleep stage is an important step in a sleep study. In this paper, we propose a hybrid automatic sleep stage scoring approach, named HyCLASSS, based on single channel electroencephalogram (EEG). HyCLASSS, for the first time, leverages both signal and stage transition features of human sleep for automatic identification of sleep stages. HyCLASSS consists of two parts: A random forest classifier and correction rules. Random forest classifier is trained using 30 EEG signal features, including temporal, frequency, and nonlinear features. The correction rules are constructed based on stage transition feature, importing the continuity property of sleep, and characteristic of sleep stage transition. Compared with the gold standard of manual scoring using Rechtschaffen and Kales criterion, the overall accuracy and kappa coefficient applied on 198 subjects has reached 85.95% and 0.8046 in our experiment, respectively. The performance of HyCLASS compared favorably to previous work, and it could be integrated with sleep evaluation or sleep diagnosis system in the future.
自动睡眠阶段识别是睡眠研究中的重要步骤。本文提出了一种基于单通道脑电图（EEG）的混合自动睡眠阶段评分方法，称为 HyCLASSS。HyCLASSS 首次利用人类睡眠的信号和阶段转换特征来自动识别睡眠阶段。HyCLASSS 由两部分组成：随机森林分类器和校正规则。随机森林分类器使用 30 个 EEG 信号特征进行训练，包括时间、频率和非线性特征。校正规则基于阶段转换特征构建，引入了睡眠的连续性特性和睡眠阶段转换的特征。与使用 Rechtschaffen 和 Kales 标准的手动评分的金标准相比，在 198 名受试者上的整体准确性和kappa 系数分别达到了 85.95%和 0.8046。HyCLASS 的性能优于先前的工作，并且将来可以与睡眠评估或睡眠诊断系统集成。
