Koch Henriette, Christensen Julie A E, Frandsen Rune, Arvastson Lars, Christensen Soren R, Sorensen Helge B D, Jennum Poul
Annu Int Conf IEEE Eng Med Biol Soc. 2013;2013:4275-8. doi: 10.1109/EMBC.2013.6610490.
Sleep analysis is an important diagnostic tool for sleep disorders. However, the current manual sleep scoring is time-consuming as it is a crude discretization in time and stages. This study changes Esbroeck and Westover's [1] latent sleep staging model into a global model. The proposed data-driven method trained a topic mixture model on 10 control subjects and was applied on 10 other control subjects, 10 iRBD patients and 10 Parkinson's patients. In that way 30 topic mixture diagrams were obtained from which features reflecting distinct sleep architectures between control subjects and patients were extracted. Two features calculated on basis of two latent sleep states classified subjects as "control" or "patient" by a simple clustering algorithm. The mean sleep staging accuracy compared to classical AASM scoring was 72.4% for control subjects and a clustering of the derived features resulted in a sensitivity of 95% and a specificity of 80 %. This study demonstrates that frequency analysis of sleep EEG can be used for data-driven global sleep classification and that topic features separates iRBD and Parkinson's patients from control subjects.
睡眠分析是睡眠障碍的重要诊断工具。然而，当前的人工睡眠评分耗时，因为它是时间和阶段上的粗略离散化。本研究将埃斯布罗克和韦斯托弗[1]的潜在睡眠分期模型转变为全局模型。所提出的数据驱动方法在10名对照受试者上训练了一个主题混合模型，并应用于另外10名对照受试者、10名快速眼动行为障碍（iRBD）患者和10名帕金森病患者。通过这种方式获得了30个主题混合图，从中提取了反映对照受试者与患者之间不同睡眠结构的特征。基于两种潜在睡眠状态计算的两个特征通过简单聚类算法将受试者分类为“对照”或“患者”。与经典的美国睡眠医学学会（AASM）评分相比，对照受试者的平均睡眠分期准确率为72.4%，对导出特征进行聚类得到的敏感性为95%，特异性为80%。本研究表明，睡眠脑电图的频率分析可用于数据驱动的全局睡眠分类，且主题特征可将iRBD和帕金森病患者与对照受试者区分开来。
