Christensen Julie A E, Zoetmulder Marielle, Koch Henriette, Frandsen Rune, Arvastson Lars, Christensen Søren R, Jennum Poul, Sorensen Helge B D
Department of Electrical Engineering, Technical University of Denmark, Orsteds Plads, Building 349, DK-2800 Kongens Lyngby, Denmark; Danish Center for Sleep Medicine, University of Copenhagen, Department of Clinical Neurophysiology, Glostrup Hospital, Entrance 5, Nordre Ringvej 57, DK-2600 Glostrup, Denmark; H. Lundbeck A/S, Ottiliavej 9, DK-2500 Valby, Denmark.
Danish Center for Sleep Medicine, University of Copenhagen, Department of Clinical Neurophysiology, Glostrup Hospital, Entrance 5, Nordre Ringvej 57, DK-2600 Glostrup, Denmark; Department of Neurology, Bispebjerg Hospital, Bispebjerg Bakke 23, DK-2400 Copenhagen, Denmark.
J Neurosci Methods. 2014 Sep 30;235:262-76. doi: 10.1016/j.jneumeth.2014.07.014. Epub 2014 Aug 1.
Manual scoring of sleep relies on identifying certain characteristics in polysomnograph (PSG) signals. However, these characteristics are disrupted in patients with neurodegenerative diseases.
This study evaluates sleep using a topic modeling and unsupervised learning approach to identify sleep topics directly from electroencephalography (EEG) and electrooculography (EOG). PSG data from control subjects were used to develop an EOG and an EEG topic model. The models were applied to PSG data from 23 control subjects, 25 patients with periodic leg movements (PLMs), 31 patients with idiopathic REM sleep behavior disorder (iRBD) and 36 patients with Parkinson's disease (PD). The data were divided into training and validation datasets and features reflecting EEG and EOG characteristics based on topics were computed. The most discriminative feature subset for separating iRBD/PD and PLM/controls was estimated using a Lasso-regularized regression model.
The features with highest discriminability were the number and stability of EEG topics linked to REM and N3, respectively. Validation of the model indicated a sensitivity of 91.4% and a specificity of 68.8% when classifying iRBD/PD patients.
The topics showed visual accordance with the manually scored sleep stages, and the features revealed sleep characteristics containing information indicative of neurodegeneration.
This study suggests that the amount of N3 and the ability to maintain NREM and REM sleep have potential as early PD biomarkers. Data-driven analysis of sleep may contribute to the evaluation of neurodegenerative patients.
睡眠的人工评分依赖于在多导睡眠图（PSG）信号中识别某些特征。然而，这些特征在神经退行性疾病患者中会受到干扰。
本研究使用主题建模和无监督学习方法评估睡眠，以直接从脑电图（EEG）和眼电图（EOG）中识别睡眠主题。来自对照受试者的PSG数据用于开发EOG和EEG主题模型。这些模型被应用于23名对照受试者、25名周期性腿部运动（PLM）患者、31名特发性快速眼动睡眠行为障碍（iRBD）患者和36名帕金森病（PD）患者的PSG数据。数据被分为训练和验证数据集，并计算基于主题反映EEG和EOG特征的特征。使用套索正则化回归模型估计用于区分iRBD/PD与PLM/对照的最具判别力的特征子集。
具有最高判别力的特征分别是与快速眼动（REM）和N3相关的EEG主题的数量和稳定性。模型验证表明，在对iRBD/PD患者进行分类时，敏感性为91.4%，特异性为68.8%。
这些主题在视觉上与人工评分的睡眠阶段一致，并且这些特征揭示了包含神经退行性变指示信息的睡眠特征。
本研究表明，N3的量以及维持非快速眼动（NREM）和快速眼动睡眠的能力有潜力作为早期帕金森病的生物标志物。基于数据驱动的睡眠分析可能有助于对神经退行性疾病患者的评估。
