Olsen Anders Vinther, Stephansen Jens, Leary Eileen, Peppard Paul E, Sheungshul Hong, Jennum Poul Jørgen, Sorensen Helge, Mignot Emmanuel
Center for Sleep Sciences and Medicine, Stanford School of Medicine, Palo Alto, CA, USA; Department of Electrical Engineering, Technical University of Denmark, Kongens Lyngby, Denmark.
Center for Sleep Sciences and Medicine, Stanford School of Medicine, Palo Alto, CA, USA; Department of Electrical Engineering, Technical University of Denmark, Kongens Lyngby, Denmark.
J Neurosci Methods. 2017 Apr 15;282:9-19. doi: 10.1016/j.jneumeth.2017.02.004. Epub 2017 Feb 20.
Type 1 narcolepsy (NT1) is characterized by symptoms believed to represent Rapid Eye Movement (REM) sleep stage dissociations, occurrences where features of wake and REM sleep are intermingled, resulting in a mixed state. We hypothesized that sleep stage dissociations can be objectively detected through the analysis of nocturnal Polysomnography (PSG) data, and that those affecting REM sleep can be used as a diagnostic feature for narcolepsy.
A Linear Discriminant Analysis (LDA) model using 38 features extracted from EOG, EMG and EEG was used in control subjects to select features differentiating wake, stage N1, N2, N3 and REM sleep. Sleep stage differentiation was next represented in a 2D projection. Features characteristic of sleep stage differences were estimated from the residual sleep stage probability in the 2D space. Using this model we evaluated PSG data from NT1 and non-narcoleptic subjects. An LDA classifier was used to determine the best separation plane.
This method replicates the specificity/sensitivity from the training set to the validation set better than many other methods.
Eight prominent features could differentiate narcolepsy and controls in the validation dataset. Using a composite measure and a specificity cut off 95% in the training dataset, sensitivity was 43%. Specificity/sensitivity was 94%/38% in the validation set. Using hypersomnia subjects, specificity/sensitivity was 84%/15%. Analyzing treated narcoleptics the specificity/sensitivity was 94%/10%.
Sleep stage dissociation can be used for the diagnosis of narcolepsy. However the use of some medications and presence of undiagnosed hypersomnolence patients impacts the result.
1型发作性睡病（NT1）的特征是存在被认为代表快速眼动（REM）睡眠阶段解离的症状，即清醒和REM睡眠的特征相互交织，导致一种混合状态。我们假设睡眠阶段解离可通过对夜间多导睡眠图（PSG）数据的分析进行客观检测，且那些影响REM睡眠的解离可作为发作性睡病的诊断特征。
在对照受试者中使用基于从眼电图（EOG）、肌电图（EMG）和脑电图（EEG）提取的38个特征构建的线性判别分析（LDA）模型来选择区分清醒、N1期、N2期、N3期和REM睡眠的特征。接下来，睡眠阶段区分以二维投影表示。从二维空间中的剩余睡眠阶段概率估计睡眠阶段差异的特征。使用该模型，我们评估了NT1患者和非发作性睡病受试者的PSG数据。使用LDA分类器确定最佳分离平面。
该方法将训练集的特异性/敏感性更好地复制到验证集，优于许多其他方法。
在验证数据集中，八个突出特征可区分发作性睡病和对照。在训练数据集中使用复合测量方法且特异性截止值为95%时，敏感性为43%。在验证集中，特异性/敏感性为94%/38%。对于发作性睡病伴日间过度嗜睡患者，特异性/敏感性为84%/15%。分析接受治疗的发作性睡病患者时，特异性/敏感性为94%/10%。
睡眠阶段解离可用于发作性睡病的诊断。然而，某些药物的使用以及未诊断的日间过度嗜睡患者的存在会影响结果。
