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Detection of activation phases and quantification of coupling in NREM sleep EEG by pointwise transinformation.

机译:通过逐点转换信息检测NREM睡眠脑电图的激活阶段并进行耦合定量。

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BACKGROUND: The coupling dynamics of two time series can be assessed by pointwise transinformation (PTI). Due to its high temporal resolution, this algorithm is ideal for analysis of sleep microstructure. Different types of electroencephalographic (EEG) activation phases, like single K-complexes, K-complexes associated with spindle or alpha activity, K-complexes mixed with delta waves, and arousals, can be detected and changes in EEG coupling can be quantified. METHODS: Nine hundred ninety-one one-minute EEG segments (C3-A2/C4-A1) containing the described types of activation phases were selected from the sleep EEGs of 12 healthy persons. PTI was calculated with 250 Hz resolution and an embedding dimension of 20. An averaged PTI curve was assessed for single K-complexes and K-complexes followed by spindle and alpha activity, respectively. RESULTS: During background activity, PTI was nearly 0. With the onset of a K-complex, PTI increased significantly in a sequence of distinct phases (rising - peak - decay). For single K-complexes, the PTI curve had a nearly symmetric dome-shaped form. The decay phase was prolonged by subsequent spindle or alpha activity. In K-complexes mixed with delta activity and in arousals, repetitive maxima of PTI were obtained. The durations of arousals and their coupling phases were correlated (r=0.83). CONCLUSIONS: PTI displays the coupling dynamics of the sleep EEG with high resolution. It detects phases of activation represented by single K-complexes and various types of arousals. These induce a specific run of the PTI curve clearly distinguishable from background activity. PTI might, therefore, prove useful in the analysis of sleep microstructure.
机译:背景:两个时间序列的耦合动力学可以通过逐点转换信息(PTI)进行评估。由于其高时间分辨率,该算法非常适合分析睡眠微观结构。可以检测到不同类型的脑电图(EEG)激活阶段,例如单个K络合物,与纺锤或α活性相关的K络合物,与δ波混合的K络合物以及觉醒,并且可以量化EEG耦合的变化。方法:从12名健康人的睡眠EEG中选择了911个1分钟的EEG片段(C3-A2 / C4-A1),其中包含描述的激活阶段类型。用250 Hz分辨率和20的嵌入维数计算PTI。分别评估单个K复合物和K复合物的平均PTI曲线,然后分别评估纺锤体和α活性。结果:在背景活动期间,PTI几乎为0。随着K络合物的发作,PTI在一系列不同的阶段(上升-峰值-衰减)中显着增加。对于单个K复合物,PTI曲线具有近似对称的圆顶形形式。衰变阶段通过随后的纺锤体或α活性而延长。在混合有δ活性的K-复合物中和唤醒时,获得PTI的重复最大值。唤醒的持续时间及其耦合阶段是相关的(r = 0.83)。结论:PTI显示高分辨率的睡眠脑电的耦合动力学。它检测由单个K络合物和各种唤醒引起的激活阶段。这些诱导了PTI曲线的特定运行,与背景活动明显不同。因此,PTI在分析睡眠微观结构方面可能很有用。

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