Mutual Information Analysis of Sleep EEG in Detecting Psycho-Physiological Insomnia
Mutual Information Analysis of Sleep EEG in Detecting Psycho-Physiological Insomnia
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DOI：
10.1007/s10916-015-0219-1
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发表时间：
2015-05-01
影响因子：
5.3
通讯作者：
Yetkin, Sinan
中科院分区：
文献类型：
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作者：
Aydin, Serap;Tunga, M. Alper;Yetkin, Sinan
本研究的主要目的是阐明心理生理性失眠（PPI）患者在整夜睡眠期间功能性大脑连接的明显变化。第二个目标是调查互信息（MI）分析在估计皮层睡眠EEG觉醒检测PPI的有用性。为了这些目的，健康对照组和患者进行了比较，除了6个睡眠阶段的锁相量化外，还对线性（皮尔逊相关系数和相干性）和非线性量化（MI）进行了比较。1-4，rem，wake）之间的两个中枢睡眠EEG推导的半球间的依赖性。在测试中，通过估计的向量范数来识别针对每对时期（C3-A2和C4-A1）计算的每个连通性估计。然后，使用10种不同类型的数据挖掘分类器对患者和对照组进行分类，用于五个错误标准，如准确性，均方根误差，灵敏度，特异性和精密度。通过测量的分类中的高性能将验证该测量对检测PPI的高贡献。发现MI是检测PPI的最佳方法。特别是，患者在所有睡眠阶段的MI较低，PCC较高。换句话说，观察到PPI的睡眠EEG同步性较低。这些结果可能代表了神经元的损失，这些神经元然后有助于睡眠障碍中神经网络内的不太复杂的动态处理，并且功能性中枢大脑连接在夜间睡眠期间是非线性的。总之，大脑皮层半球的连接水平与睡眠障碍密切相关。因此，在所有存在的睡眠阶段量化的皮层通信可能是一个潜在的标记，由PPI引起的睡眠障碍。
The primary goal of this study is to state the clear changes in functional brain connectivity during all night sleep in psycho-physiological insomnia (PPI). The secondary goal is to investigate the usefulness of Mutual Information (MI) analysis in estimating cortical sleep EEG arousals for detection of PPI. For these purposes, healthy controls and patients were compared to each other with respect to both linear (Pearson correlation coefficient and coherence) and nonlinear quantifiers (MI) in addition to phase locking quantification for six sleep stages (stage. 1-4, rem, wake) by means of interhemispheric dependency between two central sleep EEG derivations. In test, each connectivity estimation calculated for each couple of epoches (C3-A2 and C4-A1) was identified by the vector norm of estimation. Then, patients and controls were classified by using 10 different types of data mining classifiers for five error criteria such as accuracy, root mean squared error, sensitivity, specificity and precision. High performance in a classification through a measure will validate high contribution of that measure to detecting PPI. The MI was found to be the best method in detecting PPI. In particular, the patients had lower MI, higher PCC for all sleep stages. In other words, the lower sleep EEG synchronization suffering from PPI was observed. These results probably stand for the loss of neurons that then contribute to less complex dynamical processing within the neural networks in sleep disorders an the functional central brain connectivity is nonlinear during night sleep. In conclusion, the level of cortical hemispheric connectivity is strongly associated with sleep disorder. Thus, cortical communication quantified in all existence sleep stages might be a potential marker for sleep disorder induced by PPI.