A New Kind of Permutation Entropy Used to Classify Sleep Stages from Invisible EEG Microstructure
A New Kind of Permutation Entropy Used to Classify Sleep Stages from Invisible EEG Microstructure
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DOI：
10.3390/e19050197
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发表时间：
2017-05-01
期刊：
影响因子：
2.7
通讯作者：
Bandt, Christoph
中科院分区：
文献类型：
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作者：
Bandt, Christoph
排列熵和顺序模式的EEG信号已被几位作者应用于研究睡眠，麻醉，癫痫缺席。在这里，我们讨论一个新版本的排列熵，这是解释为距离白色噪声。它的规模类似于众所周知的chi（2）分布，并且可以由统计模型支持。提供了显著性的临界值。到白色噪声的距离被用作测量睡眠深度的参数，其中人类EEG的警惕清醒状态被解释为“几乎白色噪声”。根据EEG数据对睡眠阶段进行分类通常依赖于Delta波和图形元素，这些元素可以在几秒的宏观尺度上看到。距离白色噪音可以预测这样的新兴波之前，他们变得明显，评估无形的趋势变化在40毫秒。30秒的高分辨率EEG的数据段提供了可靠的分类。适用于睡眠障碍的诊断。
Permutation entropy and order patterns in an EEG signal have been applied by several authors to study sleep, anesthesia, and epileptic absences. Here, we discuss a new version of permutation entropy, which is interpreted as distance to white noise. It has a scale similar to the well-known chi(2) distributions and can be supported by a statistical model. Critical values for significance are provided. Distance to white noise is used as a parameter which measures depth of sleep, where the vigilant awake state of the human EEG is interpreted as "almost white noise". Classification of sleep stages from EEG data usually relies on delta waves and graphic elements, which can be seen on a macroscale of several seconds. The distance to white noise can anticipate such emerging waves before they become apparent, evaluating invisible tendencies of variations within 40 milliseconds. Data segments of 30 s of high-resolution EEG provide a reliable classification. Application to the diagnosis of sleep disorders is indicated.