排列熵作为分析睡眠期间大脑活动的概念模型
Permutation Entropy as a Conceptual Model to Analyse Brain Activity in Sleep
AI总结：
本研究提出将排列熵作为分析睡眠大脑活动的概念模型，通过对18份脑电图数据的分析发现排列熵与睡眠阶段强相关，且深度睡眠时熵值持续下降，可用于睡眠分期，并可扩展至其他病理及手术脑活动分析。
摘要：
睡眠分期是一个被广泛讨论的主题，因其在失眠等睡眠障碍诊断中的重要性而备受关注。为了更深入地了解我们大脑中发生的处理过程，分析睡眠期间的大脑活动是十分必要的。我们希望将排列熵用作该分析的模型。为此，本文阐述了基于脑电图（electroencephalography, EEG）的信号处理过程，该过程会产生离散时间信号，可通过排列熵方法进行进一步处理——排列熵是香农熵（Shannon entropy）的一种改进形式，用于衡量信息处理过程。该方法被应用于18个数据集：9份来自失眠患者的脑电图测量数据，以及9份来自无睡眠障碍人群的脑电图测量数据。在模拟运行中，研究发现排列熵值与睡眠阶段之间存在强相关性。研究结果通过展示各睡眠阶段排列熵的箱线图进行分析和呈现。此外，研究还发现，当患者处于更深的睡眠状态时，排列熵值会持续下降。这表明该方法是睡眠分期的一个优良参数。最后，我们提出将该概念模型扩展应用于其他病理状况，以及手术期间的大脑活动分析。
英文摘要：
Sleep stage classification is a widely discussed topic, due to its importance in the diagnosis of sleep disorders, e.g. insomnia. Analysis of the brain activity during sleep is necessary to gain further insight into the processing that occurs in our brains. We want to use permutation entropy as a model for this analysis. Therefore, the signal processing in terms of electroencephalography is described. This results in a time discrete signal, that can be further processed by applying the method of permutation entropy, which is a modification of the Shannon entropy as a measure of information processing. The method is applied to 18 data sets, nine electroencephalography measurements of patients suffering from insomnia and nine of people without a sleep disorder. A strong correlation between the permutation entropy value and the sleep stages was found during the simulation runs. The results are analysed and presented using boxplot diagrams of the permutation entropy over the sleep stages. Furthermore, it is investigated that there is a steady decrease in the value when the patient is in a deeper sleep. This suggests that the method is a good parameter for sleep stage classification. Finally, we propose an extension of the conceptual model to other pathological conditions and also to the analysis of brain activity during surgery.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
