【摘要】：腦電信號(hào)具體的產(chǎn)生機(jī)制目前仍處于研究階段，它包含了大量神經(jīng)元以不同形式組織活動(dòng)的信息，具有非線性、多尺度、多分辨性等特性。多尺度性是指人體神經(jīng)電生理信號(hào)包含了多種時(shí)間尺度成份及多種空間解剖結(jié)構(gòu)尺度。其多尺度性是傳統(tǒng)的單尺度熵方法難以描述的，也不是傅里葉線性多頻率疊加理論可以完全解決的。其尺度的數(shù)目從理論上來(lái)說(shuō)是無(wú)限多的，這是腦電信號(hào)這種極端復(fù)雜的非線性系統(tǒng)所共有的特征。因此，多尺度特性是急待研究的重點(diǎn)問(wèn)題。 近年來(lái)，多尺度熵方法已經(jīng)逐漸成為探究多尺度特性及描述神經(jīng)生理機(jī)制的工具。雖然有多種多尺度分解方法，如粗�；^(guò)程，移動(dòng)平均過(guò)程，小波變換和集合經(jīng)驗(yàn)?zāi)B(tài)分解，但是很少有對(duì)這些方法的系統(tǒng)性的評(píng)估。本文的目的是找到描述神經(jīng)生理機(jī)制的最佳多尺度熵指標(biāo)。研究思路是綜合比較多種多尺度熵的性能，進(jìn)而應(yīng)用到實(shí)際信號(hào)中分析其多尺度特性。 首先，將四種熵方法，香農(nóng)熵、樣本熵、排序熵和遞歸熵與四種多尺度分解方法,粗粒化過(guò)程，移動(dòng)平均過(guò)程，最大重疊率離散小波變換和集合經(jīng)驗(yàn)?zāi)B(tài)分解相結(jié)合，生成16種多尺度熵方法。 然后，應(yīng)用一個(gè)基于不同參數(shù)的神經(jīng)群模型來(lái)產(chǎn)生一個(gè)強(qiáng)度可變的神經(jīng)元群，輸出類(lèi)似于正常腦電信號(hào)和癲癇棘波信號(hào)的模擬信號(hào)。添加不同密度的高斯白噪聲到神經(jīng)群模型中來(lái)量化每一個(gè)多尺度熵的抗噪性；使用預(yù)測(cè)概率分析來(lái)評(píng)估每一種多尺度熵的有效性；分別繪制不同信號(hào)狀態(tài)下的各個(gè)尺度下的多尺度熵的值，量化每一種多尺度熵在各個(gè)尺度下的區(qū)分度。 最后，將多尺度熵方法應(yīng)用到實(shí)際的癲癇信號(hào)和麻醉信號(hào)中，，找到跟蹤癲癇信號(hào)的癲癇狀態(tài)強(qiáng)度和麻醉信號(hào)的麻醉深度的最佳多尺度熵方法。
[Abstract]:The specific generation mechanism of EEG signal is still in the research stage. It contains a large number of information of neurons in different forms of tissue activity, which has the characteristics of nonlinear, multi-scale, multi-resolution and so on. Multi-scale means that the electrophysiological signal of human nerve contains a variety of time-scale components and spatial anatomical structure scales. Its multi-scale property is difficult to describe by traditional single-scale entropy method, nor can it be completely solved by Fourier linear multi-frequency superposition theory. The number of scales is infinite in theory, which is the common characteristic of EEG signal, which is a very complex nonlinear system. Therefore, multi-scale characteristics is an urgent issue to be studied. In recent years, multi-scale entropy method has gradually become a tool for exploring multi-scale characteristics and describing neurophysiological mechanism. Although there are many multi-scale decomposition methods, such as coarse-grained process, moving average process, wavelet transform and set empirical mode decomposition, few of these methods are systematically evaluated. The aim of this paper is to find the best multi-scale entropy index to describe the neurophysiological mechanism. The research idea is to synthesize and compare the performance of multi-scale entropy, and then apply it to analyze the multi-scale characteristics of real signals. Firstly, four entropy methods, Shannon entropy, sample entropy, ordering entropy and recursive entropy, are combined with four multi-scale decomposition methods, coarse-grained process, moving average process, maximum overlap discrete wavelet transform and set empirical mode decomposition. Sixteen kinds of multi-scale entropy methods are generated. Then, a neural group model based on different parameters is used to generate a group of neurons with variable intensity, which outputs analog signals similar to normal EEG signals and epileptic spike signals. Different density Gaussian white noise is added to the neural group model to quantify the anti-noise property of each multi-scale entropy, and the prediction probability analysis is used to evaluate the effectiveness of each multi-scale entropy. The value of multi-scale entropy in each scale of different signal states is plotted, and the discrimination degree of each multi-scale entropy in each scale is quantified. Finally, the multi-scale entropy method is applied to the actual epileptic signals and anesthetic signals, and the optimal multi-scale entropy method is found to track the state-of-epilepsy intensity of epileptic signals and the anesthetic depth of anesthetic signals.
【學(xué)位授予單位】：燕山大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：TN911.6

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