本文選題：意識障礙 + 腦電信號　； 參考：《杭州電子科技大學(xué)》2012年碩士論文

【摘要】：意識障礙患者腦電信號的分析和評價(jià)是當(dāng)今康復(fù)醫(yī)學(xué)工程研究領(lǐng)域熱點(diǎn)課題之一，對意識障礙患者的病理診斷及康復(fù)治療具有重大的意義。 腦電信號(Electroencephalogram，EEG)是一種能反映人類思維活動(dòng)的生物電信號，是通過布置在頭皮或顱內(nèi)的電極記錄下來的腦細(xì)胞群電活動(dòng)。大量實(shí)驗(yàn)已證明腦電信號是混沌信號，具有顯著的非線性動(dòng)力學(xué)特征，因此利用腦電信號的各種非線性動(dòng)力學(xué)特征參數(shù)可以表達(dá)大腦的多種顯著性思維意識活動(dòng)，并可以在臨床上利用這些特征參數(shù)實(shí)現(xiàn)對各種腦功能障礙癥狀的分析和評價(jià)。 本文以非線性動(dòng)力學(xué)為理論基礎(chǔ)，通過分析腦電信號研究現(xiàn)狀及處理方法，設(shè)計(jì)了能反映大腦思維意識活動(dòng)的腦電實(shí)驗(yàn)；用多尺度Lempel-Ziv復(fù)雜度、排列分劃Lempel-Ziv復(fù)雜度、C0復(fù)雜度、基本尺度熵作為刻畫腦電信號特征的非線性動(dòng)力學(xué)特征參數(shù)，用實(shí)驗(yàn)證明了這四種非線性特征參數(shù)反映大腦思維意識活動(dòng)的有效性；探索意識障礙患者和正常人腦電信號的四種非線性特征參數(shù)在各種刺激下的變化，并進(jìn)行對比分析。本文具體完成的研究工作主要包括： (1)從腦電信號的產(chǎn)生機(jī)理出發(fā)，闡釋了腦電信號的分類及處理方法，并設(shè)計(jì)了兩套實(shí)驗(yàn)方案：驗(yàn)證性實(shí)驗(yàn)和對比實(shí)驗(yàn)。驗(yàn)證性實(shí)驗(yàn)方案目的是為了證明腦電信號在不同意識任務(wù)下具有不同的特征，且相關(guān)非線性動(dòng)力學(xué)特征參數(shù)能夠體現(xiàn)出這些特征，實(shí)驗(yàn)共有安靜閉眼、閉眼心算、記憶三種模式；對比實(shí)驗(yàn)方案目的是分析正常人和意識障礙患者在相同刺激下的腦電信號的非線性特征值異同以及各自在不同刺激下的變化，共有安靜狀態(tài)、喚名刺激和抬手指令等三種模式。 (2)在腦電信號的預(yù)處理方面，針對干擾源產(chǎn)生的噪聲，，本文采用基于SURE的小波軟閾值方法對腦電信號進(jìn)行消噪，算法分析表明，基于SURE的小波軟閾值方法有比其它閾值方法更好的消噪效果；針對獨(dú)立源產(chǎn)生的干擾，無法用小波消噪的方法濾除，為此，本文首次將基于最大信噪比的盲源分離算法用于腦電信號的偽跡濾波處理，算法的實(shí)驗(yàn)結(jié)果表明，分離效果和運(yùn)行效率均優(yōu)于常用的FastICA算法和Infomax算法。由之本文得出：基于SURE的小波軟閾值方法能較好去除腦電信號中的隨機(jī)噪聲；基于最大信噪比的盲源分離算法能成功分離獨(dú)立源產(chǎn)生的干擾。 (3)在分析非線性動(dòng)力學(xué)基本理論的基礎(chǔ)上，首次將多尺度Lempel-Ziv復(fù)雜度、排列分劃Lempel-Ziv復(fù)雜度、C0復(fù)雜度、基本尺度熵等四種非線性動(dòng)力學(xué)特征值運(yùn)用于腦電思維意識活動(dòng)的分析，計(jì)算了FP1、FP2、P3、P4、F7、F8電極腦電信號在不同思維意識模式下的四種非線性特征值，并進(jìn)行了對比，用單因素方差分析(One-WayANVOA)，通過統(tǒng)計(jì)分析軟件SPSS 19.0 for windows實(shí)現(xiàn)。上述驗(yàn)證性實(shí)驗(yàn)結(jié)果表明，這四種非線性特征參數(shù)能較好地反映正常人腦電信號在不同思維意識活動(dòng)模式下的差異。 (4)在臨床實(shí)驗(yàn)數(shù)據(jù)處理方面，將多尺度Lempel-Ziv復(fù)雜度、排列分劃Lempel-Ziv復(fù)雜度、C0復(fù)雜度、基本尺度熵再次運(yùn)用于意識障礙患者腦電信號的評價(jià)和分析，計(jì)算了C3、C4、T3、T4電極腦電信號在不同刺激下的四種非線性特征值，分析了正常人和意識障礙患者在相同刺激下的腦電信號的非線性特征值異同以及各自在不同刺激下的變化，采用單因素方差分析(One-Way ANVOA)和兩獨(dú)立樣本t檢驗(yàn)分析，通過統(tǒng)計(jì)分析軟件SPSS 19.0 for windows實(shí)現(xiàn)。上述對比實(shí)驗(yàn)結(jié)果表明，腦電信號運(yùn)用于意識障礙患者的刺激反應(yīng)和意識狀態(tài)分析，這四種非線性動(dòng)力學(xué)特征參數(shù)可以成為分析評價(jià)的主要依據(jù)之一。
[Abstract]:The analysis and evaluation of electroencephalogram (EEG) in patients with consciousness disorder is one of the hot topics in the field of rehabilitation medical engineering. It is of great significance for the pathological diagnosis and rehabilitation treatment of patients with consciousness disorder.
Electroencephalogram (EEG) is a bioelectrical signal that can reflect human thinking activity. It is an electrical activity of brain cells recorded by the electrodes arranged on the scalp or intracranial. A large number of experiments have proved that the EEG signals are chaotic signals and have significant nonlinear dynamic characteristics. The characteristic parameters of linear dynamics can express a variety of significant thinking activities in the brain, and can be used to analyze and evaluate the symptoms of various brain disorders by using these parameters in clinical practice.
In this paper, based on the theoretical basis of nonlinear dynamics, by analyzing the current status and processing methods of EEG signal research, a brain electricity experiment can be designed to reflect the mind consciousness activity of the brain, and the multiscale Lempel-Ziv complexity is used to divide the Lempel-Ziv complexity, C0 complexity and basic scale entropy as the characterization of the EEG. The characteristic parameters show that the four nonlinear characteristic parameters reflect the effectiveness of brain thinking activity, and explore the changes of the four nonlinear characteristic parameters of the patients with consciousness and normal human brain signals under various stimuli.
(1) from the generation mechanism of EEG signals, the classification and processing methods of EEG signals are explained, and two experimental schemes are designed: confirmatory and contrast experiments. The purpose of the confirmatory experiment is to prove that EEG signals have different characteristics under different consciousness tasks, and the related nonlinear dynamic characteristic parameters are capable. With these characteristics, there are three modes of quiet closed eyes, closed eye, heart calculation and memory. The aim of the contrast experiment is to analyze the differences and similarities of the nonlinear characteristic values of the EEG signals of the normal and the conscious patients under the same stimulus, and the changes of their respective stimuli under different stimuli. There are three modes of silence, name stimulation and hand raising. Style.
(2) in the preprocessing of EEG signal, the wavelet soft threshold method based on SURE is used to denoise the EEG. The algorithm analysis shows that the wavelet soft threshold method based on SURE has better denoising effect than other threshold methods, and the noise generated by independent source can not be used in wavelet denoising. In this paper, the blind source separation algorithm based on the maximum signal to noise ratio is used for the first time to filter the EEG signal. The experimental results show that the separation effect and the operating efficiency are better than the common FastICA algorithm and the Infomax algorithm. This paper shows that the wavelet soft threshold method based on SURE can better remove the EEG. The blind source separation algorithm based on the maximum signal-to-noise ratio can successfully separate the interference generated by independent sources.
(3) on the basis of analyzing the basic theory of nonlinear dynamics, four kinds of nonlinear dynamic characteristics, such as multiscale Lempel-Ziv complexity, permutation, Lempel-Ziv complexity, C0 complexity and basic scale entropy, are used for the analysis of brain electrical thinking consciousness, and FP1, FP2, P3, P4, F7, and F8 electrode brain electrical signals are calculated in different thinking consciousness. The four nonlinear eigenvalues under the model are compared with the single factor variance analysis (One-WayANVOA) and the statistical analysis software SPSS 19 for windows. The experimental results show that the four nonlinear characteristic parameters can reflect the difference between the normal human brain electrical signals in different thinking modes of activity.
(4) in the clinical experimental data processing, the multiscale Lempel-Ziv complexity, the arrangement of Lempel-Ziv complexity, the C0 complexity and the basic scale entropy are reused in the evaluation and analysis of the EEG signals of the patients with consciousness obstacles, and the four nonlinear eigenvalues of the C3, C4, T3 and T4 electroencephalogram signals under different stimuli are calculated, and the normal people and the normal people are analyzed. A single factor analysis of variance (One-Way ANVOA) and two independent sample t test were used to analyze the difference of the nonlinear eigenvalues of EEG signals and their changes under the same stimulation. The results of the statistical analysis software SPSS 19 for windows were implemented. These four nonlinear dynamic characteristic parameters can be one of the main bases of analysis and evaluation.
【學(xué)位授予單位】：杭州電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：R318.0;TN911.6

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