本文選題：精神分裂癥 + 腦電��； 參考：《蘭州大學(xué)》2012年碩士論文

【摘要】：精神分裂癥是一種嚴(yán)重影響人類健康的精神疾病,隨著科技發(fā)展,經(jīng)過不斷的探索和改進(jìn),如今以腦電生理學(xué)技術(shù)為基礎(chǔ),以信號處理方法為工具的腦電信號輔助診斷方法已經(jīng)得到非常廣泛的普及。在研究中人們發(fā)現(xiàn)腦電信號具有混沌屬性,線性的信號處理方法如時頻分析、相關(guān)分析等無法反映腦電信號的這一性質(zhì)。而非線性方法以非線性動力學(xué)理論為基礎(chǔ),更能夠反映腦電信號的本質(zhì)特性。 我們將非線性理論應(yīng)用到腦電信號處理之中,尋找有效的反映精神分裂癥腦電信號本質(zhì)并且能將其與正常人腦電信號相互區(qū)分的非線性特征,從而可以建立一個相對客觀的基于腦電信號分析的精神分裂癥輔助診斷方法。 本文研究并詳細(xì)介紹了關(guān)聯(lián)維數(shù)、最大李雅普諾夫指數(shù)、LZ復(fù)雜度、CO復(fù)雜度、柯爾莫哥洛夫熵等具有代表性的非線性特征算法,并且針對各個特征算法的特性和優(yōu)缺點進(jìn)行了比較。根據(jù)現(xiàn)有文獻(xiàn)研究進(jìn)度,C0復(fù)雜度尚未用于精神分裂癥腦電信號的研究之中,本研究中為首次使用。其次我們還選擇了關(guān)聯(lián)維數(shù)、柯爾莫哥洛夫熵、LZ復(fù)雜度與C0復(fù)雜度共同應(yīng)用于比較精神分裂癥患者與正常人的腦電信號的區(qū)別,對各個特征進(jìn)行比較并且相互驗證結(jié)果。 我們使用統(tǒng)計方法對計算得到的特征值進(jìn)行分析,每個特征均有良好效果,除了少部分導(dǎo)聯(lián),兩組人群的特征值結(jié)果在大多數(shù)導(dǎo)聯(lián)上均有顯著性差異,并且通過均值比較,病人的非線性特征值要比正常人的高。這個結(jié)果說明我們選取的特征能夠滿足區(qū)分兩類人群的要求,并且發(fā)現(xiàn)C0復(fù)雜度具有更良好的結(jié)果以及更快的運算速度。同時也驗證了相關(guān)文獻(xiàn)中精神分裂癥患者的腦電信號具有更強的非線性和復(fù)雜性的結(jié)論。我們對這個結(jié)果進(jìn)行了可信度討論。最后通過分類器得出了91%的分類率,這個結(jié)果說明我們所用的基于腦電信號非線性分析的方法適用于鑒別精神分裂癥。
[Abstract]:Schizophrenia is a mental disease that seriously affects human health. With the development of science and technology, and through continuous exploration and improvement, it is now based on electrophysiologic techniques. The method of EEG aided diagnosis based on signal processing has been widely used. In the study, it is found that EEG signals have chaotic properties, and linear signal processing methods such as time-frequency analysis and correlation analysis can not reflect this property of EEG signals. The nonlinear method is based on the theory of nonlinear dynamics and can reflect the essential characteristics of EEG. We apply nonlinear theory to EEG processing to find nonlinear characteristics that reflect the nature of EEG in schizophrenia and distinguish it from normal EEG. Therefore, a relatively objective method for the diagnosis of schizophrenia based on EEG analysis can be established. In this paper, some typical nonlinear feature algorithms, such as correlation dimension, maximum Lyapunov exponent LZ complexity and CO complexity, Kolmogorov entropy and so on, are studied and introduced in detail. At the same time, the characteristics, advantages and disadvantages of each feature algorithm are compared. According to the current literature, the complexity of C0 has not been used in the study of EEG in schizophrenia, and it is the first time in this study. Secondly, we choose correlation dimension, Kolmogorov entropy LZ complexity and C0 complexity to compare the differences of EEG between schizophrenic patients and normal subjects. We use the statistical method to analyze the calculated eigenvalues, and each feature has a good effect. Except for a few leads, the results of the eigenvalues of the two groups are significantly different in most leads, and the mean values are compared. The patient's nonlinear characteristic value is higher than that of the normal person. The results show that the selected features can meet the requirements of distinguishing the two groups of population, and it is found that the C0 complexity has better results and faster operation speed. It also verifies the conclusion that the EEG of schizophrenic patients has stronger nonlinearity and complexity. We discussed the credibility of this result. Finally, the classification rate of 91% is obtained by the classifier, which shows that the method based on the nonlinear analysis of EEG signals is suitable for the identification of schizophrenia.
【學(xué)位授予單位】：蘭州大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：TN911.7;R749.3

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本文編號：1989850