本文選題：腦信號　切入點(diǎn)：精神分裂癥　出處：《南京郵電大學(xué)》2017年碩士論文

【摘要】：腦電/磁信號是能夠反映大腦不同生理狀態(tài)的復(fù)雜生物信號,常用于癲癇、老年癡呆癥、精神分裂癥等疾病的診斷和監(jiān)測,病理腦電/磁信號的分析有助于進(jìn)一步地了解腦疾病的基礎(chǔ)發(fā)生機(jī)制,為腦疾病的的臨床診斷提供參考依據(jù),為患者的康復(fù)帶來希望,論文從精神分裂癥腦磁信號以及穩(wěn)態(tài)視覺誘發(fā)電位腦電信號兩方面進(jìn)行了研究。論文首先對腦電/磁信號的分析方法進(jìn)行了介紹,重點(diǎn)介紹了特征提取和模式分類方法,對這些方法的原理和特性進(jìn)行了較為詳細(xì)的闡述。論文提出了一種基于多維復(fù)雜度的腦磁信號分析方法。通過提取精神分裂癥腦磁信號的AR模型系數(shù)、頻帶能量、近似熵和Lempel-Ziv復(fù)雜度作為特征,運(yùn)用距離準(zhǔn)則和增L減R算法進(jìn)行通道篩選,再運(yùn)用BP神經(jīng)網(wǎng)絡(luò)和支持向量機(jī)對精神分裂癥和正常人的腦磁信號進(jìn)行區(qū)分,分類正確率分別為96.25%和98.75%,實(shí)驗(yàn)表明該方法可以有效地區(qū)分精神分裂癥患者和正常人。論文還運(yùn)用遺傳算法選擇具有顯著性差異的特征,BP神經(jīng)網(wǎng)絡(luò)和支持向量機(jī)的分類正確率分別為98.5%和99.75%,支持向量機(jī)可以獲得更好的分類性能。最后,論文基于SSVEP設(shè)計(jì)腦電實(shí)驗(yàn),采集多位被試的腦電信號,運(yùn)用DFT、CCA以及MSI分析方法對采集的信號進(jìn)行分析,DFT方法的頻譜分析結(jié)果表明信號能量在目標(biāo)刺激頻率處最大,CCA和MSI方法分析過程中可以觀察到信號在目標(biāo)頻率處的相關(guān)系數(shù)以及同步指數(shù)最大,無論是短時(shí)間窗還是長時(shí)間窗,CCA方法的識別結(jié)果要優(yōu)于MSI方法和DFT方法,而MSI方法性能總體上要優(yōu)于DFT方法,尤其是在數(shù)據(jù)長度較短時(shí)。
[Abstract]:EEG / magnetic signals are complex biological signals that can reflect different physiological states of the brain. They are often used in the diagnosis and monitoring of epilepsy, Alzheimer's disease, schizophrenia and other diseases.The analysis of pathological EEG / magnetic signals is helpful to further understand the underlying mechanism of brain disease, to provide a reference for the clinical diagnosis of brain disease, and to bring hope for the recovery of patients.In this paper, the EEG signal of schizophrenia and the steady state visual evoked potential (VEP) are studied.In this paper, the analysis methods of EEG / magnetic signals are introduced, especially the methods of feature extraction and pattern classification, and the principle and characteristics of these methods are described in detail.In this paper, a method based on multi-dimensional complexity for the analysis of brain magnetic signals is proposed.The AR model coefficients, frequency band energy, approximate entropy and Lempel-Ziv complexity of the brain magnetic signals of schizophrenia were extracted as the features. The distance criterion and the algorithm of increasing L minus R were used to screen the channels.Then BP neural network and support vector machine were used to distinguish the brain magnetic signals between schizophrenia and normal subjects. The classification accuracy was 96.25% and 98.75% respectively. The experiment shows that the method can effectively distinguish schizophrenia patients from normal people.The classification accuracy of BP neural network and support vector machine are 98.5% and 99.75, respectively. Support vector machine can obtain better classification performance.Finally, the experiment of EEG was designed based on SSVEP, and the EEG signals were collected.The spectrum analysis of the collected signals by DFT and MSI shows that the signal energy can be observed at the target frequency when the signal energy is maximum at the target frequency and the MSI method can be used to analyze the signal at the target frequency.Correlation coefficient and synchronization index are the largest,The results of MSI and DFT are better than that of MSI and DFT, and the performance of MSI is better than that of DFT, especially when the length of data is short.
【學(xué)位授予單位】：南京郵電大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：R749.3;TN911.6

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