本文關(guān)鍵詞：癲癇發(fā)作腦電信號(hào)的相位幅值耦合特征的研究　出處：《沈陽(yáng)工業(yè)大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 癲癇 腦電圖 相位-幅度耦合 排列熵 發(fā)作期自動(dòng)標(biāo)記

【摘要】：癲癇是大腦神經(jīng)元突發(fā)性異常放電,導(dǎo)致短暫的大腦功能性障礙的一種慢性疾病。癲癇發(fā)作具有突然性和反復(fù)性,給患者學(xué)習(xí)、生活和工作形成極大障礙,嚴(yán)重影響到患者及其家庭的生活質(zhì)量。由于其發(fā)作短暫,臨床醫(yī)生很少目睹發(fā)作過(guò)程,增加了診治的困難。腦電圖可記錄患者癲癇發(fā)作異常放電過(guò)程,已成為癲癇診治最重要的工具。然而,長(zhǎng)時(shí)程視頻腦電圖通常記錄數(shù)天乃至數(shù)周,給癲癇中心帶來(lái)了海量數(shù)據(jù),癲癇發(fā)作數(shù)據(jù)的標(biāo)記與分析成為了臨床醫(yī)生一項(xiàng)繁重工作。本論文以癲癇發(fā)作腦電圖為研究對(duì)象,分析了癲癇發(fā)作期腦電圖的低頻相位與高頻幅度耦合的特征,研究了不同低頻和高頻節(jié)律間的耦合關(guān)系;基于相位-幅度耦合特征對(duì)癲癇發(fā)作間期與發(fā)作期腦電圖進(jìn)行了分類研究,實(shí)現(xiàn)了發(fā)作期數(shù)據(jù)段的自動(dòng)標(biāo)記。具體工作包括:首先,本文基于波恩腦電數(shù)據(jù)集,研究了癲癇腦電低頻節(jié)律相位與高頻節(jié)律幅度間的耦合關(guān)系,利用調(diào)制指數(shù)(Modulation Index,MI)來(lái)量化各頻段間的耦合強(qiáng)度,提出了依據(jù)高低頻節(jié)律范圍對(duì)MI圖分區(qū)方法。分析結(jié)果顯示,與發(fā)作間期相比,發(fā)作期Gamma節(jié)律與多種低頻節(jié)律的MI值均顯著(p0.01)增強(qiáng)。Theta節(jié)律與Beta節(jié)律間MI值存在顯著差異,分區(qū)后MI特征對(duì)發(fā)作期和間期數(shù)據(jù)分類正確率達(dá)到97%。其次,本文從腦電圖非線性特征出發(fā),研究了癲癇發(fā)作期腦電圖的排列熵特征,將排列熵、標(biāo)準(zhǔn)差等特征聯(lián)合,對(duì)波恩腦電數(shù)據(jù)集癲癇發(fā)作間期和發(fā)作期數(shù)據(jù)進(jìn)行特征提取。結(jié)果顯示在分類過(guò)程中癲癇腦電圖的排列熵和標(biāo)準(zhǔn)差特征具有互補(bǔ)性,在計(jì)算排列熵符號(hào)化過(guò)程中,有尺度信息損失,而標(biāo)準(zhǔn)差等特征可彌補(bǔ)相關(guān)信息,二者聯(lián)合也可使癲癇發(fā)作與癲間腦電識(shí)別率達(dá)到97%。再次,本文對(duì)癲癇狗腦電圖進(jìn)行了相位-幅值耦合特征的分析。利用大樣本數(shù)癲癇狗數(shù)據(jù)分類結(jié)果顯示,耦合特征能夠用于腦電圖自動(dòng)分類,正確率達(dá)到92%,且多個(gè)低頻和高頻節(jié)律間耦合特征對(duì)分類結(jié)果都有影響。最后,本文將癲癇發(fā)作期腦電圖的相位-幅度耦合、排列熵等特征應(yīng)用于實(shí)測(cè)數(shù)據(jù)分析,對(duì)沈陽(yáng)軍區(qū)總醫(yī)院患者術(shù)前評(píng)估記錄的顱內(nèi)腦電圖進(jìn)行了發(fā)作期自動(dòng)標(biāo)記研究;在對(duì)單次發(fā)作數(shù)據(jù)標(biāo)記的基礎(chǔ)上,可以非常準(zhǔn)確的自動(dòng)標(biāo)記出患者其他的發(fā)作,分類準(zhǔn)確率為95.5%。本文研究方法在臨床的應(yīng)用將能有效降低醫(yī)生長(zhǎng)時(shí)程腦電圖分析負(fù)擔(dān),具有很好應(yīng)用前景和現(xiàn)實(shí)意義。
[Abstract]:Epilepsy is a chronic disease caused by sudden abnormal discharges of brain neurons, which leads to transient functional disorders of the brain. Epileptic seizures have sudden and repetitive characteristics, which make the patients learning, living and working severely impaired. It seriously affects the quality of life of patients and their families. Because of its short duration, clinicians rarely see the onset process, increasing the difficulty of diagnosis and treatment. EEG can record the abnormal discharge process of epileptic seizures in patients. It has become the most important tool for the diagnosis and treatment of epilepsy. However, long time video EEG usually records several days or even weeks, which brings a lot of data to the epileptic center. The marking and analysis of epileptic seizure data has become a heavy task for clinicians. In this paper, the characteristics of low frequency phase and high frequency amplitude coupling of EEG during epileptic seizures were analyzed. The coupling relationship between different low-frequency and high-frequency rhythms is studied. Based on the phase-amplitude coupling feature, the electroencephalogram (EEG) in the interictal phase and the seizure phase is classified, and the automatic marking of the seizure data segment is realized. The specific work includes: first, based on the Bonn EEG data set. The coupling relationship between the phase of low-frequency rhythm and the amplitude of high-frequency rhythm in epileptic EEG was studied. The modulation index Modulation Index (MI) was used to quantify the coupling intensity between different frequency bands. A method of dividing MI map according to the range of high and low frequency rhythm is proposed. The results show that it is compared with interictal period. The MI values of Gamma rhythm and various low frequency rhythms were significantly increased (p 0.01). There was significant difference between the MI value of Theta rhythm and Beta rhythm. The classification accuracy rate of MI features on the seizure and interphase data is 97%. Secondly, from the nonlinear characteristics of EEG, this paper studies the permutation entropy characteristics of EEG during epileptic seizures, which will be permutation entropy. The standard deviation and other features were combined to extract the interictal and interictal data from the Bonn EEG data set. The results showed that the entropy and standard deviation of EEG were complementary in the course of classification. In the process of computing the entropy symbolization, there is a loss of scale information, and the standard deviation can compensate for the relevant information. The combination of the two can also make the recognition rate of epileptic seizures and epileptic diencephalogram reach 97%. Thirdly. In this paper, the phase amplitude coupling characteristics of EEG in epileptic dogs were analyzed. The results showed that the coupling features could be used to classify EEG automatically, and the correct rate was 92%. And the coupling characteristics between low frequency and high frequency rhythm have influence on the classification results. Finally, the phase amplitude coupling and permutation entropy of EEG during epileptic seizures are applied to the analysis of measured data. The intracranial electroencephalogram (EEG) recorded by preoperative evaluation in Shenyang military region General Hospital (Shenyang military region General Hospital) was studied with automatic marking during the attack period. On the basis of the single attack data marker, it is very accurate to automatically mark the other attacks of the patient. The classification accuracy is 95.5.The clinical application of this research method can effectively reduce the burden of long-term EEG analysis of doctors, which has a good application prospect and practical significance.
【學(xué)位授予單位】：沈陽(yáng)工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：R742.1

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 張濤;陳萬(wàn)忠;李明陽(yáng);;基于頻率切片小波變換和支持向量機(jī)的癲癇腦電信號(hào)自動(dòng)檢測(cè)[J];物理學(xué)報(bào);2016年03期

2 王杰;李牧瀟;;改進(jìn)的極限學(xué)習(xí)機(jī)在癲癇腦電分類中的應(yīng)用[J];計(jì)算機(jī)仿真;2014年06期

3 孟慶芳;陳珊珊;陳月輝;馮志全;;基于遞歸量化分析與支持向量機(jī)的癲癇腦電自動(dòng)檢測(cè)方法[J];物理學(xué)報(bào);2014年05期

4 龐春穎;王小甜;孫曉琳;;一種基于改進(jìn)經(jīng)驗(yàn)?zāi)B(tài)分解的癲癇腦電識(shí)別新方法[J];中國(guó)生物醫(yī)學(xué)工程學(xué)報(bào);2013年06期

5 張振;杜守洪;陳子怡;田翔華;周毅;張洋;;近似熵與SVM在自動(dòng)分類癲癇腦電信號(hào)中的應(yīng)用[J];生物醫(yī)學(xué)工程研究;2013年02期

6 馬莉;杜一鳴;黃光;王耘;;基于樣本熵與人工神經(jīng)網(wǎng)絡(luò)的癲癇發(fā)作預(yù)測(cè)初步研究[J];中國(guó)生物醫(yī)學(xué)工程學(xué)報(bào);2013年02期

7 陳金燕;鄒俊忠;張見;;基于小波變換和Teager能量算子的癲癇腦電自動(dòng)分類[J];生物醫(yī)學(xué)工程學(xué)進(jìn)展;2012年03期

8 袁琦;周衛(wèi)東;李淑芳;蔡冬梅;;基于ELM和近似熵的腦電信號(hào)檢測(cè)方法[J];儀器儀表學(xué)報(bào);2012年03期

9 韓敏;孫磊磊;洪曉軍;韓杰;;基于自回歸模型和關(guān)聯(lián)向量機(jī)的癲癇腦電信號(hào)自動(dòng)分類[J];中國(guó)生物醫(yī)學(xué)工程學(xué)報(bào);2011年06期

10 王玉;周衛(wèi)東;李淑芳;袁琦;耿淑娟;;腦電信號(hào)的分形截距特征分析及在癲癇檢測(cè)中的應(yīng)用[J];中國(guó)生物醫(yī)學(xué)工程學(xué)報(bào);2011年04期

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