本文選題：癲癇腦電 + 發(fā)作檢測(cè)��； 參考：《山東大學(xué)》2016年博士論文

【摘要】：癲癇是一種常見的慢性腦部疾病,影響全世界近1%的人口。長期反復(fù)突然的癲癇發(fā)作,給患者帶來極大的痛苦和嚴(yán)重的身心傷害。癲癇發(fā)作是多種病因引起的大腦神經(jīng)元群突發(fā)性異常超同步化放電的結(jié)果,約80%的癲癇患者存在腦電圖異�，F(xiàn)象。因此,腦電圖檢查與分析是癲癇疾病診斷、病灶定位和發(fā)作類型判斷的重要手段。而借助計(jì)算機(jī)技術(shù),研究癲癇腦電信號(hào)的自動(dòng)分析與檢測(cè)方法,對(duì)提高癲癇診斷的效率和研制閉環(huán)癲癇刺激器,具有重要意義。腦電圖(Electroencephalogram, EEG)信號(hào)作為大腦神經(jīng)元電活動(dòng)在頭皮表面或大腦皮層的總體反應(yīng),具有復(fù)雜的非線性特性。雖然EEG信號(hào)的非線性分析得到了癲癇自動(dòng)檢測(cè)研究人員的重視,但是EEG信號(hào)的分形特性研究較少。分形理論是現(xiàn)代非線性科學(xué)的一個(gè)重要分支,研究EEG信號(hào)的分形特性,有助于進(jìn)一步了解癲癇發(fā)作過程中大腦混沌動(dòng)力活動(dòng)的內(nèi)在本質(zhì)。同時(shí),由于癲癇發(fā)作的機(jī)理非常復(fù)雜,發(fā)作類型和過程多種多樣,不同的癲癇患者,甚至是同一患者的不同次發(fā)作,其發(fā)作過程都不相同。因此,目前的癲癇發(fā)作自動(dòng)檢測(cè)技術(shù)還難以滿足臨床應(yīng)用所提出的準(zhǔn)確性、實(shí)時(shí)性和魯棒性要求。針對(duì)癲癇腦電分析和發(fā)作檢測(cè)領(lǐng)域存在的上述問題,本文對(duì)癲癇腦電信號(hào)的分形及多重分形特性進(jìn)行系統(tǒng)、深入的研究,并將機(jī)器學(xué)習(xí)和模式識(shí)別領(lǐng)域的前沿算法或分類器模型引入到癲癇發(fā)作檢測(cè)領(lǐng)域,研究準(zhǔn)確度高、實(shí)時(shí)性好的癲癇發(fā)作檢測(cè)方法。具體研究內(nèi)容包括以下幾方面。首先,研究EEG信號(hào)的Higuch分形維數(shù)在癲癇發(fā)作前期的演化規(guī)律,并從發(fā)作機(jī)理的角度進(jìn)行分析解釋；將發(fā)作前期EEG信號(hào)Higuchi分形維數(shù)的變化,作為癲癇發(fā)作的先兆特征,結(jié)合貝葉斯線性判別分析器,提出一種癲癇發(fā)作預(yù)測(cè)算法,對(duì)發(fā)作前期腦電進(jìn)行檢測(cè)識(shí)別。該算法在Freiburg癲癇腦電數(shù)據(jù)集上,達(dá)到較高的預(yù)測(cè)靈敏度和較低的誤報(bào)率,同時(shí)具有較低的計(jì)算成本。然后,對(duì)比研究發(fā)作期與間歇期腦電的K近鄰分形維數(shù),發(fā)現(xiàn)兩類腦電信號(hào)的K近鄰分形維數(shù)具有顯著的統(tǒng)計(jì)差異性。于是引入梯度Boosting集成學(xué)習(xí)算法,提出一種基于K近鄰分形維數(shù)和梯度Boosting的癲癇發(fā)作檢測(cè)方法。在Freiburg長程腦電數(shù)據(jù)集上,不但取得了較高的檢測(cè)靈敏度和較低的誤檢率,而且對(duì)發(fā)作期起始時(shí)刻(Onset)的檢測(cè)延時(shí)小,21例癲癇患者的平均檢測(cè)時(shí)延僅為2.46秒。接著,本文從對(duì)癲癇EEG進(jìn)行單一分形維數(shù)的算法研究和特性分析,進(jìn)一步擴(kuò)展和深入到研究EEG信號(hào)的多重分形特性,用多重分形譜深層次地刻畫癲癇腦電的局部奇異性和分形特性的不均勻性。在證明癲癇腦電信號(hào)具有多重分形特性的基礎(chǔ)上,對(duì)EEG信號(hào)多重分形譜參數(shù)的物理意義進(jìn)行解釋,并通過對(duì)比研究,發(fā)現(xiàn)發(fā)作期與間歇期EEG的多重分形特性和譜參數(shù)(α0、αmin、αmax、Δα、f(αmin)、 f(αmax)、Δf, R)都具有顯著的統(tǒng)計(jì)差異性。最后,將癲癇患者EEG信號(hào)的多重分形譜特征與相關(guān)向量機(jī)相結(jié)合,提出一種融合多導(dǎo)聯(lián)判決結(jié)果的癲癇發(fā)作檢測(cè)系統(tǒng)。在對(duì)相關(guān)向量機(jī)輸出的類概率進(jìn)行后處理的過程中,將多導(dǎo)聯(lián)的判決結(jié)果進(jìn)行融合,使其更符合臨床醫(yī)生的診斷過程。該癲癇發(fā)作檢測(cè)系統(tǒng)在Freiburg癲癇腦電數(shù)據(jù)集上進(jìn)行性能測(cè)試,取得了較高的檢測(cè)靈敏度和識(shí)別率。同時(shí)該檢測(cè)系統(tǒng)具有較低的計(jì)算復(fù)雜度,對(duì)一小時(shí)三導(dǎo)聯(lián)EEG進(jìn)行處理大約只需要1.2分鐘,表現(xiàn)出很好的檢測(cè)實(shí)時(shí)性。本文在對(duì)腦電信號(hào)單一分形維數(shù)的計(jì)算中,所采用的Higuchi算法和K近鄰算法,都是直接從信號(hào)時(shí)域進(jìn)行,不需要重構(gòu)相空間,算法簡單,計(jì)算復(fù)雜度低；而對(duì)EEG進(jìn)行多重分形分析所采用的Moment方法,相對(duì)于其他研究領(lǐng)域中常用的多重分形去趨勢(shì)波動(dòng)分析法,也具有物理意義簡單明確,計(jì)算量小等優(yōu)點(diǎn)。因此,本文基于EEG的各分形特征建立的癲癇發(fā)作檢測(cè)算法,大大降低了EEG分析和特征提取所需的時(shí)間,保證檢測(cè)算法具有較好的實(shí)時(shí)性。另外,本文所提出的幾種癲癇發(fā)作檢測(cè)算法中,分別采用了貝葉斯線性判別分析、基于集成學(xué)習(xí)思想的梯度Boosting和基于貝葉斯稀疏學(xué)習(xí)理論的相關(guān)向量機(jī)等前沿的學(xué)習(xí)算法和分類器模型,對(duì)腦電模式進(jìn)行分類識(shí)別,從而保證檢測(cè)算法具有較高的檢測(cè)準(zhǔn)確度。因此,本文的研究工作進(jìn)一步推進(jìn)了癲癇腦電的非線性特性研究,并且為研究檢測(cè)準(zhǔn)確度高、實(shí)時(shí)性好的自動(dòng)檢測(cè)方法,提供了新的思路。本文所提出的癲癇發(fā)作自動(dòng)檢測(cè)算法將在臨床大量癲癇腦電數(shù)據(jù)上,進(jìn)行性能驗(yàn)證與完善。
[Abstract]:Epilepsy is a common chronic brain disease that affects nearly 1% of the world's population. A long and recurrent seizure has caused great pain and serious physical and mental injury to the patient. Epileptic seizures are the result of a sudden abnormal hyper synchrotron discharge of a group of brain neurons caused by a variety of causes, and about 80% of the epileptic patients have electroencephalogram. Therefore, the examination and analysis of electroencephalogram (EEG) is an important means for the diagnosis of epilepsy, the location of the focus and the type of seizure, and the method of automatic analysis and detection of epileptic EEG with the help of computer technology is of great significance to improve the efficiency of epileptic diagnosis and develop a closed loop epilepticus. (Electroencephalog Ram, EEG) signals have complex nonlinear characteristics as the electrical activity of the brain neurons in the scalp surface or the cerebral cortex. Although the nonlinear analysis of EEG signals has been paid attention to by the researchers of the automatic detection of epilepsy, the fractal characteristics of the EEG signal are seldom studied. Fractal theory is an important part of the modern nonlinear science. The study of the fractal characteristics of EEG signals helps to further understand the intrinsic nature of the chaotic dynamic activity of the brain during epileptic seizures. At the same time, due to the complex mechanism of epileptic seizures, the types and processes of seizures are varied, different epileptic patients, even the different episodes of the same patient, have different episodes. Therefore, the current automatic detection techniques for epileptic seizures are difficult to meet the accuracy, real-time and robustness requirements of the clinical application. In view of the above problems in the field of epileptic EEG analysis and seizure detection, the fractal and multifractal characteristics of epileptic EEG signals are introduced in this paper, and the machine learning and modeling are studied in depth. The forward algorithm or classifier model in the field of pattern recognition is introduced into the field of epileptic seizure detection, and the methods of epileptic seizure detection with high accuracy and good real-time are studied. The specific research contents include the following aspects. First, we study the evolution of the Higuch fractal dimension of the EEG signal in the pre epileptic seizure, and divide it from the point of view of the attack mechanism. The change of the fractal dimension of the pre paroxysmal EEG signal Higuchi, as the precursor of the epileptic seizures, combines with the Bias linear discriminant analyzer, and proposes an epileptic seizure prediction algorithm, which can detect and recognize the EEG in the preparoxysmal EEG. The algorithm achieves high predictive sensitivity and low in the Freiburg epileptic EEG data set. By comparing the K nearest neighbor fractal dimensions of the brain electricity in the attack and the intermittent period, we find that the K nearest neighbor fractal dimension of the two kinds of EEG signals has significant statistical difference. Then the gradient Boosting integrated learning algorithm is introduced to propose a epilepsy based on the near neighbor fractal dimension of K and the gradient Boosting. The detection method of epileptic seizure. On the Freiburg long range EEG data set, not only high detection sensitivity and low misdetection rate have been obtained, but also the detection delay of onset time (Onset) is small. The average detection delay of 21 epileptic patients is only 2.46 seconds. Then, this paper studies the single fractal dimension algorithm of epileptic EEG The multifractal characteristics of EEG signal are further expanded and studied, and the local singularity and fractal characteristics of epileptic EEG are deeply depicted with multifractal spectrum. On the basis of the multifractal characteristics of epileptic EEG, the physical meaning of the EEG signal multifractal parameters is explained. By contrast, the multifractal characteristics and spectral parameters (alpha 0, alpha min, alpha max, delta alpha, f (alpha min), f (alpha max), delta f, R) were found to have significant statistical differences. Finally, the multiple fractal spectrum characteristics of EEG signals in epileptic patients were combined with the correlation vector machines, and a kind of epilepsy was proposed to fuse the multiple lead decision results. In the process of post processing of the class probability of the output of the related vector machine, the decision results of the multi lead are fused to make it more consistent with the diagnosis process of the clinician. The epileptic seizure detection system performs the performance test on the Freiburg epileptic EEG data set, and has obtained high detection sensitivity and recognition rate. The detection system has a low computational complexity, and it takes only 1.2 minutes to deal with the one hour three lead EEG. In the calculation of the single fractal dimension of the brain electrical signal, the Higuchi algorithm and the K nearest neighbor algorithm are all directly from the signal time domain and do not need to reconstruct the phase space. The algorithm is simple and the computational complexity is low, and the Moment method used in multifractal analysis for EEG is also characterized by simple physical meaning and small calculation, compared with the common multifractal detrending wave analysis method in other research fields. Therefore, the epileptic seizure detection algorithm based on the fractal characteristics of EEG is established in this paper. The time required for EEG analysis and feature extraction is greatly reduced to ensure that the detection algorithm has good real-time performance. In addition, the Bayesian linear discriminant analysis, the gradient Boosting based on the integrated learning idea and the correlation vector machine based on Bayesian sparse learning theory are used respectively in several epileptic seizure detection algorithms proposed in this paper. The advanced learning algorithm and classifier model are used to classify the EEG pattern, thus ensuring the high detection accuracy of the detection algorithm. Therefore, the research work of this paper further advances the study of the nonlinear characteristics of epileptic EEG, and provides a new thought for the study of the automatic detection method with high detection accuracy and good real-time performance. The automatic epileptic seizure detection algorithm proposed in this paper will be validated and perfected on clinical epileptic EEG data.

【學(xué)位授予單位】：山東大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類號(hào)】：R742.1;TN911.6
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本文編號(hào)：1808873