本文選題：癲癇腦電 + 發(fā)作檢測�。� 參考：《山東大學》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。長期反復突然的癲癇發(fā)作,給患者帶來極大的痛苦和嚴重的身心傷害。癲癇發(fā)作是多種病因引起的大腦神經元群突發(fā)性異常超同步化放電的結果,約80%的癲癇患者存在腦電圖異常現象。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。而借助計算機技術,研究癲癇腦電信號的自動分析與檢測方法,對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)信號作為大腦神經元電活動在頭皮表面或大腦皮層的總體反應,具有復雜的非線性特性。雖然EEG信號的非線性分析得到了癲癇自動檢測研究人員的重視,但是EEG信號的分形特性研究較少。分形理論是現代非線性科學的一個重要分支,研究EEG信號的分形特性,有助于進一步了解癲癇發(fā)作過程中大腦混沌動力活動的內在本質。同時,由于癲癇發(fā)作的機理非常復雜,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)作自動檢測技術還難以滿足臨床應用所提出的準確性、實時性和魯棒性要求。針對癲癇腦電分析和發(fā)作檢測領域存在的上述問題,本文對癲癇腦電信號的分形及多重分形特性進行系統、深入的研究,并將機器學習和模式識別領域的前沿算法或分類器模型引入到癲癇發(fā)作檢測領域,研究準確度高、實時性好的癲癇發(fā)作檢測方法。具體研究內容包括以下幾方面。首先,研究EEG信號的Higuch分形維數在癲癇發(fā)作前期的演化規(guī)律,并從發(fā)作機理的角度進行分析解釋；將發(fā)作前期EEG信號Higuchi分形維數的變化,作為癲癇發(fā)作的先兆特征,結合貝葉斯線性判別分析器,提出一種癲癇發(fā)作預測算法,對發(fā)作前期腦電進行檢測識別。該算法在Freiburg癲癇腦電數據集上,達到較高的預測靈敏度和較低的誤報率,同時具有較低的計算成本。然后,對比研究發(fā)作期與間歇期腦電的K近鄰分形維數,發(fā)現兩類腦電信號的K近鄰分形維數具有顯著的統計差異性。于是引入梯度Boosting集成學習算法,提出一種基于K近鄰分形維數和梯度Boosting的癲癇發(fā)作檢測方法。在Freiburg長程腦電數據集上,不但取得了較高的檢測靈敏度和較低的誤檢率,而且對發(fā)作期起始時刻(Onset)的檢測延時小,21例癲癇患者的平均檢測時延僅為2.46秒。接著,本文從對癲癇EEG進行單一分形維數的算法研究和特性分析,進一步擴展和深入到研究EEG信號的多重分形特性,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具有多重分形特性的基礎上,對EEG信號多重分形譜參數的物理意義進行解釋,并通過對比研究,發(fā)現發(fā)作期與間歇期EEG的多重分形特性和譜參數(α0、αmin、αmax、Δα、f(αmin)、 f(αmax)、Δf, R)都具有顯著的統計差異性。最后,將癲癇患者EEG信號的多重分形譜特征與相關向量機相結合,提出一種融合多導聯判決結果的癲癇發(fā)作檢測系統。在對相關向量機輸出的類概率進行后處理的過程中,將多導聯的判決結果進行融合,使其更符合臨床醫(yī)生的診斷過程。該癲癇發(fā)作檢測系統在Freiburg癲癇腦電數據集上進行性能測試,取得了較高的檢測靈敏度和識別率。同時該檢測系統具有較低的計算復雜度,對一小時三導聯EEG進行處理大約只需要1.2分鐘,表現出很好的檢測實時性。本文在對腦電信號單一分形維數的計算中,所采用的Higuchi算法和K近鄰算法,都是直接從信號時域進行,不需要重構相空間,算法簡單,計算復雜度低；而對EEG進行多重分形分析所采用的Moment方法,相對于其他研究領域中常用的多重分形去趨勢波動分析法,也具有物理意義簡單明確,計算量小等優(yōu)點。因此,本文基于EEG的各分形特征建立的癲癇發(fā)作檢測算法,大大降低了EEG分析和特征提取所需的時間,保證檢測算法具有較好的實時性。另外,本文所提出的幾種癲癇發(fā)作檢測算法中,分別采用了貝葉斯線性判別分析、基于集成學習思想的梯度Boosting和基于貝葉斯稀疏學習理論的相關向量機等前沿的學習算法和分類器模型,對腦電模式進行分類識別,從而保證檢測算法具有較高的檢測準確度。因此,本文的研究工作進一步推進了癲癇腦電的非線性特性研究,并且為研究檢測準確度高、實時性好的自動檢測方法,提供了新的思路。本文所提出的癲癇發(fā)作自動檢測算法將在臨床大量癲癇腦電數據上,進行性能驗證與完善。
[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.

【學位授予單位】：山東大學
【學位級別】：博士
【學位授予年份】：2016
【分類號】：R742.1;TN911.6
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本文編號：1808873