發(fā)布時間：2017-12-28 09:23

  本文關鍵詞：癲癇發(fā)作自動檢測算法研究　出處：《山東大學》2016年博士論文　論文類型：學位論文

  更多相關文章： 腦電信號 癲癇檢測 小波變換 擴散距離 協(xié)作表示 核方法 對稱正定矩陣 稀疏表示

【摘要】：癲癇是一種由腦部神經(jīng)元群陣發(fā)性異常電活動導致的慢性神經(jīng)系統(tǒng)疾病,其發(fā)作具有突發(fā)性、反復性特點,并伴隨意識喪失、昏厥、四肢抽搐等臨床表現(xiàn),嚴重危及人們的身心健康與生命安全。據(jù)統(tǒng)計,全球有超過1%的人口遭受該疾病的困擾。癲癇發(fā)作的病因復雜多樣,發(fā)病機制迄今尚未完全明確。作為研究癲癇疾病的一種重要手段,腦電圖利用電極記錄腦部神經(jīng)細胞的電活動,包含了大量的生理與病理信息,在癲癇的臨床診斷、病灶定位與治療等方面都發(fā)揮著極其重要的作用。目前,腦電圖分析和癲癇檢測主要依靠醫(yī)務人員根據(jù)臨床經(jīng)驗視覺觀察來完成。然而,龐大的數(shù)據(jù)量使得該項工作非�？菰锱c耗時,且醫(yī)務人員的主觀性對癲癇發(fā)作的判斷也會造成影響。因此,利用計算機自動分析腦電信號并有效檢測癲癇發(fā)作是十分迫切和必要的,它不僅可以減輕醫(yī)生的負擔,提高癲癇的診斷效率,而且在有效治療癲癇疾病,改善患者生活質(zhì)量,并深入揭示癲癇發(fā)病機制等方面具有重大意義。本文立足于癲癇自動檢測這一課題進行相關研究,圍繞不同時期腦電信號非相似度距離特征提取,基于稀疏表示和協(xié)作表示理論的腦電信號分類等內(nèi)容展開研究,并提出了幾種有效的癲癇自動檢測算法。本論文的研究內(nèi)容與創(chuàng)新點主要包括以下幾點：(1)將距離測度應用到腦電信號分析中,提出了基于擴散距離與貝葉斯線性判別分析(BLDA)的癲癇發(fā)作檢測算法。該算法應用小波變換對腦電信號進行時頻分析,并將三個頻段的腦電信號組合構(gòu)成腦電分布。然后,計算癲癇發(fā)作期與間歇期腦電分布之間的擴散距離,定量描述不同時期腦電信號之間的差異性。根據(jù)同類別腦電信號之間的差異性低于不同類別腦電信號的原則,將擴散距離作為腦電特征與BLDA分類器相結(jié)合,實現(xiàn)癲癇發(fā)作的識別與檢測。與推土機距離(EMD)相比,擴散距離不僅能有效地區(qū)分發(fā)作期腦電信號和間歇期腦電信號,而且具有更強的抗噪性與更低的計算復雜度。BLDA通過正則化方法避免了在含噪聲的數(shù)據(jù)集上出現(xiàn)過擬合問題,可以得到更好的分類效果。在Freiburg長程腦電數(shù)據(jù)庫上的實驗結(jié)果驗證了腦電信號擴散距離特征的有效性,而且表明該癲癇檢測算法具有良好的分類識別性能。(2)以稀疏表示理論為基礎,提出了一種基于多層核協(xié)作表示分類方法的癲癇自動檢測算法。該算法將腦電信號進行多層小波分解,然后在各層上結(jié)合核方法與協(xié)作表示對子頻帶腦電信號進行分類,并提出一種新穎的判決決策有效融合各層與各導聯(lián)的判斷結(jié)果,最終構(gòu)建出多層核協(xié)作表示分類與檢測系統(tǒng)。在核協(xié)作表示分類框架中,求解最小72范數(shù)優(yōu)化問題得到測試樣本在訓練字典上的稀疏向量并計算兩類訓練樣本集對測試樣本的重構(gòu)誤差,將其差值作為判決變量進行分類識別,避免了傳統(tǒng)檢測算法中的特征選取與分類器設計難題。核函數(shù)的應用增強了腦電信號的可分性,有利于提高算法的分類性能。協(xié)作表示使用l2范數(shù)代替稀疏表示的l1范數(shù)進行求解,在保證分類性能的同時大大降低了算法復雜度。多層的系統(tǒng)設計與判決準則將腦電信號的頻域和空間信息有機結(jié)合,進一步提高了檢測準確性。將該算法在長程腦電數(shù)據(jù)庫上進行測試評估,取得了較理想的檢測靈敏度與較低的誤檢率。實驗結(jié)果表明該算法對癲癇腦電信號具有較好的檢測性能且實時性較高。(3)研究了對稱正定(SPD)矩陣的稀疏表示算法并提出一種Log-Euclidean高斯核稀疏表示分類方法進行癲癇發(fā)作自動檢測。該算法使用協(xié)方差描述子對多導聯(lián)腦電信號建模,并修正協(xié)方差矩陣使之成為SPD矩陣。腦電信號協(xié)方差矩陣形成的空間是一個非線性黎曼流形,應用Log-Euclidean高斯核函數(shù)將其嵌入到線性可再生核希爾伯特空間(RKHS)中進行稀疏表示,并計算重構(gòu)誤差進行分類識別。協(xié)方差描述子結(jié)合了腦電信號在時域、頻域及空間域上的統(tǒng)計特性并能夠有效抑制噪聲。不同于歐氏空間中向量的稀疏表示,Log-Euclidean高斯核函數(shù)考慮了流形數(shù)據(jù)的幾何結(jié)構(gòu),使得SPD矩陣的稀疏表示合理有效。而且,傳統(tǒng)稀疏表示檢測算法需要對每個導聯(lián)的腦電信號迭代處理,具有重復運算且系統(tǒng)設計復雜的缺點。該算法應用SPD矩陣的稀疏表示同時處理多導聯(lián)腦電數(shù)據(jù),成功解決了傳統(tǒng)算法的缺陷,有效降低了算法的復雜度。在長程腦電數(shù)據(jù)庫的實驗結(jié)果表明,該算法不僅具有更加理想的檢測性能,魯棒性較強,且運行速度更快,基本滿足在線檢測系統(tǒng)對檢測準確性與實時性的要求。本論文的工作有助于促進腦電信號分析和癲癇自動檢測算法在理論和臨床實際應用方面的研究,積極有效地推動了癲癇自動檢測技術的發(fā)展。由于實驗數(shù)據(jù)的局限性,本文提出的癲癇自動檢測算法的有效性和魯棒性還有待進一步驗證與提高。
[Abstract]:Epilepsy is a chronic neurological disorder caused by brain neuron group paroxysmal abnormal electrical activity, its onset is sudden and repeated characteristics, accompanied by loss of consciousness, fainting, twitching limbs and other clinical manifestations, seriously endanger people's health and life safety. According to statistics, more than 1% of the population in the world is suffering from the disease. The causes of epileptic seizures are complex and diverse, and the pathogenesis has not yet been fully defined. As an important means to study epilepsy, EEG uses electrodes to record electrical activity of brain neurons, which contains a lot of physiological and pathological information. It plays an extremely important role in clinical diagnosis, location and treatment of epilepsy. At present, electroencephalogram analysis and epileptic detection are mainly done by medical staff according to clinical experience visual observation. However, the large amount of data makes the work very boring and time-consuming, and the subjectivity of the medical staff will also affect the judgment of epileptic seizures. Therefore, the use of computer automatic EEG signal analysis and detection of seizures is very urgent and necessary, it can not only reduce the burden on doctors, improve the diagnostic efficiency and effectiveness in the treatment of epilepsy, seizure disorders, improve the quality of life of patients, and reveal the pathogenesis of epilepsy is of great significance. This paper is based on the automatic detection of epilepsy related research on this topic, on different stages of extraction of similarity distance characteristics of non electric signal, sparse representation and cooperation study theory of EEG signal classification based on content, and puts forward several effective method for automatic detection of epilepsy. The research contents and innovations of this paper include the following points: (1) applying distance measure to EEG analysis, a epileptic seizure detection algorithm based on diffusion distance and Bayesian linear discriminant analysis (BLDA) is proposed. The algorithm uses wavelet transform to analyze the time frequency of EEG and combine the EEG signals of three bands to form the EEG distribution. Then, the diffusion distance between epileptic and intermittent EEG distribution is calculated, and the difference between EEG signals in different periods is quantitatively described. According to the principle that the difference of EEG signals between the same kind is lower than the different kinds of EEG signals, the diffusion distance is used as the combination of EEG features and BLDA classifier to realize the recognition and detection of epileptic seizures. Compared with bulldozer distance (EMD), diffusion distance can not only effectively distribute EEG and intermittent EEG, but also has stronger noise immunity and lower computational complexity. Through the regularization method, BLDA avoids the over fitting problem in the noisy data set, and can get better classification effect. Experimental results on Freiburg long range electroencephalogram database verify the validity of EEG diffusion distance characteristics, and show that the epileptic detection algorithm has good classification and recognition performance. (2) based on the sparse representation theory, an automatic detection algorithm for epilepsy based on multi-layer kernel cooperative representation classification is proposed. The algorithm divides the EEG wavelet decomposition, and then combined with the kernel method and cooperation on band EEG signal classification in each layer, and proposes a novel effective decision making fusion judgment results of each layer and each lead, and ultimately build a multi nuclear cooperation in classification and detection system. In the nuclear cooperation classification framework, minimum 72 norm optimization problem to get the test samples in the training dictionary sparse vectors and calculate the two kinds of training sample set the reconstruction error of the test sample, the difference as the decision variables for classification, to avoid the traditional detection algorithm of feature selection and classifier design problem. The application of kernel function enhances the separability of EEG signal, which is helpful to improve the classification performance of the algorithm. The cooperative representation uses the L2 norm instead of the L1 norm of sparse representation to solve the problem, which greatly reduces the complexity of the algorithm while ensuring the performance of the classification. Multi-layer system design and decision criteria combine the frequency domain and spatial information of EEG signal, and further improve the detection accuracy. The algorithm is tested and evaluated on the long range EEG database, and the ideal detection sensitivity and low false detection rate are obtained. The experimental results show that the algorithm has good detection performance and high real-time performance for epileptic EEG. (3) the sparse representation algorithm of symmetric positive definite (SPD) matrix is studied, and a Log-Euclidean Gauss kernel sparse representation classification method is proposed for automatic detection of epileptic seizures. The covariance descriptor is used to model the multi lead EEG, and the covariance matrix is modified to make it a SPD matrix. The space formed by the covariance matrix of EEG is a nonlinear Riemann manifold. The Log-Euclidean Gauss kernel function is applied to the sparse representation in the linear regenerative kernel Hilbert space (RKHS), and the reconstruction error is calculated for classification and recognition. Covariance descriptors combine the statistical characteristics of EEG in time domain, frequency domain and space domain, and can effectively suppress noise. Unlike the sparse representation of vectors in Euclidean space, the Log-Euclidean Gauss kernel function takes into account the geometric structure of manifold data, making the sparse representation of SPD matrix reasonable and effective. Moreover, the traditional sparse representation detection algorithm needs to iterate over the EEG signals of each lead, which has the disadvantages of repeated operation and complex design of the system. The algorithm uses sparse representation of SPD matrix to deal with multi lead EEG data simultaneously. The algorithm has successfully solved the defects of the traditional algorithm and effectively reduces the complexity of the algorithm. Experimental results in long range EEG database show that the algorithm has better detection performance, robustness and faster operation, and basically meets the accuracy and practicality of online detection system.
【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;TN911.7
，

本文編號：1345476