本文關(guān)鍵詞： 灶點(diǎn)定位 動(dòng)態(tài)源成像 功能連接 黎曼流形 癲癇網(wǎng)絡(luò)動(dòng)態(tài)　出處：《浙江大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：癲癇是中國(guó)當(dāng)前第二大神經(jīng)科疾病,由神經(jīng)元放電異常引發(fā),發(fā)作時(shí)患者會(huì)出現(xiàn)痙攣、意識(shí)喪失等癥狀,對(duì)患者的生理和心理都會(huì)造成嚴(yán)重的影響。目前常見(jiàn)的治療手段為藥物治療、手術(shù)治療、電刺激等,約70%的患者能夠通過(guò)藥物治療得到有效控制,剩下的藥物難治性癲癇患者需要手術(shù)切除治療或者電刺激治療。在手術(shù)切除治療中,癲癇灶點(diǎn)定位是臨床手術(shù)前極其重要的診斷步驟。由于癲癇發(fā)病原因和發(fā)作機(jī)制十分復(fù)雜,診斷和治療非常困難。隨著電子工藝、及計(jì)算機(jī)信息處理技術(shù)的發(fā)展,利用信號(hào)處理手段進(jìn)行癲癇灶點(diǎn)定位的研究正在逐步興起。頭皮腦電(sEEG)空間覆蓋率高,采集方便無(wú)植入式創(chuàng)傷,但是空間分辨率較低,適合用于粗略定位癲癇灶點(diǎn)區(qū)域;皮層腦電(iEEG)的時(shí)空分辨率高,但是空間覆蓋率低且需要開顱手術(shù),適合用于在初步定位的區(qū)域植入電極后精確定位癲癇灶點(diǎn)。本文主要圍繞基于腦電信號(hào)(EEG)的局灶性癲癇灶點(diǎn)定位問(wèn)題,著重探索基于頭皮腦電(sEEG)動(dòng)態(tài)源成像的癲癇灶點(diǎn)粗定位和基于皮層腦電(GEEG)黎曼流形的癲癇灶點(diǎn)精定位兩個(gè)子問(wèn)題。(1)sEEG信號(hào)采集點(diǎn)都是標(biāo)準(zhǔn)的電極位置,首先使用獨(dú)立成分分析將sEEG信號(hào)分離成頭皮表面的相互獨(dú)立的信號(hào)成分,然后利用癲癇信號(hào)成分與非癲癇信號(hào)成分在時(shí)頻特征上的差異挑選出癲癇信號(hào)成分,最后根據(jù)用邊界元法構(gòu)造的三層三維電子大腦模型將癲癇信號(hào)成分逆向映射回皮層的源信號(hào),從而完成癲癇灶點(diǎn)定位。在源映射的過(guò)程中,我們根據(jù)大腦結(jié)構(gòu)和生理機(jī)制的特點(diǎn)添加了平滑約束。(2)在通過(guò)sEEG初步定位的癲癇灶點(diǎn)區(qū)域,埋入植入式電極采集皮層腦電(iEEG)。首先計(jì)算表征通道之間相互關(guān)系的功能連接矩陣,然后根據(jù)功能連接矩陣分析癲癇網(wǎng)絡(luò)與灶點(diǎn)有關(guān)的空間上的特征和時(shí)間上的特征。在分析癲癇網(wǎng)絡(luò)動(dòng)態(tài)時(shí)通過(guò)引入自定義度量-對(duì)數(shù)密度比ρ1發(fā)現(xiàn)癲癇發(fā)作期后半段與發(fā)作前期和發(fā)作期前半段有顯著的差異,為我們將發(fā)作期聚類成兩個(gè)狀態(tài),并取后一個(gè)狀態(tài)進(jìn)行灶點(diǎn)定位提供依據(jù)。我們提出了基于黎曼距離的k-means聚類算法,利用整個(gè)功能連接矩陣而不是有損壓縮的特征向量中心性等進(jìn)行狀態(tài)聚類。最后我們基于聚類得到的后一個(gè)狀態(tài)進(jìn)行灶點(diǎn)定位,取得了比較理想的效果。
[Abstract]:Epilepsy is currently the second largest neurologic disease in China. It is caused by abnormal firing of neurons, and when the seizure occurs, the patient will have symptoms such as spasm, loss of consciousness, etc. At present, the common treatment methods are drug therapy, surgical treatment, electrical stimulation, etc. About 70% of the patients can be effectively controlled through drug therapy. The remaining patients with drug-refractory epilepsy need surgical resection or electrical stimulation. In surgical resection, the location of epileptic foci is an extremely important diagnostic step before clinical operation. Because of the complexity of the cause and mechanism of epilepsy, Diagnosis and treatment are very difficult. With the development of electronic technology and computer information processing technology, the study of epileptic foci location using signal processing means is gradually rising. The acquisition is convenient without implantation trauma, but the spatial resolution is low, which is suitable for rough location of epileptic foci. The cortical EEG has high spatial and temporal resolution, but the space coverage is low and the craniotomy is required. This paper focuses on the localization of focal epileptic foci based on EEG-based EEG. The two sub-problems of epileptic focal point coarse location based on scalp EEG dynamic source imaging and epileptic focal fine location based on EMG Riemann manifold are all standard electrode positions. Firstly, the sEEG signal was separated into independent signal components on the scalp surface by independent component analysis (ICA), and then the epileptic signal components were selected by using the difference between the epileptic signal components and the non-epileptic signal components in time-frequency characteristics. Finally, according to the three-layer three-dimensional electronic brain model constructed by the boundary element method, the epileptic signal components are reversely mapped back to the source signal of the cortex to locate the epileptic foci. According to the characteristics of brain structure and physiological mechanism, we added smoothing constraint. 2) in the region of epileptic foci preliminarily located by sEEG, we embedded an implanted electrode to collect EEGG. First, we calculated the functional connection matrix, which was used to characterize the relationship between channels. Then, the spatial and temporal characteristics of epileptic network related to focal points are analyzed according to the functional connection matrix. In the analysis of epileptic network dynamics, the seizure period is found by introducing the custom metric logarithmic density ratio 蟻 1. There were significant differences between the half segment and the preictal and the first half of the attack. It provides the basis for us to cluster the seizure period into two states, and then take the latter state to locate the focal point. We propose a k-means clustering algorithm based on Riemann distance. The whole functional connection matrix rather than the feature vector centrality of lossy compression is used for state clustering. Finally, we use the latter state to locate the focal point, and obtain a better result.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：R742.1;TN911.7

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