【摘要】：癲癇作為僅次于腦血管疾病的第二大腦部疾病,不僅對(duì)患者的身心造成巨大的傷害,而且對(duì)患者所處的家庭和社會(huì)也會(huì)產(chǎn)生巨大的影響,所以癲癇的診斷和治療一直是臨床醫(yī)生和神經(jīng)信息學(xué)家特別關(guān)注的領(lǐng)域。本文從神經(jīng)信息工程學(xué)的角度,結(jié)合癲癇手術(shù)中對(duì)皮層腦電分析的需要,從癲癇發(fā)作的檢測(cè)、癲癇病灶定位、大腦功能區(qū)定位和機(jī)制等方面進(jìn)行了研究,主要包括以下幾個(gè)方面:首先,提出使用基于遞歸圖的最大熵率方法對(duì)癲癇發(fā)作進(jìn)行自動(dòng)標(biāo)記,通過(guò)對(duì)多通道的皮層腦電信號(hào)進(jìn)行相空間分布的信息熵估計(jì),實(shí)現(xiàn)對(duì)癲癇發(fā)作期數(shù)據(jù)進(jìn)行自動(dòng)標(biāo)記。對(duì)具有個(gè)體化差異的多名病人的發(fā)作間期和發(fā)作期的數(shù)據(jù)進(jìn)行分析,結(jié)果表明該方法可將發(fā)作期數(shù)據(jù)和發(fā)作間期數(shù)據(jù)準(zhǔn)確區(qū)分出來(lái)。這表明該方法可有效的應(yīng)用到臨床,用于癲癇發(fā)作的自動(dòng)標(biāo)記。其次,提出使用基于譜分解的電流源密度分析方法對(duì)癲癇患者的皮層腦電信號(hào)進(jìn)行電流源分析,用于癲癇病灶的檢測(cè)。結(jié)果表明,相對(duì)于傳統(tǒng)的電流源密度分析方法,基于譜分解的電流源密度分析方法能得到更穩(wěn)定和更準(zhǔn)確的結(jié)果,并且可以將結(jié)果按照腦電的頻率信息進(jìn)行區(qū)分,更好的理解癲癇病灶的放電模式。另外發(fā)現(xiàn)發(fā)作間期數(shù)據(jù)和發(fā)作期數(shù)據(jù)得到的結(jié)果有較高的一致性,這表明基于譜分解的電流源密度分析方法能不依賴(lài)發(fā)作期數(shù)據(jù)即可進(jìn)行癲癇病灶定位。然后,提出了基于蒙特卡洛的小波相干方法對(duì)單任務(wù)皮層腦電信號(hào)進(jìn)行時(shí)頻腦皮層網(wǎng)絡(luò)構(gòu)建,用于定位大腦功能區(qū),并根據(jù)臨床上對(duì)于實(shí)時(shí)性的要求,對(duì)該方法使用GPGPU技術(shù)進(jìn)行了大規(guī)模并行化設(shè)計(jì)。結(jié)果表明,使用蒙特卡洛小波相干方法可有效的對(duì)大腦高級(jí)認(rèn)知功能區(qū)進(jìn)行定位,GPU引入后,蒙特卡洛小波相干方法的計(jì)算速度得到了巨大的提升,使得該方法可用于臨床上對(duì)大腦功能區(qū)的實(shí)時(shí)定位。更進(jìn)一步,開(kāi)發(fā)了針對(duì)于臨床醫(yī)生的癲癇手術(shù)輔助工具包。該工具包的核心為大腦三維可視化系統(tǒng),針對(duì)當(dāng)前臨床醫(yī)生對(duì)于手術(shù)規(guī)劃的需要,包含了帶有靈活接口的大腦模型顯示、顱內(nèi)電極定位、大腦功能繪圖、腦網(wǎng)絡(luò)連接可視化等功能,并且解決了現(xiàn)有電極定位技術(shù)中所存在的位置偏差的問(wèn)題。工具包具有友好的界面,并且具有較高的執(zhí)行效率,可適應(yīng)臨床上對(duì)軟件響應(yīng)的要求。該工具包可以很好的為臨床醫(yī)生提供手術(shù)規(guī)劃、術(shù)中分析、術(shù)后評(píng)估等功能。最后,提出基于諧小波的冪率分析方法,使用臨界理論,對(duì)癲癇發(fā)展的機(jī)制進(jìn)行了初步的研究。基于皮層腦電,對(duì)癲癇患者的癲癇發(fā)展過(guò)程進(jìn)行了分析,結(jié)果表明,對(duì)于大腦系統(tǒng),癲癇的發(fā)作是將大腦系統(tǒng)從一個(gè)非穩(wěn)定狀態(tài)向穩(wěn)定狀態(tài)改變的過(guò)程,癲癇發(fā)作后的恢復(fù)是將大腦系統(tǒng)從一個(gè)穩(wěn)定系統(tǒng)重新調(diào)整到非穩(wěn)定系統(tǒng)的過(guò)程,并且在癲癇發(fā)作之前,大腦系統(tǒng)的臨界狀態(tài)就會(huì)發(fā)生改變。臨界理論分析是一個(gè)新的描述神經(jīng)信號(hào)動(dòng)態(tài)變化的工具,可以很好的用于理解癲癇的發(fā)展過(guò)程,也可以有效的對(duì)癲癇發(fā)作進(jìn)行預(yù)測(cè)。
文內(nèi)圖片：
圖片說(shuō)明：癲癇發(fā)作臨床表現(xiàn)與EEGFig.1-1EpilepticseizureandEEG
[Abstract]:Epilepsy, as the second major brain disease following the cerebrovascular disease, not only causes great harm to the body and mind of the patient, but also has a great impact on the family and society in which the patient is located, The diagnosis and treatment of epilepsy has been an area of special interest to clinicians and neuroscientists. This paper, from the angle of neuroinformation engineering, combined with the need of the analysis of cortical EEG in the course of epilepsy, has carried out the research from the aspects of the detection of the seizure, the location of the epileptic focus, the positioning and the mechanism of the function of the brain, and mainly includes the following aspects: first, It is proposed to use the maximum entropy rate method based on the recursive graph to automatically mark the seizure, and the data of the seizure period can be automatically marked by the information entropy estimation of the phase space distribution of the multi-channel cortical brain electrical signal. The data of the onset interval and the episode period of multiple patients with individual difference were analyzed. The results showed that the method can accurately distinguish the episode data and the episode interval data. This indicates that the method can be effectively applied to the clinic for the automatic marking of the seizure. Secondly, the current source analysis of the cortical and brain electrical signals of the epileptic patients was put forward using the current source density analysis method based on the spectrum decomposition, which was used for the detection of the epileptic foci. The results show that the current source density analysis method based on the spectrum decomposition can be more stable and more accurate with respect to the conventional current source density analysis method, and the result can be distinguished according to the frequency information of the EEG, and the discharge mode of the epileptic focus can be better understood. It is also found that the results obtained from the interval data and the episode data have higher consistency, which indicates that the current source density analysis method based on the spectrum decomposition can be used for the location of the epileptic focus without relying on the episode data. Then, a Monte Carlo-based wavelet coherence method is proposed to construct the cortical network of the brain cortex of the single-task cortex, which is used to locate the functional area of the brain, and based on the requirements of the real-time performance, the large-scale parallelization design is carried out using the GPGPU technology. The results show that the Monte-Carlo wavelet coherence method can effectively position the high-level cognitive function area of the brain, and the calculation speed of the Monte-Carlo wavelet coherence method is greatly improved after the GPU is introduced, so that the method can be used for real-time positioning of the functional area of the brain. Further, an epileptic surgical aid kit for a clinician is developed. The core of the kit is the brain three-dimensional visualization system, And the problem of the position deviation existing in the prior electrode positioning technology is solved. The kit has a friendly interface and has a high execution efficiency and can be adapted to the requirements of the software response clinically. The kit can provide the clinician with the functions of operation planning, intraoperative analysis, post-operation assessment and the like. Finally, a power-rate analysis method based on harmonic wavelet is proposed, and the mechanism of the development of epilepsy is studied by using the critical theory. The results show that, for the brain system, the onset of the seizure is the process of changing the brain system from one non-stable state to the steady state, The recovery after a seizure is a process of realigning the brain system from a stabilization system to a non-stable system, and the critical state of the brain system changes before the onset of the seizure. The critical theory analysis is a new tool for describing the dynamic change of the nerve signal, which can be used to understand the development process of the epilepsy, and can also effectively predict the seizure.
【學(xué)位授予單位】：燕山大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2015
【分類(lèi)號(hào)】：R651.1

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