本文選題：癲癇 + 皮層腦電信號(hào)(ECoG)�。� 參考：《電子科技大學(xué)》2017年碩士論文

【摘要】：癲癇是一種神經(jīng)系統(tǒng)疾病,癲癇病人發(fā)作時(shí)會(huì)有一系列臨床表現(xiàn),有些行為會(huì)對(duì)癲癇病人有一定的損害,如燒傷、淹死、骨折、事故、死亡等等。隨著現(xiàn)代醫(yī)學(xué)的發(fā)展和進(jìn)步,許多癲癇學(xué)家開(kāi)始研究癲癇,治療癲癇的方法和手段也隨之增多,許多癲癇患者因此而得到治療恢復(fù)了正常的生活。雖然現(xiàn)代醫(yī)學(xué)的發(fā)展并沒(méi)有解決癲癇病人的所有問(wèn)題,但是,人們?nèi)匀粚?duì)全面控制癲癇充滿了希望。近十年來(lái),通過(guò)對(duì)臨床腦電信號(hào)的分析,人們對(duì)大腦活動(dòng)的理解從大腦活動(dòng)的不同點(diǎn)的鑒別到包含信息處理和任務(wù)機(jī)制的大腦網(wǎng)絡(luò)的鑒別上。大量研究證明,腦網(wǎng)絡(luò)方法能夠很好的定位癲癇,因此本文研究了癲癇腦網(wǎng)絡(luò)的癲癇灶定位方法,同樣驗(yàn)證了腦網(wǎng)絡(luò)方法對(duì)定位的有效性,并通過(guò)癲癇腦網(wǎng)絡(luò)機(jī)制分析探索癲癇的發(fā)作機(jī)制。另外對(duì)于一些難治性癲癇病人來(lái)說(shuō),研究出一種精確有效的癲癇預(yù)測(cè)方法是很重要的,現(xiàn)有的癲癇預(yù)測(cè)方法由于數(shù)據(jù)庫(kù)的限制和算法本身的缺陷至今沒(méi)有一種能有效應(yīng)用于臨床的算法,因此本文提出了一種基于癲癇腦網(wǎng)絡(luò)的癲癇預(yù)測(cè)方法,此方法能夠準(zhǔn)確預(yù)測(cè)癲癇發(fā)作。本文圍繞癲癇發(fā)作區(qū)域定位和癲癇預(yù)測(cè)等兩個(gè)方面,深入地開(kāi)展了如下三項(xiàng)內(nèi)容的研究:首先,本文對(duì)癲癇腦網(wǎng)絡(luò)理論進(jìn)行分析研究,為癲癇發(fā)作區(qū)域(Seizure Onset Zone,SOZ)定位和癲癇預(yù)測(cè)提供基礎(chǔ)。然后,本文研究了基于癲癇腦網(wǎng)絡(luò)的癲癇發(fā)作區(qū)域定位方法。利用有向傳遞函數(shù)方法(Directed Transfer Function,DTF)分析皮層腦電信號(hào)(Electrocorticogram,ECoG)之間的連接性,并利用圖論方法(度中心性、中介中心性和接近中心性)計(jì)算網(wǎng)絡(luò)屬性,根據(jù)癲癇發(fā)作前后不同電極位置網(wǎng)絡(luò)屬性的變化定位癲癇發(fā)作區(qū)域。利用該方法對(duì)三個(gè)臨床癲癇病人的7個(gè)發(fā)作數(shù)據(jù)進(jìn)行了實(shí)驗(yàn)驗(yàn)證,結(jié)果表明了此方法在定位上的有效性。最后,根據(jù)發(fā)作前后的癲癇腦網(wǎng)絡(luò)特征,提出了一種基于癲癇腦網(wǎng)絡(luò)的癲癇預(yù)測(cè)方法,此方法利用支持向量機(jī)(Support Vector Machine,SVM)對(duì)三個(gè)臨床病人7個(gè)癲癇發(fā)作數(shù)據(jù)的三個(gè)時(shí)期進(jìn)行分類處理,三個(gè)病人的平均敏感性、特異性和準(zhǔn)確性分別為94.82%、91.55%和93.14%,預(yù)測(cè)時(shí)間分別為35秒,60秒和85秒。進(jìn)一步把此方法應(yīng)用在頭皮腦電數(shù)據(jù)庫(kù)中,計(jì)算了數(shù)據(jù)庫(kù)中的四個(gè)癲癇病人的數(shù)據(jù),敏感性、特異性和準(zhǔn)確性的平均值分別為73.70%、88.32%和80.54%。研究結(jié)果表明基于癲癇網(wǎng)絡(luò)的方法在皮層腦電的定位和預(yù)測(cè)方面特異性較強(qiáng),但是對(duì)頭皮腦電的預(yù)測(cè)效果較差。本文通過(guò)對(duì)基于癲癇腦網(wǎng)絡(luò)的癲癇定位和預(yù)測(cè)研究,得到的成果為今后癲癇機(jī)制分析和預(yù)測(cè)準(zhǔn)確性的研究提供了基礎(chǔ)和研究的方向。同時(shí),本文提出的癲癇預(yù)測(cè)算法有一定的工程應(yīng)用前景。
[Abstract]:Epilepsy is a nervous system disease, epileptic patients will have a series of clinical manifestations, some behavior will have certain damage to epileptic patients, such as burns, drowning, fractures, accidents, deaths and so on. With the development and progress of modern medicine, many epileptists have begun to study epilepsy, and the methods and means of treating epilepsy have increased, and many epileptic patients have been treated and returned to normal life. Although the development of modern medicine does not solve all the problems of epileptic patients, there is still hope for full control of epilepsy. In the past decade, through the analysis of clinical EEG, people's understanding of brain activity is from the different points of brain activity to the identification of brain network which includes information processing and task mechanism. A large number of studies have proved that the brain network method can locate epilepsy very well, so this paper studies the epileptic focus localization method of epileptic brain network, and also verifies the effectiveness of brain network method in localization. The seizure mechanism of epilepsy was explored by analyzing the mechanism of epileptic brain network. In addition, for some patients with refractory epilepsy, it is important to develop an accurate and effective method for predicting epilepsy. Due to the limitations of the database and the shortcomings of the algorithm, the existing epileptic prediction methods have not been effectively applied to clinical applications. Therefore, a epileptic prediction method based on epileptic brain network is proposed in this paper. This method can accurately predict epileptic seizures. This paper focuses on two aspects of epileptic seizure location and epileptic prediction, in-depth research on the following three aspects: first, this paper analyzes the theory of epileptic brain network. To provide the basis for the location and prediction of epileptic seizure area (Seizure Onset Zonesoz). Then, this paper studies the epileptic seizure region location method based on epileptic brain network. Using directed transfer function (DTF) to analyze the connectivity between electrocorticogram (ECoG), and using graph theory (degree centrality, intermediary centrality and proximity centrality) to calculate the network properties. According to the changes of the network properties of different electrode positions before and after epileptic seizure, the epileptic seizure region was located. Seven seizure data of three clinical epileptic patients were tested by this method, and the results showed that the method was effective in localization. Finally, according to the characteristics of epileptic brain network before and after seizure, a epileptic prediction method based on epileptic brain network is proposed. In this method, support Vector Machine (SVM) was used to classify the three periods of 7 epileptic seizures in three clinical patients, and the average sensitivity of the three patients was obtained. The specificity and accuracy were 91.55% and 93.14%, respectively, and the predicted time was 35 seconds, 60 seconds and 85 seconds, respectively. Furthermore, the method was applied to the scalp EEG database. The data of four epileptic patients in the database were calculated. The average values of sensitivity, specificity and accuracy were 73.7088.32% and 80.54%, respectively. The results show that the method based on epileptic network is more specific in the localization and prediction of cortical EEG, but it has a poor prediction effect on scalp EEG. In this paper, the localization and prediction of epilepsy based on epileptic brain network are studied. The results provide the basis and research direction for the analysis and prediction accuracy of epilepsy mechanism in the future. At the same time, the epileptic prediction algorithm proposed in this paper has a certain engineering application prospect.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：R742.1;O157.5

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