本文選題：同步性 + 相關性　； 參考：《杭州電子科技大學》2015年碩士論文

【摘要】：癲癇是一種很常見的神經(jīng)系統(tǒng)疾病，由大腦神經(jīng)元過度同步放電所致。全世界預計超過5000萬人患有癲癇病，其反復性、猝發(fā)性導致患者心智障礙、意外事故、突然死亡等發(fā)生，嚴重危害患者身心健康。如能在癲癇發(fā)作前預測到即將到來的癲癇發(fā)作，即便時間很短，，也有可能使我們對癲癇進行一定的干預治療，使得癲癇發(fā)作造成傷害大大緩解。 研究表明，癲癇發(fā)作是一個隨時間漸變的過程，特別是發(fā)作前的先兆有一定的規(guī)律性，這使癲癇預測成為可能。目前，預測癲癇最常用的方法是分析患者的腦電信號，并應用某些統(tǒng)計學方法進行預測。腦電信號又可分為頭皮腦電信號（EEG）和顱內(nèi)腦電信號（iEEG），相比于EEG信號，iEEG信號不易受偽跡和環(huán)境噪聲的影響，利用iEEG信號進行癲癇預測獲得了廣泛關注。 在本研究中，我們采用植入患者顱內(nèi)的微電極所采集的顱內(nèi)腦電信號（MiE）作為癲癇預測數(shù)據(jù)。這是因為微電極比宏電極更貼近于神經(jīng)元，并且對于神經(jīng)元的活動變化要比宏電極更敏感�；谖㈦姌O的顱內(nèi)腦電信號采集器，必然在揭示癲癇的發(fā)病機制上要比基于宏電極的顱內(nèi)腦電信號采集器更具有優(yōu)勢。 本文研究了在癲癇發(fā)作前，基于微電極的顱內(nèi)腦電信號在四個頻段的相關性和相位的同步問題，這四個頻段為：(1-30HZ)、(30-80HZ)、鏈波(80-250HZ)以及快速鏈波（250HZ）。研究發(fā)現(xiàn)，癲癇發(fā)作前，波和鏈波頻段的相關性和相位同步具有遞增趨勢，其過程持續(xù)時間從幾秒鐘到幾分鐘不等。這一發(fā)現(xiàn)與目前基于宏電極的腦電信號研究所公認的癲癇發(fā)作前同步性降低的結果相反。這一發(fā)現(xiàn)表明，臨床宏電極所觀察到的癲癇信號是由病灶周圍大量神經(jīng)元在癲癇發(fā)作時同步放電產(chǎn)生的，同時該結果也支持微觀癲癇領域的漸進聚結假說。對早期微觀癲癇的檢測可為即將來臨的癲癇發(fā)作實施干預治療提供重要的依據(jù)。
[Abstract]:Epilepsy is a common neurological disease caused by excessive synchronous discharges of brain neurons. More than 50 million people around the world are expected to suffer from epilepsy, whose recurrence and sudden onset lead to mental disorders, accidents, sudden deaths, and so on, seriously endangering the physical and mental health of patients. If we can predict the coming epileptic seizure before the seizure, even if the time is very short, it is possible for us to intervene in the treatment of epilepsy, so that the injury caused by epileptic seizure can be greatly alleviated. It has been shown that epileptic seizures are a gradual process over time, especially the preictal precursors have certain regularity, which makes it possible to predict epilepsy. At present, the most commonly used method to predict epilepsy is to analyze the EEG of patients and use some statistical methods to predict epilepsy. EEG signals can be divided into scalp EEG signals and intracranial EEG signals. Compared with EEG signals, EEG signals are not easily affected by artifacts and environmental noise, and the use of iEEG signals to predict epilepsy has been paid more and more attention. In this study, we used intracranial electroencephalogram (EEG) collected from microelectrodes implanted into the patient's brain as epileptic prediction data. This is because microelectrodes are closer to neurons than macro electrodes and are more sensitive to changes in neuronal activity than macro electrodes. Intracranial EEG collector based on microelectrode must have more advantages in revealing the pathogenesis of epilepsy than that based on macro electrode. The correlation and phase synchronization of intracranial EEG signals based on microelectrode in the four frequency bands before seizure were studied in this paper. The four frequency bands are: 1-30 HZ, 30-80 HZ, 80-250 HZ) and 250 HZ fast wave. It is found that the correlation and phase synchronization of the wave and chain waves have an increasing trend before seizure and the duration of the process varies from a few seconds to a few minutes. This finding contradicts the results of the current macro-electrode-based electroencephalogram (EEG)-based reduction in preepileptic synchrony. The findings suggest that the epileptic signals observed by clinical macro electrodes are generated by the simultaneous discharge of a large number of neurons around the lesion during seizures, and the results also support the progressive aggregation hypothesis in the field of microepilepsy. The detection of early microscopic epilepsy can provide important basis for the intervention treatment of the coming seizure.
【學位授予單位】：杭州電子科技大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：R742.1;TN911.7

【共引文獻】

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1 孔娜;賈文艷;馬駿;高小榕;高上凱;;大鼠癲癇發(fā)作可預測性的研究[J];北京生物醫(yī)學工程;2007年02期

2 歐陽取平;王玉平;;肌陣攣性癲癇26例臨床分析[J];北京醫(yī)學;2009年04期

3 李明愛;崔燕;楊金福;;腦電信號中眼電偽跡自動去除方法的研究[J];電子學報;2013年06期

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6 趙建林;周衛(wèi)東;劉凱;蔡冬梅;;基于SVM和小波分析的腦電信號分類方法[J];計算機應用與軟件;2011年05期

7 劉成;何可人;周天彤;鄒凌;;左右手運動想象腦電模式識別研究[J];常州大學學報(自然科學版);2013年01期

8 姜慧;周霆;;EEG信號動態(tài)演化過程的研究[J];計算機工程;2013年09期

9 楊昌健;鄧趙紅;蔣亦樟;王士同;;基于遷移學習的癲癇EEG信號自適應識別[J];計算機科學與探索;2014年03期

10 李志萍;;基于支持向量機的多通道癲癇發(fā)作預測[J];計算機工程;2014年02期

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