本文選題：癲癇 + 改進(jìn)Hjorth參數(shù)�。� 參考：《西北大學(xué)》2017年碩士論文

【摘要】：癲癇是最常見的大腦神經(jīng)紊亂疾病之一,因其發(fā)作的突發(fā)性和反復(fù)性,對患者的生理和心理都造成巨大傷害,嚴(yán)重危害人們的正常生活。傳統(tǒng)的癲癇檢測主要通過有經(jīng)驗的臨床醫(yī)生對腦電圖進(jìn)行視覺檢查來進(jìn)行診斷,但是海量的腦電數(shù)據(jù)使得傳統(tǒng)的檢測方法十分耗時,而且主觀性強。于是,癲癇性發(fā)作的自動檢測成為近年來的一個熱門問題。而實現(xiàn)自動檢測的關(guān)鍵問題則在于設(shè)計有效的特征提取方法�；诖�,本論文主要對特征提取方法進(jìn)行研究,提出一種新的癲癇腦電融合特征提取方法,并結(jié)合超限學(xué)習(xí)機與支撐向量機完成自動檢測。具體的工作安排如下:第一章系統(tǒng)論述了癲癇性發(fā)作的自動檢測的研究背景、檢測流程以及國內(nèi)外的研究現(xiàn)狀;第二章主要介紹了腦電信號的相關(guān)知識和癲癇性發(fā)作自動檢測中常用的特征提取方法及分類器;第三章基于Hjorth參數(shù)和樣本熵首先分別提出了改進(jìn)的Hjorth參數(shù)特征和二階差分樣本熵,其次將二者結(jié)合提出一種新的融合特征提取方法;第四章將本文提出的新的融合特征應(yīng)用于德國波恩大學(xué)癲癇疾病研究中心的公開數(shù)據(jù)集中,通過數(shù)值實驗驗證本文所提方法的可行性與有效性。
[Abstract]:Epilepsy is one of the most common neurologic disorders of the brain. Because of its sudden and recurrent seizures, it causes great harm to the patients' physiology and psychology, and seriously endangers people's normal life. The traditional epilepsy detection is mainly through the experienced clinicians to make the diagnosis of EEG, but the massive EEG data make the traditional detection methods very time-consuming and subjective. Therefore, the automatic detection of epileptic seizures has become a hot issue in recent years. The key problem of automatic detection is to design an effective feature extraction method. Based on this, this paper mainly studies the feature extraction method, proposes a new feature extraction method of epileptic EEG fusion, and combines the out-of-limits learning machine and support vector machine to complete the automatic detection. The specific work arrangements are as follows: the first chapter systematically discusses the background of the automatic detection of epileptic seizures, detection process and domestic and foreign research status; The second chapter mainly introduces the related knowledge of EEG and the methods of feature extraction and classifier used in the automatic detection of epileptic seizures. In chapter 3, based on Hjorth parameters and sample entropy, the improved Hjorth parameter feature and the second order differential sample entropy are proposed, and then a new fusion feature extraction method is proposed. In chapter 4, the new fusion features proposed in this paper are applied to the open data set of the Research Center for Epilepsy at the University of Bonn, Germany. The feasibility and effectiveness of the proposed method are verified by numerical experiments.
【學(xué)位授予單位】：西北大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：R742.1;TN911.6

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本文編號：1808771