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(jiàn)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(duì)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(cè)主要通過(guò)有經(jīng)驗(yàn)的臨床醫(yī)生對(duì)腦電圖進(jìn)行視覺(jué)檢查來(lái)進(jìn)行診斷,但是海量的腦電數(shù)據(jù)使得傳統(tǒng)的檢測(cè)方法十分耗時(shí),而且主觀性強(qiáng)。于是,癲癇性發(fā)作的自動(dòng)檢測(cè)成為近年來(lái)的一個(gè)熱門(mén)問(wèn)題。而實(shí)現(xiàn)自動(dòng)檢測(cè)的關(guān)鍵問(wèn)題則在于設(shè)計(jì)有效的特征提取方法�；诖�,本論文主要對(duì)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(jī)與支撐向量機(jī)完成自動(dòng)檢測(cè)。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(dòng)檢測(cè)的研究背景、檢測(cè)流程以及國(guó)內(nèi)外的研究現(xiàn)狀;第二章主要介紹了腦電信號(hào)的相關(guān)知識(shí)和癲癇性發(fā)作自動(dòng)檢測(cè)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(guó)波恩大學(xué)癲癇疾病研究中心的公開(kāi)數(shù)據(jù)集中,通過(guò)數(shù)值實(shí)驗(yàn)驗(yàn)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(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：R742.1;TN911.6

【參考文獻(xiàn)】

相關(guān)期刊論文 前6條

1 鄧澤懷;劉波波;李彥良;;常見(jiàn)的功率譜估計(jì)方法及其Matlab仿真[J];電子科技;2014年02期

2 蔡冬梅;周衛(wèi)東;劉凱;李淑芳;耿淑娟;;基于Hurst指數(shù)和SVM的癲癇腦電檢測(cè)方法[J];中國(guó)生物醫(yī)學(xué)工程學(xué)報(bào);2010年06期

3 彭建華,劉延柱;腦科學(xué)中若干非線性動(dòng)力學(xué)問(wèn)題[J];力學(xué)進(jìn)展;2003年03期

4 劉慧婷,程家興,張e，

本文編號(hào)：1808771