本文關(guān)鍵詞： 癲癇腦電 改進(jìn)多元多尺度熵 小波包分解 分類　出處：《燕山大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：癲癇是一種常見的神經(jīng)系統(tǒng)疾病，約80%患者在發(fā)病時(shí)伴有癲癇樣放電，這種放電也是目前癲癇診斷的主要依據(jù)。為正確診斷癲癇病癥，往往需要對病患進(jìn)行長時(shí)間的腦電監(jiān)測。腦電信號數(shù)據(jù)量龐大，導(dǎo)致對腦電信號的分析判別成為一項(xiàng)繁重而又低效的工作。癲癇檢測結(jié)果容易受到醫(yī)生主觀因素的影響，因此對癲癇腦電信號的自動分類識別就顯得尤為重要。 在研究國內(nèi)外癲癇腦電信號常用分析方法后，重點(diǎn)介紹了改進(jìn)多元多尺度熵算法的發(fā)展過程。多元多尺度熵作為多尺度熵在多元信號上的推廣，是非線性動態(tài)相關(guān)性的一種反映。但是傳統(tǒng)的多元多尺度熵計(jì)算量大，，對于通道數(shù)較多的系統(tǒng)需要耗費(fèi)大量的時(shí)間和空間，并且無法準(zhǔn)確的反應(yīng)變量間的相關(guān)性。本文提出的改進(jìn)多元多尺度熵，將傳統(tǒng)多元多尺度熵針對單個變量的嵌入模式改為對所有變量同時(shí)嵌入，不但解決了通道數(shù)增加內(nèi)存溢出問題，也更適用于實(shí)際多變量信號分析。 本文中應(yīng)用改進(jìn)多元多尺度熵與小波包分解方法對癲癇腦電信號進(jìn)行分類。癲癇腦電信號分類算法大致可分為時(shí)域、頻域、時(shí)頻和非線性域。其中時(shí)頻分析中的小波包變換不僅能夠反映信號的頻率特性又能很好表征信號的局部信息，但是利用小波包特征進(jìn)行分類需要耗費(fèi)大量的時(shí)間和空間。本文提出的改進(jìn)多元多尺度熵不僅保有原來多元多尺度熵對多通道數(shù)據(jù)并行處理、多尺度分析等特點(diǎn)，還大大降低了原有方法的復(fù)雜度與計(jì)算冗余。通過對癲癇腦電信號進(jìn)行多次尺度分解，將改進(jìn)多元多尺度熵與小波包變換結(jié)合，對其進(jìn)行統(tǒng)計(jì)分析與分類。該方法既避免了由于特征數(shù)據(jù)量大而引發(fā)的大量時(shí)空消耗，又避免了傳統(tǒng)時(shí)頻分析中高頻信號的干擾，更有利于實(shí)際應(yīng)用。對于改進(jìn)多元多尺度熵，針對GAERS大鼠癲癇腦電和波恩癲癇腦電數(shù)據(jù)進(jìn)行實(shí)驗(yàn)，結(jié)果表明該方法能夠有效提取癲癇腦電特征，具有很好的統(tǒng)計(jì)特性和分類精度。
[Abstract]:Epilepsy is a common nervous system disease, about 80% patients in the onset accompanied by epileptoid discharge, this discharge is also the main basis for the diagnosis of epilepsy, for the correct diagnosis of epilepsy. Patients often need to be monitored for a long period of time, EEG data volume is huge. As a result, the analysis and discrimination of EEG signal becomes a heavy and inefficient work. The results of epilepsy detection are easily affected by the subjective factors of doctors. Therefore, the automatic classification and recognition of epileptic EEG signals is particularly important. After studying the common analysis methods of epileptic EEG signals at home and abroad, the development process of improved multiscale entropy algorithm is introduced emphatically, which is the extension of multiscale entropy in multivariate signals. It is a reflection of nonlinear dynamic correlation, but the traditional multivariate multi-scale entropy calculation is large, and it needs a lot of time and space for the system with more channels. And the correlation between variables can not be accurately reflected. This paper proposes an improved multivariate multi-scale entropy, the traditional multi-scale entropy for a single variable embedding model instead of all variables at the same time. It not only solves the problem of increasing memory overflow, but also is more suitable for multivariable signal analysis. In this paper, improved multi-scale entropy and wavelet packet decomposition are used to classify epileptic EEG signals, which can be divided into time domain and frequency domain. The wavelet packet transform in time-frequency analysis can not only reflect the frequency characteristics of the signal but also represent the local information of the signal. However, using wavelet packet features to classify requires a lot of time and space. The improved multivariate multi-scale entropy proposed in this paper not only retains the original multi-scale entropy to process multi-channel data in parallel. Multi-scale analysis also greatly reduces the complexity and computational redundancy of the original method. The improved multi-scale entropy and wavelet packet transform are combined by multi-scale decomposition of epileptic EEG signals. The method not only avoids a large amount of space-time consumption caused by the large amount of characteristic data, but also avoids the interference of high-frequency signals in traditional time-frequency analysis. For improving multivariate multi-scale entropy, the GAERS rat epileptic EEG and Bonn epileptic EEG data were tested. The results show that this method can effectively extract epileptic EEG features. It has good statistical characteristics and classification accuracy.
【學(xué)位授予單位】：燕山大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：R742.1

【參考文獻(xiàn)】

相關(guān)期刊論文 前8條

1 許敏光,龍開平,菅忠,董秀珍,楊繼慶,韓晟,江文;大鼠癲癇模型腦電信息的非線性分析[J];生物醫(yī)學(xué)工程學(xué)雜志;2003年03期

2 白冬梅;邱天爽;李小兵;;樣本熵及在腦電癲癇檢測中的應(yīng)用[J];生物醫(yī)學(xué)工程學(xué)雜志;2007年01期

3 王俊;寧新寶;李錦;馬千里;徐寅林;卞春華;;心電圖的多尺度熵分析[J];生物醫(yī)學(xué)工程學(xué)雜志;2007年05期

4 張睿;劉紹明;;基于EEG信號分析處理的癲癇預(yù)測研究[J];現(xiàn)代生物醫(yī)學(xué)進(jìn)展;2013年04期

5 吳延?xùn)|;謝洪波;;一種新的時(shí)間序列確定性辨識方法[J];物理學(xué)報(bào);2007年11期

6 袁琦;周衛(wèi)東;李淑芳;蔡冬梅;;基于ELM和近似熵的腦電信號檢測方法[J];儀器儀表學(xué)報(bào);2012年03期

7 胡江和;張佃中;;心電RR間期序列的近似熵與Lempel-Ziv復(fù)雜度分析[J];中國醫(yī)學(xué)物理學(xué)雜志;2007年06期

8 李鵬;劉澄玉;李麗萍;紀(jì)麗珍;于守元;劉常春;;多尺度多變量模糊熵分析[J];物理學(xué)報(bào);2013年12期

本文編號：1450015