aEEG信號圖像重構(gòu)及基于集成SVM的分類研究
發(fā)布時間：2018-11-12 10:26
【摘要】：新生兒振幅整合腦電(aEEG)是一種有效的新生兒腦功能狀態(tài)長期監(jiān)測技術(shù)。因其成本低,操作簡單以及可長期監(jiān)測等優(yōu)點,aEEG已經(jīng)越來越多地應(yīng)用于新生兒重癥監(jiān)護病房中。設(shè)計一個自動判別方案或算法實現(xiàn)對aEEG信號的自動判別非常重要,這不僅能夠使醫(yī)生從識別腦功能異常這項繁重任務(wù)中解放出來,從而專注于解決這些腦部疾病,而且對aEEG這項技術(shù)的使用和推廣具有深遠意義。本研究主要工作是設(shè)計一套自動化的aEEG信號判讀方案。該方案從數(shù)據(jù)中提取其有效特征表示并訓練出模型,之后通過該模型來預(yù)測未知aEEG樣本的類別。論文首次提出一種aEEG信號重構(gòu)的方法,通過該方法獲得aEEG信號幅值頻率等高圖,該圖比原始aEEG信號更能直觀反映局部振幅變化特點。本論文從不同角度刻畫aEEG信號并提取了 4類特征,包含圖像特征、線性特征、直方圖特征以及復(fù)雜度特征。其中,論文將LBP圖像算子引入aEEG信號幅值頻率等高圖分析中并得到其二維圖像特征,該特征比一般基于幅值的一維特征更能刻畫aEEG信號的特點;下邊界作為醫(yī)生判讀aEEG重要依據(jù)但醫(yī)學上沒有具體定義,論文重新定義aEEG下邊界并引入打分系統(tǒng)對其量化;同時,還第一次引入自排列熵來量化aEEG信號復(fù)雜度。最后,論文提出一種基于支持向量機的集成方法稱為Hybrid-SVM,用于實現(xiàn)aEEG信號的自動分類。為證明其有效性和準確性,論文在包含276個aEEG樣本的數(shù)據(jù)集上進行驗證。實驗結(jié)果表明,圖像特征可以有效刻畫正異常aEEG信號特征,并且可以不同程度地提升各常用分類器的分類性能。與原始的支持向量機算法以及其它集成方法相比,本文的aEEG信號識別方法綜合性能最優(yōu),其中識別準確率達到95.68%�；诩煞椒℉ybrid-SVM的aEEG信號分類方法有助于臨床檢測新生兒腦部異常。
[Abstract]:Amplitude integrated EEG (aEEG) is an effective long-term monitoring technique for neonatal brain function status. Because of its advantages of low cost, simple operation and long-term monitoring, aEEG has been used more and more in neonatal intensive care unit. It is very important to design an automatic discriminant scheme or algorithm to identify aEEG signals automatically, which not only frees doctors from the heavy task of identifying abnormal brain function, but also focuses on solving these brain diseases. Moreover, it is of great significance to use and popularize aEEG technology. The main work of this study is to design a set of automatic aEEG signal interpretation scheme. The scheme extracts its effective feature representation from the data and trains the model. Then the model is used to predict the class of unknown aEEG samples. In this paper, a method of aEEG signal reconstruction is proposed for the first time. The amplitude and frequency contour diagram of aEEG signal is obtained by this method, which can reflect the characteristic of local amplitude change more intuitively than the original aEEG signal. This paper describes aEEG signals from different angles and extracts four kinds of features, including image features, linear features, histogram features and complexity features. In this paper, the LBP image operator is introduced into the amplitude and frequency contour graph analysis of aEEG signal and its two-dimensional image feature is obtained. This feature is more capable of characterizing the aEEG signal than the general one-dimensional feature based on the amplitude. The lower boundary is an important basis for doctors to interpret aEEG, but there is no specific definition in medicine. This paper redefines the lower boundary of aEEG and introduces a scoring system to quantify it. At the same time, the self-permutation entropy is introduced for the first time to quantify the complexity of aEEG signals. Finally, an ensemble method based on support vector machine (SVM) called Hybrid-SVM, is proposed to realize the automatic classification of aEEG signals. To prove its validity and accuracy, this paper is validated on a dataset containing 276 aEEG samples. The experimental results show that the image features can effectively describe the positive abnormal aEEG signal features, and can improve the classification performance of the commonly used classifiers in varying degrees. Compared with the original support vector machine (SVM) algorithm and other ensemble methods, the aEEG signal recognition method in this paper has the best comprehensive performance, and the recognition accuracy is 95.68%. The method of aEEG signal classification based on Hybrid-SVM is helpful for clinical detection of neonatal brain abnormalities.
【學位授予單位】：華東師范大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：R722.1;TP391.41
本文編號：2326844
[Abstract]:Amplitude integrated EEG (aEEG) is an effective long-term monitoring technique for neonatal brain function status. Because of its advantages of low cost, simple operation and long-term monitoring, aEEG has been used more and more in neonatal intensive care unit. It is very important to design an automatic discriminant scheme or algorithm to identify aEEG signals automatically, which not only frees doctors from the heavy task of identifying abnormal brain function, but also focuses on solving these brain diseases. Moreover, it is of great significance to use and popularize aEEG technology. The main work of this study is to design a set of automatic aEEG signal interpretation scheme. The scheme extracts its effective feature representation from the data and trains the model. Then the model is used to predict the class of unknown aEEG samples. In this paper, a method of aEEG signal reconstruction is proposed for the first time. The amplitude and frequency contour diagram of aEEG signal is obtained by this method, which can reflect the characteristic of local amplitude change more intuitively than the original aEEG signal. This paper describes aEEG signals from different angles and extracts four kinds of features, including image features, linear features, histogram features and complexity features. In this paper, the LBP image operator is introduced into the amplitude and frequency contour graph analysis of aEEG signal and its two-dimensional image feature is obtained. This feature is more capable of characterizing the aEEG signal than the general one-dimensional feature based on the amplitude. The lower boundary is an important basis for doctors to interpret aEEG, but there is no specific definition in medicine. This paper redefines the lower boundary of aEEG and introduces a scoring system to quantify it. At the same time, the self-permutation entropy is introduced for the first time to quantify the complexity of aEEG signals. Finally, an ensemble method based on support vector machine (SVM) called Hybrid-SVM, is proposed to realize the automatic classification of aEEG signals. To prove its validity and accuracy, this paper is validated on a dataset containing 276 aEEG samples. The experimental results show that the image features can effectively describe the positive abnormal aEEG signal features, and can improve the classification performance of the commonly used classifiers in varying degrees. Compared with the original support vector machine (SVM) algorithm and other ensemble methods, the aEEG signal recognition method in this paper has the best comprehensive performance, and the recognition accuracy is 95.68%. The method of aEEG signal classification based on Hybrid-SVM is helpful for clinical detection of neonatal brain abnormalities.
【學位授予單位】：華東師范大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：R722.1;TP391.41
【參考文獻】
相關(guān)期刊論文 前5條
1 謝平;魏秀利;杜義浩;陳曉玲;;基于自排序熵的表面肌電信號特征提取方法[J];模式識別與人工智能;2014年06期
2 王寶宏;時文玲;王克煊;李燕;羅瑤;;振幅整合腦電圖在新生兒缺氧缺血性腦病中的診斷價值[J];中國兒童保健雜志;2013年10期
3 慈春燕;李文;盧憲梅;;早產(chǎn)兒早期振幅整合腦電圖特點的分析[J];山東大學學報(醫(yī)學版);2012年09期
4 王紹賓;王一抗;王志中;施億峗;邵肖梅;;基于非線性動力學的振幅整合新生兒腦電圖分析[J];生物醫(yī)學工程學雜志;2009年06期
5 張丹丹;丁海艷;劉云峰;周叢樂;丁海曙;葉大田;;振幅整合腦電圖的實現(xiàn)及其在新生兒腦功能監(jiān)護中的應(yīng)用前景[J];中國醫(yī)療器械信息;2008年03期
相關(guān)碩士學位論文 前1條
1 王愈;新生兒振幅整合腦電自動識別研究[D];華東師范大學;2015年
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