本文選題：腦中風　切入點：微波　出處：《東華大學》2017年碩士論文

【摘要】：隨著現(xiàn)在生活水平不斷提升,腦中風發(fā)病人數(shù)激增,腦中風已經(jīng)成為除癌癥之外威脅人類生命的一大殺手,腦中風及時得到檢測和治療會大大提高存活率和治愈率。所以腦中風的早期診斷尤為重要,同時也得到了海內(nèi)外研究人員的高度關注。腦中風微波檢測是利用微波技術對腦中風進行檢測,具有及時快速,低成本,高有效性并且安全性能良好的優(yōu)點。目前人體微波無損檢測主要采用基于電磁逆散射原理的微波成像技術,難以有效地應用于具有復雜結構組織,中風病灶與正常組織介電特性相差不大的腦中風檢測,本文將模式識別的分類檢測方法用于腦中風檢測,采用基于子空間分析法的腦中風檢測分類器模型,提出了基于天線對交叉點的中風病灶定位算法,有效而且快速地檢測并定位腦中風。本文首先介紹了微波檢測腦中風的理論基礎,包括腦部復雜的結構組織以及微波檢測的基本原理,進行微波檢測系統(tǒng)的設計,主要由產(chǎn)生激勵信號的調(diào)制高斯脈沖、發(fā)送和接收天線、數(shù)據(jù)收集模塊和數(shù)據(jù)分析模塊四個部分組成。其次,本文基于時域有限差分法(FDTD,Finite-Difference Time-Domain)算法創(chuàng)建人腦電磁計算仿真模型,獲取微波透射S參數(shù)作為仿真數(shù)據(jù)樣本,并對兩類不同樣本,即含有血塊的腦部數(shù)據(jù)和正常腦部數(shù)據(jù),進行標識,組成樣本庫;然后進行特征提取,建立子空間線性分類函數(shù),利用交叉驗證,設計和訓練子空間分類器,來區(qū)分這兩類樣本,并利用主角度序列法,對分類子空間基向量進行優(yōu)化;進一步,建立子空間分類器識別血塊是否在天線對連線上,進而利用天線對交叉定位原理,進行腦中風病灶的定位。然后搭建實驗平臺利用實驗數(shù)據(jù)驗證腦中風檢測和定位方法,實驗平臺利用超寬帶天線收發(fā)射頻信號,根據(jù)腦部介電特性采用材料替代腦部具體組織,構建腦部頭型。微波收集模塊采用羅德施瓦茨(ZVL)矢量網(wǎng)絡分析儀收集S參數(shù)。通過腦中風檢測實驗系統(tǒng)證明了本方法對實驗數(shù)據(jù)具有較高的分類準確性。最后總結全文,并進行展望。經(jīng)仿真和實驗系統(tǒng)證明,基于子空間分類方法的腦中風檢測和定位方法,具有較高的分類正確性,是一種有效的腦中風檢測方法,適用于入院前預診斷,對于中風病人早期診斷和治療具有重要的應用意義。
[Abstract]:With rising living standards and a surge in the number of stroke cases, stroke has become a major killer of human life besides cancer. Timely detection and treatment of stroke can greatly improve survival and cure rates, so early diagnosis of stroke is particularly important. At the same time, it has received great attention from researchers at home and abroad. Microwave detection of stroke is a method of detecting stroke using microwave technology, which has the advantages of prompt, fast and low cost. At present, microwave imaging technology based on electromagnetic inverse scattering principle is mainly used in human microwave nondestructive testing, which is difficult to be effectively applied to complex structures. In this paper, the classification method of pattern recognition is applied to the detection of stroke, and the model of cerebral stroke detection classifier based on subspace analysis is used. In this paper, an algorithm for locating stroke focus based on antenna intersection is proposed to detect and locate stroke effectively and quickly. Firstly, the theoretical basis of microwave detection of stroke is introduced in this paper. Including the complex structure of the brain and the basic principles of microwave detection, the design of the microwave detection system is mainly composed of the modulation of Gao Si pulse, which generates the excitation signal, to send and receive the antenna. Data collection module and data analysis module are composed of four parts. Secondly, based on FDTDX Finite-Difference Time-Domain-based algorithm, a human brain electromagnetic computing simulation model is established, and microwave transmission S parameters are obtained as simulation data samples. Two different kinds of samples, brain data containing blood clots and normal brain data, are identified to form a sample database, and then feature extraction is carried out, subspace linear classification function is established, and cross-validation is used. The subspace classifier is designed and trained to distinguish the two kinds of samples, and the classification subspace basis vector is optimized by the main angle sequence method. Furthermore, the subspace classifier is established to identify whether the blood clot is on the antenna line. Then the principle of cross-localization of the antenna is used to locate the cerebral apoplexy focus. Then the experimental platform is built to verify the method of stroke detection and location using experimental data. The experimental platform uses ultra-wideband antenna to receive and receive RF signals. According to the dielectric properties of the brain, the material is used to replace the specific brain tissue. The microwave collection module uses Luo De Schwartz ZVLV vector network analyzer to collect S parameters. The experimental system of cerebral stroke detection proves that this method has high classification accuracy for experimental data. Finally, the full text is summarized. The simulation and experimental results show that the method of stroke detection and location based on subspace classification method has higher classification accuracy and is an effective method for stroke detection, which is suitable for pre-hospital diagnosis. It is of great significance for the early diagnosis and treatment of stroke patients.
【學位授予單位】：東華大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TN015;R743.3

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本文編號：1678294