基于EMD奇异值分解诊断震颤的新方法  

A New Method of Tremor Diagnosis Based on Singular Value Decomposition of EMD

作　　者：艾玲梅[1,2] 王珏[1] 

机构地区：[1]西安交通大学生物医学信息工程教育部重点实验室,西安710049 [2]陕西师范大学计算机科学学院,西安710062

出　　处：《生物医学工程学杂志》2009年第6期1335-1339,共5页Journal of Biomedical Engineering

基　　金：国家863高技术研究发展计划项目资助(2006AA04Z370)

摘　　要：针对目前临床上对特发性震颤(Essential tremor,ET)、帕金森病(Parkinsonian disease,PD)、生理性震颤(Physiological tremor,PT)等3种常见震颤误诊断的问题,我们提出了一种基于经验模式分解提取奇异值特征和支持向量机识别3种不同类型震颤的新方法。首先采集了40例震颤受试者的手加速度信号,并用经验模式分解法将其分解成多个平稳的固有模态函数。将能刻画信号的最重要的前四个固有模态函数形成初始特征向量矩阵,然后对该矩阵进行奇异值分解,提取其奇异值作为诊断震颤类型的特征向量,再用支持向量机分类震颤类型。实验结果分析表明,以经验模式分解提取奇异值为特征参数的支持向量机识别方法能较好识别3种不同震颤,为临床诊断震颤类型提供了一种新方法。Aiming at three kinds of tremor, including essential tremor （ET）, Parkinsonian disease （PD） tremor and physiological tremor （PT）, which are subjected to frequent clinical misdiagnosis, a new method based on singular value decomposition （SVD） of empirical mode decomposition （EMD） and support vector machine （SVM） for the recognition of tremor is proposed in this paper. First, the hand acceleration signals of three different types of 40 tremor voluntary subjects were collected on the basis of informed consent, and the EMD method was used to decompose the signals into a number of stationary intrinsic mode functions （IMFs）. Then the preceding four IMFs which could describe signals were selected, and the initial feature vector matrixes were formed. After the application of SVD technique to the initial feature vector matrixes, the singular values were obtained and used as the feature vectors of tremor types to be put in the support vector machine classifier as well as in the identification of tremor types. The results of experiment have shown that the proposed diagnosis method based on SVD of EMD and SVM can extract tremor features effectively and identify tremor types accurately. It also provides a new assistant approach for clinical diagnosis of tremor.

关 键 词：震颤识别 经验模式分解 奇异值分解 支持向量机 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]