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基于隐马尔可夫模型的躯感网心电图信号特征提取方法
Alternative TitleFeature Extraction Method for ECG Signal Based on HMM in Body Sensor Network
凤超; 梁炜; 张晓玲; 杨雨沱; 谈金东
Department工业控制网络与系统研究室
Source Publication信息与控制
ISSN1002-0411
2012
Volume41Issue:5Pages:628-636
Indexed ByCSCD
CSCD IDCSCD:4664938
Contribution Rank1
Funding Organization国家自然科学基金资助项目(60725312,61100159,61174026,61172145); 国家科技重大专项基金资助项目(2010ZX03006-005-01,2011ZX03005-002); 国家863计划资助项目(2011AA040101,2011AA040103); 国家973计划前期研究专项课题(2010CB334705); 中国科学院知识创新工程重要方向性项目(KGCX2-EW-104)
Keyword躯感网 心电图信号 隐马尔可夫模型 特征提取
Abstract为了解决躯感网的心电信号特征提取问题,结合心电图信号波形的特征区间,建立了面向心电图信号特征提取的离散隐马尔可夫模型;并面向该模型定制了专家标注选取、导联选取、观察数据归一化、三元组初始值选取以及训练数据量选取等方法.最后,采用Baum-Welch算法训练HMM模型的参数,并利用Viterbi算法提取心电图的信号特征.仿真结果表明,基于HMM的心电图信号特征提取算法的复杂度较低、精确度较高、实时性较好,适合在线处理非线性、动态变化的心电图信号,能够满足基于躯感网的心电图信号特征提取的性能要求.
Other AbstractA discrete hidden Markov model(HMM) for ECG(electrocardiogram) signal feature extraction is built,which solves problems of ECG signal feature extraction in body sensor network and considers the feature partition of ECG waveform.Based on the proposed HMM,methods of expert annotation selection,lead selection,normalization of observation data,triple initial value selection,and training data quantity selection are customized.Finally,the HMM model parameters are trained by using the Baum-Welch algorithm,and the ...
Language中文
Citation statistics
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/10312
Collection工业控制网络与系统研究室
Affiliation1.中国科学院沈阳自动化研究所工业控制网络及系统研究室
2.中国科学院研究生院
Recommended Citation
GB/T 7714
凤超,梁炜,张晓玲,等. 基于隐马尔可夫模型的躯感网心电图信号特征提取方法[J]. 信息与控制,2012,41(5):628-636.
APA 凤超,梁炜,张晓玲,杨雨沱,&谈金东.(2012).基于隐马尔可夫模型的躯感网心电图信号特征提取方法.信息与控制,41(5),628-636.
MLA 凤超,et al."基于隐马尔可夫模型的躯感网心电图信号特征提取方法".信息与控制 41.5(2012):628-636.
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