信息与控制2012，Vol.41Issue(5)：628-636,9.DOI:10.3724/SP.J.1219.2012.00628
基于隐马尔可夫模型的躯感网心电图信号特征提取方法
Feature Extraction Method for ECG Signal Based on HMM in Body Sensor Network
摘要
Abstract
A 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 ECG signal feature is extracted by using the Viterbi algorithm. Simulation results show that this feature extraction method for ECG signal based on HMM has lower complexity, higher accuracy, and better timeliness, which is suitable for processing nonlinear and dynamic changing ECG signal on-line and can satisfy the performance requirements of ECG signal feature extraction in body sensor network.关键词
躯感网/心电图信号/隐马尔可夫模型/特征提取Key words
body sensor network/ ECG (electrocardiogram) signal/ hidden Markov model (HMM)/ feature extraction分类
信息技术与安全科学引用本文复制引用
凤超,梁炜,张晓玲,杨雨沱,谈金东..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):628-636,9.基金项目
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) （60725312,61100159,61174026,61172145）
国家863计划资助项目(2011AA040101,2011AA040103):国家973计划前其研究专项课题(2010CB334705) （2011AA040101,2011AA040103）
中国科学院知识创新工程重要方向性项目 (KGCX2-EW-104). （KGCX2-EW-104）
