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

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.

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.