心房颤动患者心房活动特征的提取与分析

目的构造一种新的盲源分离方法,从心房颤动(简称房颤)患者心电图中有效提取心房活动信号,在此基础上分析心房活动的特征指标。方法采用小波变换来实现心电信号的稀疏表示,证明了独立分量分析(ICA)的3个基本条件在小波域依然满足,建立了小波域心电信号的独立分量分析的数学模型。结果应用小波域独立分量分析模型可以有效提取心房活动信号。讨论了与房颤相关的P波离散度、频谱、房颤周长等几个预测指标的具体实现,比较了不同房颤的具体特征。结论该算法可以有效提取房颤特征信号,对房颤机制的深入研究以及临床治疗具有很好的借鉴意义。

Feature extraction and analysis of atrial activity from surface electrocardiogram with atrial fibrillation

Objective A new method of blind source separation is proposed to extract atrial activity signals from surface electrocardiogram and analyze the features of atrial activity during atrial fibrillation(AF) episodes.Metheds The wavelet analysis was used to implement the sparse representation of ECG.Those three fundamental requirements which must be satisfied in independent component analysis(ICA) theory were verified in wavelet domain and a mathematical model of ICA in wavelet domain was presented.Results The atrial activity signals were extracted by the ICA model in wavelet domain.Some metrics such as the P wave dispersion(Pdis),frequencies spectrum and AF cycle length(AFCL) which had close relation with AF were discussed in details.And features of different kinds of atrial fibrillation were compared.Conclusion These new non-invasive approaches have the ability to extract atrial activity signals during AF episodes,which have great importance to the mechanism research and clinical treatment of AF.