摘要
应用独立成分分析(independent component analysis,ICA)提取的房颤F波在QRST数据段有明显"扰动"失真,为减少这种失真,提出了一种ICA与小波变换相结合的F波提取算法。首先对原始信号进行ICA分解,获得初始F波及其分离向量;然后对初始F波进行多层小波分解,在小波域内构造反映F波失真的目标函数;最后利用最速下降法优化目标函数,获得准确的F波分离向量,从而实现对F波的准确提取。对仿真信号和真实信号的F波提取实验表明,该算法明显减少了F波的"扰动"失真。
When independent component analysis （ICA） algorithms are used to extract atrial fibrillation （F） wave from electrocardiogram signal （ECGs） of persistent atrial fibrillation, serious distortion exists in F wave QRST segment. To reduce this distortion, a new algorithm based on ICA and wavelet transform is proposed. Firstly, ICA algorithm is applied to get initial F wave and its separation vector. Then wavelet transform is used to decompose initial F wave, and the objective function that reflects F wave distortion is constructed in wavelet domain. Finally, steepest descent method is used to optimize the objective function and get more accurate separation vector. The proposed algorithm is validated with both simulated signal and real ECGs of atrial fibrillation, and distortion in the extracted F wave is considerately reduced.
出处
《仪器仪表学报》
EI
CAS
CSCD
北大核心
2011年第8期1716-1723,共8页
Chinese Journal of Scientific Instrument
基金
国家基础科研项目(B2320XX0604)资助
关键词
房颤
独立成分分析
小波变换
最速下降法
atrial fibrillation
independent component analysis （ICA）
wavelet transform
steepest descent method
