小波变换的信号分形分析及其在心电信号处理中的应用研究

介绍了一种基于小波变换的分形分析方法,并将它成功地应用于心房纤颤的检测中.该方法提取了一个特征值r,结果表明在心房纤颤开始时,特征值是上升的,心房纤颤结束后,特征值又慢慢恢复到原来的数值,由此可以成功地检测出心房纤颤的开始和结束.此外,由于特征值同时反映了heartratevariability(HRV)信号的复杂程度,所以同时它也表明:心房纤颤开始后,HRV信号的复杂度是下降的 关键词： HRV 小波变换 分形

Research on the analysis of cardiogram signal with fractals based upon wavelet transform and its application

The nonlinear analysis of heart rate variability (HRV) has been the hotspot in the field of biomedical engineering There are several methods in the research of its dynamic behaviour, such as complexity, Lyapunov exponents, correlation dimension, approximate entropy, detrended fluctuation analysis. Because of the long\|range power\|law correlation characteristic of HRV, this paper proposeds another alternative technique with fractals based upon wavelet transform and gaves its application in the detection of atrial fibrillation. This technique extracts a characteristic value r . The results show that the value rises when atrial fibrillation begins, while the value falls when atrial fibrillation ends. Thus, the begin and the end of atricial fibrillation can be successfully detected according to the change of the characteristic value. In addition, since the value also indicates the complexity of HRV, the results show that the complexity of HRV decreases at the beginning of atricial fibrillation.