The computation of atrial fibrillation chaos characteristics based on wavelet analysis

Abstract

Atrial fibrillation data series show the non-linear and chaos characters in the process of time-space kinetics evolution. In the case of unknowing the fractal dimension of atrial fibrillation chaos, the process of querying the similarity of diagnosis curve figure will be affected to a certain degree. An evaluation formula of varying-time Hurst index is established by wavelet and the algorithm of varying-time index is presented, which is applied to extract the characteristics of the atrial fibrillation in this paper. The diagnosis of atrial fibrillation curve figure can be done at some resolution ratio level. The results show that the time-varying fractal dimension rises when atrial fibrillation begins, while it falls when atrial fibrillation ends. The begin and the end characteristics of atrial fibrillation can be successfully detected by means of the change of the time-varying fractal dimension. The results also indicate that the complexity of heart rate variability (HRV) decreases at the beginning of atrial fibrillation. The effectiveness of the method is validated by means of the HRV example in the end. © Springer-Verlag Berlin Heidelberg 2007.