CONVERGENCE INVESTIGATION OF MULTIFRACTAL ANALYSIS BASED ON Lp-NORM CONSTRAINT

In this paper, we propose the multifractal detrending fluctuation analysis based on L-p-norm constrain (MF-DFN). We assess the performance of the proposed algorithm by constructing a p-model based multiplicative cascades time series. We calculate the generalized Hurst exponent H(q), Renyi exponent tau(q), singularity exponent alpha and multifractal spectrum f(alpha) for multifractal detrended fluctuation analysis (MF-DFA) and MF-DFN, respectively. We notice that under different norm constraints, the distribution of multifractal characteristics is quite different. Appropriate norm constraints can make the multifractal characteristics of time series described more accurately. Based on the analytical solution curve, the distribution of different multifractal numerical curves determines the correct selection of norm constraint value. In this study, using a combination of norms 0.01 and 4, the depicted numerical curves almost overlap the theoretical curve, which shows that the proposed MF-DFN is superior to MF-DFA. To eliminate the influence of specific time series, we reset parameters n(max) and P-1 in p-model. Various experimental results also show the effectiveness of the proposed MF-DFN. In addition, we also verify the efficiency of the proposed MF-DFN by using the practical application. The experimental results show that our proposed method plays a significant role in ECG classification.

Automated summary

In this paper, a multi-fractal detrending fluctuation analysis method based on L-p-norm constraint (MF-DFN) is proposed. The performance of the algorithm is evaluated using a p-model-based multiplicative cascades time series. The results show that the appropriate norm constraints can accurately describe the multifractal characteristics of the time series, and the proposed MF-DFN method outperforms MF-DFA in analyzing the multifractal characteristics and plays a significant role in ECG classification.