A Revised Dynamic Hurst Parameter Estimation Method Based on Generalized Exponential Window Function

Dynamic Hurst parameter estimation is very important in the data filtering and system modelling. It is difficult to accurately estimate the dynamic Hurst parameter of long memory processes. In this research, a generalized exponential window function based dynamic Hurst parameter estimation method is provided, and the effective performance of the method is analyzed using fractional Gaussian noise. In the end of this paper, the application of revised estimation method in sleeping electroencephalogram signal is given. The simulation and sleeping electroencephalogram signal analysis results show that the revised estimation method can effectively analyze the random signals with local correlation characteristics. This method can be widely used in biomedical signal analysis, network traffic analysis, vibration signal analysis, statistical data analysis, etc.