Multifractal Characteristic Analysis Of Electroencephalogram

With the intensification of social aging, the ratio of people getting the Alzheimer's disease in elderly population is increased, which brought social new challenges and problems. How to predict and quantify the extent of brain's slump early is important.Firstly, the paper applied wavelet transform maximum modulus multifractal method to electroencephalogram. This method is mainly used to detect signal singularity. And this paper states a multifractality based on wavelet transform maximum modulus method. By calculating singularity spectrum, the singular intensity distribution of the normal ECG of young subjects and old subjects'singularity spectrums were obtained. Comparing young subjects'singularity spectrum with old subjects'singularity spectrum, we found that the span of young subjects'singularity spectrum was larger than old subjects. It was found that the multifractal strength of EEG signal is weakened with the increasing of age, changing from multifractality to monofractality. Therefore the singular intensity distribution could be used as a criterion for identifying normal EEG.Finally, this paper introduces Detrended Cross-Correlation Analysis. The DCCA was designed to investigate power-law cross correlations between different simultaneously recorded time series in the presence of nonstationarity. Here in this paper we found the cross-correlation between two EEG from different leads is weakening with growing age.