DETC2011/47878 MULTIFRACTIONAL PROPERTY ANALYSIS OF HUMAN SLEEP EEG SIGNALS

Electroencephalogram (EEG), the measures and records of the electrical activity of the brain, exhibits evidently nonlinear, non-stationary, chaotic and complex dynamic properties. Based on these properties, many nonlinear dynamical analysis tech-niques have emerged, and much valuable information has been extracted from complex EEG signals using these nonlinear anal-ysis techniques. Among these techniques, the Hurst exponent es-timation was widely used to characterize the fractional or scaling property of the EEG signals. However, the constant Hurst expo-nent H cannot capture the the detailed information of dynamic EEG signals. In this research, the multifractional property of the normal human sleep EEG signals is investigated and character-ized using local Hölder exponent H(t). The comparison of the analysis results for human sleep EEG signals in different stages using constant Hurst exponent H and the local Hölder exponent H(t) are summarized with tables and figures in the paper. The analysis results show that local Hölder exponent provides a novel and valid tool for dynamic assessment of brain activities in dif-ferent sleep stages.