Traditional methods for nonlinear dy-namic analysis,such as correlation dimension,Lyapunov exponent,approximate entropy,detrended fluctuation analysis,using a single parameter,cannot fully describe the extremely sophi...Traditional methods for nonlinear dy-namic analysis,such as correlation dimension,Lyapunov exponent,approximate entropy,detrended fluctuation analysis,using a single parameter,cannot fully describe the extremely sophisticated behavior of electroencephalogram (EEG). The multifractal for-malism reveals more “hidden” information of EEG by using singularity spectrum to characterize its nonlin-ear dynamics. In this paper,the zero-crossing time intervals of sleep EEG were studied using multifractal analysis. A new multifractal measure Δasα was pro-posed to describe the asymmetry of singularity spec-trum,and compared with the singularity strength range Δα that was normally used as a degree indi-cator of multifractality. One-way analysis of variance and multiple comparison tests showed that the new measure we proposed gave better discrimination of sleep stages,especially in the discrimination be-tween sleep and awake,and between sleep stages 3 and 4.展开更多
Complexity and nonlinearity approaches can be used to study the temporal and structural order in heart rate variability (HRV) signal, which is helpful for understanding the underlying rule and physiological essence of...Complexity and nonlinearity approaches can be used to study the temporal and structural order in heart rate variability (HRV) signal, which is helpful for understanding the underlying rule and physiological essence of cardiovascular regulation. For clinical applications, methods suitable for short-term HRV analysis are more valuable. In this paper, sign series entropy analysis (SSEA) is proposed to characterize the feature of direction variation of HRV. The results show that SSEA method can detect sensitively physiological and pathological changes from short-term HRV signals, and the method also shows its robustness to nonstationarity and noise. Thus, it is suggested as an efficient way for the analysis of clinical HRV and other complex physiological signals.展开更多
基金Acknowledgements This work was supported by the National Natural Science Foundation of China （Grant No. 60501003）.
文摘Traditional methods for nonlinear dy-namic analysis,such as correlation dimension,Lyapunov exponent,approximate entropy,detrended fluctuation analysis,using a single parameter,cannot fully describe the extremely sophisticated behavior of electroencephalogram (EEG). The multifractal for-malism reveals more “hidden” information of EEG by using singularity spectrum to characterize its nonlin-ear dynamics. In this paper,the zero-crossing time intervals of sleep EEG were studied using multifractal analysis. A new multifractal measure Δasα was pro-posed to describe the asymmetry of singularity spec-trum,and compared with the singularity strength range Δα that was normally used as a degree indi-cator of multifractality. One-way analysis of variance and multiple comparison tests showed that the new measure we proposed gave better discrimination of sleep stages,especially in the discrimination be-tween sleep and awake,and between sleep stages 3 and 4.
基金Supported by the National Natural Science Foundation of China (Grant Nos. 60501003, 60701002) Colleges Oriented Provincial Natural Science Research Plan of Jiangsu Province (Grant No. 06KJD510138)
文摘Complexity and nonlinearity approaches can be used to study the temporal and structural order in heart rate variability (HRV) signal, which is helpful for understanding the underlying rule and physiological essence of cardiovascular regulation. For clinical applications, methods suitable for short-term HRV analysis are more valuable. In this paper, sign series entropy analysis (SSEA) is proposed to characterize the feature of direction variation of HRV. The results show that SSEA method can detect sensitively physiological and pathological changes from short-term HRV signals, and the method also shows its robustness to nonstationarity and noise. Thus, it is suggested as an efficient way for the analysis of clinical HRV and other complex physiological signals.
