Extraction And Analysis Of Multiscale Dynamical Characteristics Of Human Physiological Time Series

Nonlinear behaviors are ubiquitous in natural and social sciences. The human beings physiological time series have much nonlinear behavior and extremely complex features. They can well reflect the body’s physiological condition, so we could achieve the purpose of warning some diseases if we find their changes in time. So researching the dynamic behaviors and characteristics of human physiological time series has become more and more popularity and attention by medical professional or science researchers because of the very realistic value and significance. Traditional methods usually depend on a single physiological time series or study it at a single scale such as approximate entropy, sample entropy, wavelet transform modulus maximum value method, DFA and so on. They can not accurately detect physiological time series behaviors or characteristics, also could not well reflect the humans physiological state.The paper propose the way called multiscale entropy to investigate the complexity of two physiological time series such as ECG signal and respiration signal which are simultaneously recorded with the different physiological state under the multi-scale. Results show that the movement state of the signal complexity slightly stronger than the state of calm, but individual health signal complexity far stronger than the heart disease.The methods MF-DFA and MF-DCCA are proposed to investigate the long range autocorrelation by themselves or long range cross correlated between ECG signal and respiration signal. They also discover both of the ECG signal and respiration signal have multifractal characteristics. Through the quantitative calculation about the different parameters named multifractal spectrum width, multifractal spectrum area and multifractal spectrum symmetry to describe the strength of the multi-fractal properties. It is an effective method to precisely distinguish the different physiological state of human body.The paper proposed different research methods to explore human physiological time series, so it can more full and accurate statistical analysis dynamic behavior characteristics of the human body physiological time series. This paper also has achieved good results about distinction between the different physiological state. Theoretical basis and actual data analysis of the article also provide a reference direction for prevention and treatment of diseases in clinical medicine.