Explore The Stability Of HRV Signal Entropy Information Under Different Trends

Output signals of nonlinear system always display complex fluctuations,and the nonlinear characteristic of signals reflect the underlying dynamics.Entropy analysis is regarded as a important nonlinear analysis method,which have been applied to analyze actual signals for estimating complexity and evaluating dynamic characteristic of systems in many fields,including market economy,life science,control theory,etc.Meanwhile,the stability of entropy methods has been a hot of study,which focus on the length of signals,the integrity of signals,anti-noise ability.However,considering the effects of environment disturbances and instrument systems,the actual detecting signals always are carrying different trends,which result in that it is difficult to accurately catch signals complexity.So we studied the effect of trends on stability of entropy analysis for serving as experimental basis for quantifying complexity of data in clinical setting.Heart rate variability(HRV)signals are some typical nonlinear signals,which can reflect the underlying physiological states of heart system,and the clinical analysis has great significance.However,HRV signals display linear trend due to body position change;the monitoring time cause the periodic trend of HRV signals;HRV signals display different trends,effecting by some acquisition devices such as power frequency interference,baseline drift,etc.An immediate problem facing researchers applying entropy analysis to signals is whether trends affect complexity analysis of intrinsic dynamics of the systems.In the case,recognizing and filtering trends may be a measures.However,recognizing may be inaccurate and filtering trends may break the integrity of data.Therefor,the existence of signal trends puts forward higher requirements for analysis method stability.Therefor,we conducted a series of exploration and research.The main work and innovation of the thesis could be summarized as follows:(1)In this paper,we study three trend——linear,periodic,and power-law trends which are likely to occur in actual signals,and setup different parameters to explore effects of trends on the raw signals,that is,the slope of the linear trend Al,the amplitude of the periodic trend signal As and the periodic of the periodic trend signal T,the power of power-law trends Ap and the index of power-law trends signal ?.(2)In order to simulate effects of trends on entropy methods,we first generate the raw noise signals,and the noise signals superposed by different trends,then we used two approaches——the approximate entropy and base-scale entropy to quantify complexity of these signals,respectively.The results show that the approximate entropy is sensitive to the trends,and is very unsteady when trends enhance;For comparison,the base-scale entropy has preferable stability and accuracy for signal with different trends.Take the Logistic series as an example,we explore discernment and stability of two entropy measures by embedding different trends into the Logistic series.The results show that the existence of trends cause that the approximate entropy of Logistic series is terrible when compared with the raw change law with different nonlinear parameters r.However,the base-scale entropy is closed to the raw change law of base-scale entropy with different nonlinear parameters r.Therefor,the base-scale entropy still has preferable stability and accuracy for different r Logistic series with different trends.(3)The actual HRV signal of acupuncture stimulation?HRV signals from Normal Sinus Rhythm subjects(NSR)and congestive heart failures subjects(CHF)were used as a verification,then we use approximate entropy and base-scale entropy to calculate entropy values of the raw HRV signals and the HRV signals superposed by trend,respectively.The results indicate that approximate entropy is more sensitive to the trends of HRV signals so that it can not distinguish completely between the two states signals;Besides,the effects of trends on the approximate entropy are terrible because of misdirected judgment of different physiological states.So the approximate entropy is not suitable to analyze signal with trends.For the base-scale entropy analysis,the trends have little effect on the analysis results of the actual signals,and the base-scale entropy has preferable stability and accuracy for distinguishing signals internal complexity,which provide an experimental basis for the wide applications of the method to analyze the actual signals.