Correlation Analysis Of Heart Rate Variability Signals

Heart is one of the most important organs of the human body,its function is related to human health and even life safety.Heart system is a typically chaotic system in nature,its output signal represents the complex volatility of itself.Heart rate variability(HRV)signal which usually has complex volatility is an important output signal of the heart,can reflect important information of the cardiac function.The correlation of heart rate variability signal is the concrete manifestation of the chaotic nature of the heart system,which reflects the regulation of the autonomic nervous system and the pathological information of the heart.In recent years,researchers have done a lot of research,the most basic method is time domain and frequency domain analysis,but it can not reflect the chaotic characteristics of the heart system.For this reason,a large number of nonlinear methods such as fractal theory,multifractal,basic scale entropy and empirical modal decomposition have been introduced.However,due to the fact that the actual signal has noise and no interference,so these methods are rarely applied in clinical practice,looking for a more appropriate way to study the heart rate mutation signal is still needed.In this article,firstly,analysis the HRV signal of healthy people and patients with congestive heart failure(CHF)in detrended fluctuation analysis(DFA),reveals the change of correlation in single fractal,find out that the correlation of healthy people is stronger than CHF patients.Then analysis the two signals in multifractal detrended fluctuation analysis(MF-DFA),found that the change of multifractal and correlation of the HRV signal,strengthen the conclusion that the correlation of healthy people is stronger than the patients with CHF and the multifractal nature is weaker than that of the CHF patients.Finally,a method based on ensemble empirical mode decomposition(EMD)is proposed to analyze the correlation of heart rate variability signals.According to the analysis of correlation of the intrinsic mode functions,explore the association of the heart disease and the heart rate variability signal.First,analysis the HRV signal of patients with congestive heart failure(CHF)and healthy people in ensemble empirical mode decomposition and obtained intrinsic mode functions.Then,demand the Hurst index of the intrinsic mode functions,according to the change of Hurst index to analyze the correlation of the intrinsic mode functions.The results show that the correlation of the IMF of the healthy people increased gradually,but the correlation of the IMF of CHF patients decreased and then increased.At the same time,the changing of the correlation of IMF is associated to the ability of ventricular depolarization.The ventricular depolarization ability of patients with CHF is abated,so the correlation of IMF2 is lower than that of IMF1.Therefore,the changing correlation of IMF2 can provide experimental basis for the diagnosis of cardiac function.This method can overcome the non self adaptability of before methods in HRV signal study,and the accuracy and has little relationship with the length of the time series,you can use a small amount of data to get similar results.For this method,on the correlation of the HRV signal,not only can analyze the overall relevance of heart rate variance signals but also the correlation of different time scale in the signal can be analyzed deeply.Complain the influence of disease on the correlation of the HRV signal from the natures,provide a theoretical basis for the clinical diagnosis.