Research On The Characteristics Of Heart Rate Variability Signals Under Physiological Aging

For the nonlinear study of the output signal of the cardiac system,the hidden information inside the heart system can be obtained,which provides a great help for us to study the nonlinear dynamic behavior of the heart system.The heart system is one of the most complex systems in nature.The role of the heart is to pump arterial blood rich in oxygen and nutrients to different parts of the body,keeping our body functioning in a normal,healthy state.A large number of experimental studies have shown that the complexity of the cardiac system is the result of a combination of multiple tissues in the heart and the regulation of autonomic nerves.This complex and organized mode of regulation can be demonstrated by the variability of the cardiac output signal,which we call the heart rate variability signal(HRV).The signal has characteristics common to physiological signals,and the output waveform of the signal appears to have regular and actual fluctuations.Therefore,the signal is a complex chaotic signal.The study of heart rate variability signals can not only promote the application of nonlinear analysis in the biomedical signal,but also find the relationship between the complexity of the heart system and the autonomic regulation function,and provide valuable theoretical research on cardiovascular diseases.information.In recent years,the aging of our country has become increasingly serious.It is of great significance to study the effects of physiological aging on the heart system.This paper uses the fractal method in nonlinear theory,and through practical experiments,analyzes the correlation between HRV signals of healthy young people and healthy old people based on the analysis of castration fluctuations.Combined with the detrended wave analysis method,multivariate detrended fluctuation analysis is established.The method analyzes the multifractal characteristics of HRV signals in healthy young people and healthy elderly people.It is concluded that the correlation fluctuation range of HRV signals in healthy young people is greater than that of healthy elderly people,which indicates that the healthy elderly people’s cardiac system is more stable,and the multi-fractal characteristics of healthy elderly HRV signals are smaller than healthy young people,which indicates that healthy elderly people The chaotic nature of the human heart system is weak and the heart rate variability is low.The paper also studied the correlation of HRV signals and the variation of multifractal characteristics at different time scales.The specific experimental steps are:a set of empirical modal decomposition of HRV signals of healthy young people and healthy old people,and the results of different components The eigenmode function,compared to the EMD method EEMD method,overcomes the modal aliasing phenomenon that occurs in the EMD decomposition,and then calculates the correlation of the HRV signals of healthy young people and healthy elderly people with different IMF components based on the DFA algorithm.In conclusion,the IMF4 component can be an indicator of HRV signaling in healthy and healthy elderly individuals.To ensure the accuracy and universality of the results of the dissertation,we calculated the multifractal characteristics of signals under different scales under different IMF components based on the MF-DFA method.We can find that no matter what the fractal order,two The curves can all be completely separated,and the IMF4 component can be used as an important parameter for distinguishing HRV signals of healthy yong people and healthy elderly people.It can provide the basis for the diagnosis of cardiac function.The experimental method used in this paper has a good self-adaptability,and can use a small amount of experimental data points to get accurate results.For the analysis of HRV signal correlation and multifractal characteristics,not only the eigenin formation of the signal can be studied from different fractal scales,but also the fractal characteristics of different time scale components of the signal can be analyzed in depth to explain the effect of physiological aging on the HRV signal.It can provides theoretical basis for the detection and diagnosis of clinical medicine.