Hypertension Risk Stratification Study For Multiparameter Assessment Of Heart Rate Variability Signals

Hypertension has been a great threat to human health in recent years.The disability and mortality of hypertension are mainly caused by its complications.One of the major types is hypertension.Patients with hypertension are roughly divided into high-risk subjects and low-risk subjects according to the possibility of cardiovascular events.In recent years,Domestic and foreign scholars have summarized a large number of clinical cases of hypertension,and gradually believe that it is very important to stratify the risk of cardiovascular events of hypertension.The cardiovascular system presents complex variability,namely,heart rate variability(HRV).Studying heart rate variability(HRV)signals can effectively evaluate heart health and discriminate heart function.However,the use of any single parameter for the evaluation of ECG signals is very onesided Only a multi-parameter joint evaluation can fully evaluate its performance in all aspects.Most of the previous research methods on time series have focused on time domain,frequency domain and non-linear methods.With the continuous development of complex networks in the past 10 years,transforming time series into complex networks is a new analytical perspective and a new attempt in the field of early warning of heart diseases.HRV signals of healthy people,high-risk subjects with hypertension and low-risk subjects with hypertension were analyzed by using time series non-linear methods,such as multifractal trend analysis,entropy analysis and visual graph algorithm of complex network.The aim of this paper is to explore the physiological characteristics of different states of the human body through multiparameter and multi-angle analysis methods,and to seek effective analysis indicators as the double warning indicators for the diagnosis of hypertension patients and the risk stratification of cardiovascular events in hypertension.The experimental results show that:1.The multifractality of healthy people was the strongest,followed by subjects with low risk of hypertension.It is suggested that the heart rate variability of patients with hypertension is reduced,the autonomic nervous dysfunction,that is,sympathetic stimulation,and the vagal tension is decreased.The multifractal of subjects at high risk for hypertension cardiovascular events was the weakest,suggesting that autonomic nervous function of subjects at high risk of hypertension was abnormal,that is,further sympathetic vagal tension.2.The average of multi-scale entropy in healthy people is the largest,followed by subjects with low risk of hypertension,indicating that the complexity of HRV signals in healthy people is the highest.The mean multi-scale entropy of high-risk subjects is the smallest on all scales,indicating that the complexity of HRV signals in high-risk subjects is the lowest.3.The average of the entropy(EDD)and average(AverD)analysis indicators of the complex network degree of healthy people is the largest,followed by those with low risk of hypertension,indicating that the healthy people have the strongest nonlinear coupling in the cardiac system,and hypertension has This function of the human body has a negative effect.The mean of EDD and AverD of the complex network analysis indicators of high-risk subjects with hypertension is the smallest,indicating that the random network of highrisk subjects has the strongest randomness,the nonlinear coupling of the cardiac system is the weakest,and the heart health is deteriorating,which needs to cause patients and their families.Vigilance.4.The Hurst index,singular spectrum width,multi-scale entropy value and two network indicators(EDD,AverD)were tested by independent sample T to distinguish healthy subjects,highrisk subjects of hypertension cardiovascular events and low-risk subjects(P<0.05).That is,these indicators can be used as a double-layer early warning indicator for the clinical treatment of hypertension,the hypertension health care system in daily life,and the stratification of cardiovascular events in patients with hypertension.In this paper,the traditional nonlinear methods of time series and complex networks are combined to analyze the HRV signal characteristics of hypertension patients more commonly in daily life from multiple perspectives.This analysis mode is used for the first time to assess the risk of cardiovascular events in hypertension patients Layered research.The results of this study provide a double-layer early-warning index for the diagnosis of hypertension and the risk of heart health in hypertensive patients.