Analysis Of Ventircular Premature Contraction And Atrial Premature Beat ECG Signals Based On Multiscale Jensen-Shannon Divergence

The role of the heart is to promote blood flow,provide sufficient blood flow to organs and tissues to supply oxygen and various nutrients,and take away the end products of metabolism to maintain normal metabolism and function.Premature beats are very common arrhythmias.They can be the result or development of certain heart attacks,and premature ventricular contractions(VPC)and premature atrial contractions(APC)are serious heart diseases that threaten human life.A large number of interested scholars are actively involved in this research.The use of Jensen Shannon’s differentiation method to analyze ECG is a popular research methods.The characteristics of nonnegative,symmetrical,continuous and boundedness of JSD have been widely used in time series data analysis.The ECG signal can also be regarded as a time series.We consider the JSD-based complexity analysis method to study the degree of coupling between ECG time series of premature beat patients.This paper also calculates the statistical complexity of ECG signals in normal and premature beat patients by the improved multi-scale Jensen Shannon entropy algorithm,and compares the differences between the two,which has important practical significance for clinical diagnosis and treatment.The main contents of this paper are as follows:First,the proposed algorithm JSD compares healthy heart ECGS with ECG signals of the atrial premature beats and ECG signals of ventricular premature beat,and JSD is a symmetric and smooth result of Kullback-Leibler Divergence(KLD).Features such as non-negative,symmetrical,continuous,and boundedness of JSD have been widely used in time series data analysis,and ECG signals can also be regarded as a time series,which can be effectively used by JSD algorithm.The health ECG signal and the premature beat ECG signal indicate that there are significant differences in the ECG signals in the two states.The algorithm can be used for the diagnosis and treatment of various heart diseases,and can provide effective auxiliary effects.Second,the proposed algorithm multi-scale JSD compares healthy heart ECGS with ECG signals of the atrial premature beats and ECG signals of ventricular premature beat.The accuracy and reliability of the experiments are verified by SPSS statistical software.The error bar graph method was used to analyze the ECG signals of atrial premature beats and premature ventricular contractions as well as healthy status.We consider the JSD-based complexity analysis method to study the degree of coupling between ECG time series of premature beat patients,and calculate the statistical complexity of ECG signals in normal and premature beat patients by the improved multi-scale Jensen Shannon entropy algorithm to compare the differences between the Health,APB and PVC ECGS which has important practical significance for clinical diagnosis and treatment.Third,the multi-scale Jensen Shannon difference algorithm was used to calculate the statistical complexity of ventricular premature beats and atrial premature beats in the MLII lead mode and V5 lead mode,and verified by SPSS statistical software on the results of the two experiments of MLII lead mode and V5 lead mod.The two sets of experimental results have achieved good results in both accuracy and reliability.In this paper,two different pathological ECG signals are analyzed by means of error bar graph method.The results show that the improved multi-scale Jensen Shannon bifurcation algorithm can effectively distinguish the ECG signals of healthy pathology and premature beat pathology,which means there are obvious differences between the state of health and premature beats.The results show that the JSD algorithm and the improved multi-scale JSD algorithm can effectively distinguish between healthy ECG signals and premature beat ECG signals,indicating that there are significant differences between the two states and the healthy ECG signals.This research has important practical guiding significance because of that the algorithm can be further used in the staging study of ECG,and can be used for the diagnosis and treatment of various sleep diseases,and can provide effective auxiliary effects.