Study On Heart Rate Variability Analysis Method Based On R Wave Detection Of ECG Signal

In recent years,many studies have shown that heart rate variability is associated with cardiovascular disease and can be an important indicator for the auxiliary diagnosis of cardiovascular disease.Therefore,it is very important to explore effective methods of heart rate variability analysis.As a powerful tool for measuring signal complexity,entropy analysis is very suitable for heart rate variability analysis of complex ecg signals.In this paper,heart rate variability analysis based on entropy is as follows:Firstly,the ecg signal is preprocessed based on IIR bandpass filter.For the original ECG signal has baseline drift,power frequency interference and other noise interference problems,in this paper,IIR bandpass filter is designed based on Butterworth filter due to its advantages of simple design and fewer parameters.According to the characteristic of the filter and the frequency characteristic of the signal,the parameter selection scheme of the filter is designed.Secondly,a Multi-scale Teager Energy Operator based R wave detection algorithm for ECG signals is proposed.Because Teager energy operator has the advantages of simple calculation and high temporal resolution,it is applied in the detection of R wave of ecg signal.However,clinical ECG signals mostly belong to low signal-to-noise ratio signals.When Teager energy operator is used to identify R waves,the problem of high false detection rate appears.In view of the low frequency sensitivity of a single scale of Teager energy operator to R wave,multi-scale parameters are introduced in this paper,and a detection algorithm based on multi-scale Teager energy operator R wave is proposed,increase the extraction of detail signals.Experimental results show that the R wave detection algorithm presented in this paper has a low error detection rate.Finally,a new multi-scale sample entropy method is proposed to analyze the heart rate variability.The sample entropy can only be used for single scale analysis of complex ECG signals,in the classical signal multi-scale method,the empirical mode decomposition algorithm establishes a multi-scale sample entropy method by analyzing the entropy of each scale based on the sample entropy after multi-scale decomposition of the signal.However,the decomposed signals of the empirical modal decomposition algorithm have some problems,such as modal alias.Therefore,a new multi-scale sample entropy method is proposed in this paper for heart rate variability analysis.The RR interval signals are obtained based on the detected R wave sequences,and then the sample entropy,the multi-scale sample entropy method based on empirical mode decomposition and the multi-scale sample entropy method proposed in this paper are compared respectively.Independent sample T test is used for difference test.The experimental results show that the multi-scale sample entropy method proposed in this paper results in the greatest difference level of entropy between groups,which can effectively distinguish the disease group from the healthy group.In this paper,an effective method of heart rate variability analysis based on R wave detection of ecg signals is proposed,and a more accurate analysis scheme is given from the perspective of multi-scale.In order to provide some reference value for the auxiliary diagnosis of cardiovascular diseases.