Analysis Of Heart Rate Signals Based On Phase-rectified Signal Averaging Algorithm

Dysregulation of autonomic nervous system(ANS)is an important cause of cardiovascular disease.Heart Rate Variability(HRV)is a conventional method to analyze ANS,but it is limited in disnguishing deceleration-and acceleration-related HRV.Phase-rectified signal averaging(PRSA)is a novel algorithm for heart rate signal analysis.It allows calculation of deceleration capacity(DC)and acceleration capacity(AC)of heart rate indices from the heart rate signal under different time scale(T)and wavelet scale(s)for assessing deceleration-and acceleration-related HRV.DC and AC are superior to the conventional HRV.However,the following problems still remain unresolved: 1.The relationship between the indices and the ANS remains controversial.2.The performances of DC and AC are distinct,but the reason is unclear.3.The performance of DC and AC needs to be improved.Physiological experiments and clinical trials are limited in studying these problems.In this thesis,the problems were investigated using engineering approaches,such as heart rate signal modeling and algorithm modification.The following researches were conducted.1.Multi-scale DC and AC were calculated by applying PRSA to RR interval(RRI)time series generated by a cardiovascular system model.The model allows analysis of sympathetic and vagal activity for analyzing the relationship of DC and AC to the ANS,respectively.The results indicate that: the correlations of the indices to ANS functions are influenced by time and wavelet scales.Under the conventional scales(T = 1,s = 2),both DC and AC were solely dependent on vagal activity.With higher scales(T = 3,s = 5),both DC and AC were positively correlated to sympathetic activity and negatively correlated to vagal activity.2.It is hypothesized that the presence of heart rate asymmetry(HRA)leads to different performances of DC and AC.The relative performance of DC and AC under different HRA levels was analyzed by adjusting the inspiration/expiration(I/E)ratio in the cardiovascular system model.The results indicate that: the HRA level determined which DC or AC is the optimal index for express ANS functions.An HRA level abvoe 50% resulted in a strong association of DC with the ANS.3.RRI time series was interpolated and resampled for the PRSA analysis to avoid the unevenly sampling problem.The effect of the modification was examined based on healthy and chronic heart failure subjects provided by clinical databases.The results indicate that: the enhanced algorithm improved the accuary of diagnosis of chronic heart failure patients from 79.8% to 92.6%.The study clarifies the relationship of the indices with the ANS function,and indicates that the modified PRSA improves the performance of the index.It can promote the application of the novel signal processing algorithm and its relevant indices in future clinical use.