Based On The Dual Channel Ecg R Wave Detection Algorithm Research

Cardiovascular disease has been one of major diseases that seriously threaten the health of people. Ectrocardiogram (ECG) is the scientific evidence of clinical medicine in diagnosis of heart disease and analysis of electrocardiosignal, and the effective treatment of cardiovascular disease largely depends on the accuracy of electrocardiosignal detection and analysis. Therefore, the detection and analysis of electrocardiosignal has been a research hotspot in the current signal processing field. In this thesis, pretreatment of electrocardiosignal and R wave detection are as the main research contents.Electrocardiosignal is a weak and non-stationary random signal, so it is vulnerable to the interference of all kinds of noise. This thesis was focused on the algorithm of removal of electrocardiosignal baseline drifts noise based on wavelet transform. According to the frequency characteristics of electrocardiosignal and baseline drifts noise, this thesis decomposed the electrocardiosignal to multi-scale based on multi-resolution analysis of wavelet transform, utilized the analog approximate signal obtained after decomposition to fully close to the characteristics of the baseline drift noise to remove the electrocardiosignal baseline drift in the low-frequency components, and reconstrutct the signal to get a clearer electrocardiosignal.In the detection analysis of the electrocardiosignal, the most key issue is the positioning of the R-wave. The traditional differential R-wave detection algorithms have certain limitation, and the fitness of threshold will directly influence the effect of dection. Therefore, this thesis proposed a difference absolute value algorithm. This algorithm can position R-wave through determing the location of R waves to reduce the dependence of the threshold.This method is not only a simple algorithm, less calculation. The current R-wave detection algorithms are often only detecting a kind of lead electrocardiosignal, so many signal characteriscs can not fully extracted. A dual-channel detection method proposedin this thesis can detect and analyze the electrocardiosignal in double channels using many kinds of algorithms to detect more R waves. Through the practical detection for the electrocardiosignal in the MIT-BTH database, the higher recognition rate of the algorithm is verified to achieve better detection results. The R-wave detection method based on dual-channel proposed in this thesis provides a novel detection mean for the automatic analysis of electrocardiosignal.