The Design Of Dynamic Electrocardiogram Qrs Wave Detection Algorithm Using Derivatized Leads

Cardiovascular disease is one of the most common diseases in the world in recent years.It is a common circulatory system disease and it seriously threatens human health.The 24-hour dynamic electrocardiograph is the main device to diagnose heart disease in the modern medical field.However,the acquired ECG data is easily mixed with various noises,resulting in inaccurate automatic ECG analysis results.QRS wave detection is the basis of automatic ECG analysis.Therefore,QRS wave detection of dynamic ECG has become a research hotspot.In this paper,QRS wave detection algorithm is studied based on ECG data of real-time Holter system.This paper analyzes the interference in dynamic ECG data,then generates derivatized leads on the basis of12 leads,and proposes a method to analyze ECG with motion artifact.The method can effectively reduce the situation that the QRS wave is misdetected due to motion interference.This paper designs a QRS wave feature extraction method based on wavelet transform,and recovers the QRS characteristic waveform by wavelet decomposition and reconstruction.The Shannon transform envelope method was used to enhance the QRS characteristic wave,which effectively reduced the abnormal heartbeat missed detection of premature atrial premature beats.The dynamic adaptive threshold method is used to detect the peak value of Shannon energy envelope to achieve the purpose of detecting QRS wave position.The detection rate of QRS wave in the MIT-BIH arrhythmia database is 99.93%,and the positive predictive rate is 99.86%.In the AHA database,the detection rate of QRS wave is 99.90%,and the positive predictive rate is 99.72%.Compared with other QRS wave detection algorithms of dynamic electrocardiogram,this algorithm has certain advantages.