Research Of Key Technology To ECG Signal Processing

Cardiopathy is a chronic and severe disease that threats people's lives because of its high dangerousness. As one of biological electric signals which are researched and applied on clinical medicine firstly, ECG is still the main method to record heart activities in the process of cardiopathy diagnosing. It plays a decisive role to kinds of arrhythmia and conduction disorder diagnosis.Domestic and foreign scholars have been being active on the research of ECG signal processing technology. The research for ECG signal extraction and ECG waveform detection is great of practical significance to the diagnosis of cardiopathy and evaluation of cardiac function based on extensive research of existing ECG signal processing methods and ECG response characteristics, the research of key technology to ECG signal processing on basis of wavelet analysis theory is studied in this dissertation. It includes de-noising and QRS waveform detecting of ECG signal.ECG Signal De-noising Algorithm1 According to the concept of multi-resolution analysis of wavelet transform, ECG signal is decomposed to 8th scale by using coif4 wavelet and Mallat algorithms.2 The ECG signal de-noising algorithm combining the wavelet decomposing and reconstructing method and wavelet threshold method is used by comparing these two methods and referencing the frequency distribution of each scale of ECG signal. 3 At first, algorithm uses wavelet decomposing and reconstructing method make approximation signal on 8th scale to be zero, which can remove baseline drift effectively. Then the algorithm uses soft threshold method to remove EMG interference and frequency interference on the first scale to the third scale. At last it uses soft and hard threshold compromised method to remove EMG interference on large-scale.4 The clean ECG signal is the signal reconstructed on each scale after being dealt with.QRS Waveform of ECG Signal Detecting Algorithm.1 The algorithm uses the porous algorithm to decompose the ECG signal. This can make the decomposing wavelet transforming coefficient be the same length with the original signal, which is necessary to detect the singularity of ECG signal. Quadratic Spline wavelet is chosen to be wavelet and the decomposing scale is 4.2 According selected threshold of each scale, the method determining modulus maxima line is that searching for the maximum value from large-scale to small-scale.3 The algorithm makes use of Lee's index of feature point to remove the modulus maxima line because of interference on each scale. Then, it removes isolated and redundant modulus maxima line.4 In order to minimize the impact of the high–frequency noise on small-scale, the algorithm takes great value-small value anode of R wave peak corresponding to s = 22 scale to shift time amendment.5 At last, the algorithm executes compensation and leak detecting prevention strategy for R wave peak detection according the physiological characteristics of the human body.Besides, it also detects the beginning and ending points of QRS wave after detecting R wave peak. The two algorithms in this dissertation both use the signal in the MIT/BIH arrhythmia database to carry out the simulation and they both get better results.