Dynamic Ecg Automatic Detection And Analysis Methods

Under the background of the automatic analyses system of the dynamic electrocardiogram, a method of de-noising via wavelet threshold was studied. By comparing four threshold-select rules, the Donoho immovable threshold was used to eliminate the 50Hz interference and muscle artifact of the electrocardiograms, which leads to excellent outcomes.A new method using wavelet approximation to remove the electrocardiogram baseline wander is proposed in this dissertation. Via the multi-resolution analysis of the original signal, the baseline wander of electrocardiogram can be characterized almost fully by the approximation signal. Then the baseline wander can be removed by dealing with the approximation signal. Applying this method to the real 30min-ECG-logs provided by MIT-BIH Arrhythmia Database, the experiment results show that it can effectively remove the electrocardiogram's baseline wander, and have little harm to the other elements of the signals.During the process of character detection of the electrocardiogram, a method based on wavelet was improved to detect the QRS complex. It makes use of not only the wavelet coefficients, but also the original signals to find the accurate position of the peak wave of QRS complex. In the experiments, 99.99% signals can be detected exactly by using this method. After QRS detection, the P wave and T wave is detected by the information of the amplitude and slope in certain section. This method works well for normal signals yet it isn't fit for the unmoral signals and diseased signals.Finally, an automatic analysing method of Arrhythmia based on decision tree was designed in this dissertation. The constructing of the decision tree and the producing of diagnostic rules were shown by the example of sinus rhythm and ectopic rhythm decision tree.