The invention relates to a ventricular premature beat intelligent analysis method based on abnormal characteristic values. The method comprises the following steps: 1) signal preprocessing: denoisingan original signal by using a wavelet filter, positioning a QRS composite wave through digital analysis of slope, amplitude and width, and finally segmenting and extracting a single heart beat from acomplete ECG signal by taking an R peak as a center; 2) feature extraction: selecting a QRS composite wave area, an RR interval and a QRS amplitude vector sum as feature parameters for input of a classifier; 3) model training: repeatedly and randomly extracting k samples from the original training sample set in a place-back manner through a self-service method resampling technology to generate a new training sample set, then generating M classification trees according to the self-service sample set to form a random forest, and determining a classification result of new data according to a score formed by the voting amount of the classification trees. The method has the advantages that abnormal signals with abnormal electrocardiosignal centrality are identified accurately, and classification is accurate.
Ventricular premature beat intelligent analysis method based on abnormal characteristic values