Modeling And Recognition Of ECG Beat

ECG (electrocardiogram) beat classification is very important for clinical automated detection of arrhythmia. Based on previous work, this paper focuses on the further research on recognition and classification of ventricular premature beats and normal beats.Firstly, various existed methods for arrhythmia recognition are categorized and summarized. Comparing the methods in detail, the problems are discussed in depth and the main handicaps in arrhythmia analysis are given.Secondly, the shape of QRS complex wave, RR duration variation and predominant rhythm are analyzed in detail. The results are valuable to arrhythmia classification.Thirdly, the mirror Gauss model(MGM) of QRS complex wave is presented. As the variation of QRS complex wave is the primary reference criteria on clinical diagnosis of arrhythmia, exact extraction of the features of QRS complex wave is the key problem. Based on study in shape, the method of fitting QRS complex by Gauss function curve is presented to extract the width of QRS complex easily. By employing mirror operation, more high precision is achieved.Fourthly, cluster template which combine template queue with correlation match is introduced. If the QRS complex wave fails to match each template in the queue, then mirror Gauss model will be practiced. By the application of the cluster template, the frequency of Gauss fitting is greatly reduced, and even high efficiency is reached in the assurance of detection precision. On the other hand, as cluster template can distinguish multi-class of ventricular premature beats effectively, it may instruct the clinical diagnosis of multiform ventricular premature.Finally, an integrated algorithm for recognition and classification of normal and ventricular premature beats is implemented, which utilizes other technologies, such as mathematical morphology filtering and fuzzy reasoning, based on the mirror Gauss model and cluster template.The complete set of MIT-BIH arrhythmia database is used to evaluate this algorithm, the gross sensitivity of normal beats and ventricular premature beats are 93.01% and 94.25% respectively. With the result better than other algorithm which used the same test set, the analysis speed is also satisfying. It is proved by experiment that mirror Gauss model is able to effectively describe the QRS complex wave shape of other common arrhythmias.