Electrocardiogram Classification Based On Rough Sets And Support Vector Machine

This paper studies electrocardiogram classification based on rough sets and support vector machine. First of all, it introduces the basic electrocardiogram and the theory and current application of rough sets, then review the theory and application of support vector machine based on the statistical learning theory. The weighed binary tree multi-classes classifications support vector machine is put forward at last. This paper evaluates the proposed model on T228 of Arrhythmia Database in MIT-BIH, which shows a better result compared with other methods in terms of classification speed and accuracy in the experiments.