Cardiac problems are the main reason of people's death nowadays. However, one way that light save the life is the analysis of the an electrocardiograph. This analysis consist in the diagnosis of the arrhythmia when it presents. In this paper, we propose to combine the Support Vector Machines used in classification on one hand, with the Principal Component Analysis used in order to reduce the size of the data by choosing some axes that capture the most variance between data and on the other hand, with the kernel principal component analysis where a mapping to a high dimensional space is needed to capture the most relevant axes but for nonlinear separable data. The efficiency of the proposed SVM classification is illustrated on real electrocardiogram dataset taken from MIT-BIH Arrhythmia Database.