ECG Beat Classifier Using Support Vector Machine

This paper introduces a new method of heartbeat classification based on the support vector machine classifier using morphological descriptors and High Order Statistic using MIT/BIH Arrhythmia database. Using the morphological descriptors and polynomial kernel, we have obtained an average sensitivity equal to 89,92% and an average specificity about 82,45%, and in the case of Gaussian kernel, we have obtained an average sensitivity equal to 94,26% and an average specificity about 79,02%. Using the High Order Statistic and polynomial kernel, we have obtained an average sensitivity equal to 95,86% and an average specificity about 90,20%, and in the case of Gaussian kernel, we have obtained an average sensitivity equal to 97,15% and an average specificity about 93,07%. The association of the two parameters increases the averages of classification rates; so the sensitivity is 98,38% and the specificity to 94,87% with polynomial kernel and respectively about 94,43% et 95,81 % with Gaussian kernel.