Diagnosing heart diseases using morphological and dynamic features of electrocardiogram (ECG)

Abstract

In this paper, an automatic method is proposed for the heart beat classification of 15 classes mapped into five main categories. The proposed method is applied separately to both leads 1 and 2. Dynamic segmentation is considered to reduce the effect of the heart beat rate variation. The segmented beats are subjected to discrete wavelet decomposition (DWT) to extract the morphological features besides the dynamic features represented by four RR intervals. Principle component analysis (PCA) is considered to reduce the dimension of the extracted morphological features. After that, the reduced features are concatenated with the dynamic features and fed into Support vector machine (SVM) classifier. Finally, the rejection fusion step is applied to combine the results from both leads 1 and 2 with a 93.84% average accuracy and 99.5% overall accuracy having been achieved using MIT-BIH dataset as a validation database.