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Classifying electrocardiogram (ECG) heartbeats for arrhythmic risk prediction is a challenging task due to minute variations in the amplitude, duration and morphology of the ECG signal. In this paper, we propose two feature extraction approaches to classify five types of heartbeats: normal, premature ventricular contraction, atrial premature contraction, left bundle branch block and right bundle branch block. In the first approach, ECG beats are decomposed into intrinsic mode functions (IMFs) using ensemble empirical mode decomposition (EEMD). Later four parameters, namely the sample entropy, coefficient of variation, singular values, and band power of IMFs are extracted as features. In the second approach, the same features are computed from IMFs extracted using an empirical mode decomposition (EMD) algorithm. The features obtained from the two approaches are independently fed to the sequential minimal …