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This paper describes a method for the classification of heartbeats based on electrocardiogram QRS complex and T wave autoregressive (AR) features and interbeat distance (RR interval) features. The QRS complex (length n samples) is modelled using AR model of order P. A second AR model of order P1 is applied to the electrocardiogram segment including ST segment and T wave (ST/T segment) with a length of n1 samples. In addition, RR interval features such as the distance between one heartbeat and its preceding (pre-RR) and following (post-RR) heartbeats are extracted. The use of AR modelling is motivated by the fact that QRS complex and ST/T segment corresponding to normal beats present different patterns (in shapes and amplitudes) than those corresponding to ventricular ectopic beats. Lengths and AR model orders that minimize the number of misclassifications are determined. Obtained features are used to train and test support vector machine classifier. The proposed method is tested on MIT/BIH Arrhythmia Database and it is compared to state of the art methods. Overall classification accuracy reached 97.02%, whereas it reached 98.86% in the case of subject-specific scheme. Results are better or comparable to those obtained by state of the art methods. [ABSTRACT FROM AUTHOR]