The prompt and adequate detection of abnormal cardiac conditions by computer-assisted long-term monitoring systems depends greatly on the reliability of the implemented ECG automatic analysis technique, which has to discriminate between different types of heartbeats. In this paper, we present a comparative study of the heartbeat classification abilities of two techniques for extraction of characteristic heartbeat features from the ECG: (i) QRS pattern recognition method for computation of a large collection of morphological QRS descriptors; (ii) Matching Pursuits algorithm for calculation of expansion coefficients, which represent the time-frequency correlation of the heartbeats with extracted learning basic waveforms. The Kth nearest neighbour classification rule has been applied for assessment of the performances of the two ECG feature sets with the MIT-BIH arrhythmia database for QRS classification in five heartbeat types (normal beats, left and right bundle branch blocks, premature ventricular contractions and paced beats), as well as with five learning datasets-one general learning set (GLS, containing 424 heartbeats) and four local sets (GLS+about 0.5, 3, 6, 12 min from the beginning of the ECG recording). The achieved accuracies by the two methods are sufficiently high and do not show significant differences. Although the GLS was selected to comprise almost all types of appearing heartbeat waveforms in each file, the guaranteed accuracy (sensitivity between 90.7% and 99%, specificity between 95.5% and 99.9%) was reasonably improved when including patient-specific local learning set (sensitivity between 94.8% and 99.9%, specificity between 98.6% and 99.9%), with optimal size found to be about 3 min. The repeating waveforms, like normal beats, blocks, paced beats are better classified by the Matching Pursuits time-frequency descriptors, while the wide variety of bizarre premature ventricular contractions are better recognized by the morphological descriptors.