A Generic and Patient-Specific Electrocardiogram Signal Classification System

Each individual heartbeat in the cardiac cycle of the recorded electrocardiogram (ECG) waveform shows the time evolution of the heart’s electrical activity, which is made of distinct electrical depolarization–repolarization patterns of the heart. Any disorder of heart rate or rhythm, or change in the morphological pattern is an indication of an arrhythmia, which could be detected by analysis of the recorded ECG waveform. Real-time automated ECG analysis in clinical settings is of great assistance to clinicians in detecting cardiac arrhythmias, which often arise as a consequence of a cardiac disease and may be life-threatening and require immediate therapy. However, automated classification of ECG beats is a challenging problem as the morphological and temporal characteristics of ECG signals show significant variations for different patients and under different temporal and physical conditions (Hoekema et al. 2001). Many algorithms for automatic detection and classification of ECG heartbeat patterns have been presented in the literature including signal processing techniques such as frequency analysis (Minami et al. 1999), wavelet transform (Shyu et al. 2004; Inan et al. 2006), and filter banks (Alfonso and Nguyen 1999), statistical (Willems and Lesaffre 1987) and heuristic approaches (Talmon 1983), hidden Markov models (Coast et al. 1990), support vector machines (Osowski et al. 2004), artificial neural networks (ANNs) (Hu et al. 1994), and mixture-of-experts method (Hu et al. 1997). In general, ECG classifier systems based on past approaches have not performed well in