A Generic and Robust System for Automated Patient-Specific Classification of Electrocardiogram Signals

This paper presents a generic and patient-specific classification system designed for robust and accurate detection of electrocardiogram (ECG) heartbeat patterns. The proposed feature extraction process utilizes morphological wavelet transform features, which are projected onto a lower-dimensional feature space using principal component analysis, and temporal features from the ECG data. For the pattern recognition unit, feed-forward and fully-connected artificial neural networks (ANNs), which are optimally designed for each patient by the proposed multi-dimensional particle swarm optimization (MD PSO) technique, are employed. By using relatively small common and patient-specific training data, the proposed classification system can adapt to significant inter-patient variations in ECG patterns by training the optimal network structure and thus achieves higher accuracy over larger data sets. The classification experiments over a benchmark database demonstrate that the proposed system achieves such average accuracies and sensitivities better than most of the current state-of-the-art algorithms for detection of ventricular ectopic beats (VEB) and supraventricular ectopic beats (SVEB). Over the entire database, the average accuracy sensitivity performances of the proposed system for VEB and SVEB detections are 98.3% 84.6%, and 97.4% 63.5%, respectively. Finally, due to its parameter invariant nature, the proposed system is highly generic and thus applicable to any ECG dataset.