Automated patient-specific classification of premature ventricular contractions.

In this paper, we present an automated patient-specific electrocardiogram (ECG) beat classifier designed for accurate detection of premature ventricular contractions (PVCs). In the proposed feature extraction scheme, the principal component analysis (PCA) is applied to the dyadic wavelet transform (DWT) of the ECG signal to extract morphological ECG features, which are then combined with the temporal features to form a resultant efficient feature vector. For the classification scheme, we selected the feed-forward artificial neural networks (ANNs) optimally designed by the multi-dimensional particle swarm optimization (MD-PSO) technique, which evolves the structure and weights of the network specifically for each patient. Training data for the ANN classifier include both global (total of 150 representative beats randomly sampled from each class in selected training files) and local (the first 5 min of a patient's ECG recording) training patterns. Simulation results using 40 files in the MIT/BIH arrhythmia database achieved high average accuracy of 97% for differentiating normal, PVC, and other beats.