Cross-Database Evaluation of a Multilead Heartbeat Classifier

In this paper, we studied the improvement in heartbeat classification achieved by including information from multi-lead ECG recordings in a previously developed and validated classification model. This model includes features from the RR interval series and morphology descriptors for each lead calculated from the wavelet transform. The experiments were carried out in the IN-CART database, available in Physionet, and the generalization was corroborated in private and public databases. In all databases, the AAMI recommendations for class labeling and results presentation were followed. Different strategies to integrate the additional information available in the 12-leads were studied. The best performing strategy consisted in performing principal component analysis to the wavelet transform of the available ECG leads. The performance indices obtained for normal beats were sensitivity (S) 98%, positive predictive value (P+) 93%; for supraventricular beats, (S) 86%, (P+) 91%; and for ventricular beats (S) 90%, (P+) 90%. The generalization capability of the chosen strategy was confirmed by applying the classifier to other databases with different number of leads with comparable results. In conclusion, the performance of the reference two-lead classifier was improved by taking into account additional information from the 12-leads.