Evaluation of an ECG heartbeat classifier designed by generalization-driven feature selection

In this work we studied the classification performance of feature models selected with a floating algorithm, focusing in the generalization capability. The features were extracted from the RR interval series, from all ECG leads and different scales of the wavelet transform. The generalization was studied using Physionet databases. In all databases the AAMI recommendations for class labeling and results presentation were followed. A floating feature selection algorithm was used to obtain the best performing and generalizing models in the training and validation sets for different search configurations. The best model found includes 8 features, was trained in a partition of the MIT-BIH Arrhythmia database, and was evaluated in a completely disjoint partition of the same database. The results obtained were: global accuracy of 93%; for normal beats, sensitivity (S) 95%, positive predictive value (P⁺) 98%; for supraventricular beats, S 77%, P⁺ 39%; for ventricular beats S 81%, P⁺ 87%. This classifier model has less features and performs better than other state of the art methods with results suggesting better generalization capability.