Key result
A random forest classifier using selected features achieved an overall accuracy of 96.14% and F1-scores up to 97.97% for heartbeat classification on the MIT-BIH Arrhythmia Database.
Why the study?
Finding an optimal combination of features and classifier remains an open problem in developing automatic heartbeat classification systems, particularly for resource-constrained devices.
A random forest classifier with a small feature vector achieved high accuracy in inter-patient heartbeat classification, suitable for resource-constrained devices.
May support efficient arrhythmia detection on resource-constrained devices; leaves open prospective clinical validation before adoption.
Finding an optimal combination of features and classifier is still an open problem in the development of automatic heartbeat classification systems, especially when applications that involve resource-constrained devices are considered. In this paper, a novel study of the selection of informative features and the use of a random forest classifier while following the recommendations of the Association for the Advancement of Medical Instrumentation (AAMI) and an inter-patient division of datasets is presented. Features were selected using a filter method based on the mutual information ranking criterion on the training set. Results showed that normalized R-R intervals and features relative to the width of the QRS complex are the most discriminative among those considered. The best results achieved on the MIT-BIH Arrhythmia Database were an overall accuracy of 96.14% and F1-scores of 97.97%, 73.06%, and 90.85% in the classification of normal beats, supraventricular ectopic beats, and ventricular ectopic beats respectively. In comparison with other state of the art approaches tested under similar constraints, this work represents one of the highest performances reported to date while relying on a very small feature vector.
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Saenz-Cogollo et al. (2020) studied Arrhythmia. Random forest classifier with feature selection vs. Other state of the art approaches was evaluated on Overall accuracy and F1-scores for heartbeat classification. A random forest classifier using selected features achieved an overall accuracy of 96.14% and F1-scores up to 97.97% for heartbeat classification on the MIT-BIH Arrhythmia Database.
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