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Article

Investigating Feature Selection and Random Forests for Inter-Patient Heartbeat Classification

Center for Advanced Studies, Research, and Development in Sardinia (CRS4), Località Pixina Manna, Edificio 1, 09010 Pula (CA), Italy
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Algorithms 2020, 13(4), 75; https://doi.org/10.3390/a13040075
Received: 25 February 2020 / Revised: 20 March 2020 / Accepted: 22 March 2020 / Published: 25 March 2020
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 beat-to-beat (R–R) intervals and features relative to the width of the ventricular depolarization waves (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. View Full-Text
Keywords: ECG feature selection; heartbeat classification; arrhythmia detection; random forest classifier ECG feature selection; heartbeat classification; arrhythmia detection; random forest classifier
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MDPI and ACS Style

Saenz-Cogollo, J.F.; Agelli, M. Investigating Feature Selection and Random Forests for Inter-Patient Heartbeat Classification. Algorithms 2020, 13, 75. https://doi.org/10.3390/a13040075

AMA Style

Saenz-Cogollo JF, Agelli M. Investigating Feature Selection and Random Forests for Inter-Patient Heartbeat Classification. Algorithms. 2020; 13(4):75. https://doi.org/10.3390/a13040075

Chicago/Turabian Style

Saenz-Cogollo, Jose F., and Maurizio Agelli. 2020. "Investigating Feature Selection and Random Forests for Inter-Patient Heartbeat Classification" Algorithms 13, no. 4: 75. https://doi.org/10.3390/a13040075

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