Table of Contents
Algorithms, Volume 13, Issue 4 (April 2020) – 31 articles
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Cover Story (view full-size image) Finding an optimal and efficient combination of features and classifier is still an open problem in [...] Read more. Finding an optimal and efficient combination of features and classifier is still an open problem in the development of heartbeat classification algorithms for resource-constrained devices. In this paper, we present a novel study that combines random forests and feature selection based on the mutual information ranking criterion while following standard medical guidelines and inter-patient division of datasets. The best performance is obtained with the top six most informative features and a 40-tree classifier, resulting in an overall accuracy of 96.14% on the MIT-BIH Arrhythmia Database. In comparison with other state-of-the-art approaches tested under similar constraints, this work presents one of the highest performances reported to date while relying on a very small feature vector. View this paper