{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T16:45:41Z","timestamp":1791996341420,"version":"4.4.0"},"reference-count":39,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2020,3,25]],"date-time":"2020-03-25T00:00:00Z","timestamp":1585094400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100009873","name":"Regione Autonoma della Sardegna","doi-asserted-by":"publisher","award":["Determinazione no. 2171\/176 del 07.03.2016"],"award-info":[{"award-number":["Determinazione no. 2171\/176 del 07.03.2016"]}],"id":[{"id":"10.13039\/501100009873","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>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\u2013R) 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.<\/jats:p>","DOI":"10.3390\/a13040075","type":"journal-article","created":{"date-parts":[[2020,3,25]],"date-time":"2020-03-25T13:10:47Z","timestamp":1585141847000},"page":"75","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Investigating Feature Selection and Random Forests for Inter-Patient Heartbeat Classification"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1800-2616","authenticated-orcid":false,"given":"Jose Francisco","family":"Saenz-Cogollo","sequence":"first","affiliation":[{"name":"Center for Advanced Studies, Research, and Development in Sardinia (CRS4), Localit\u00e0 Pixina Manna, Edificio 1, 09010 Pula (CA), Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maurizio","family":"Agelli","sequence":"additional","affiliation":[{"name":"Center for Advanced Studies, Research, and Development in Sardinia (CRS4), Localit\u00e0 Pixina Manna, Edificio 1, 09010 Pula (CA), Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,3,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Scir\u00e8, A., Tropeano, F., Anagnostopoulos, A., and Chatzigiannakis, I. 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