{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T16:45:33Z","timestamp":1791996333369,"version":"4.4.0"},"reference-count":20,"publisher":"Public Library of Science (PLoS)","issue":"8","license":[{"start":{"date-parts":[[2022,8,18]],"date-time":"2022-08-18T00:00:00Z","timestamp":1660780800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>Detection of Premature Ventricular Contractions (PVC) is of crucial importance in the cardiology field, not only to improve the health system but also to reduce the workload of experts who analyze electrocardiograms (ECG) manually. PVC is a non-harmful common occurrence represented by extra heartbeats, whose diagnosis is not always easily identifiable, especially when done by long-term manual ECG analysis. In some cases, it may lead to disastrous consequences when associated with other pathologies. This work introduces an approach to identify PVCs using machine learning techniques without feature extraction and cross-validation techniques. In particular, a group of six classifiers has been used: Decision Tree, Random Forest, Long-Short Term Memory (LSTM), Bidirectional LSTM, ResNet-18, MobileNetv2, and ShuffleNet. Two types of experiments have been performed on data extracted from the MIT-BIH Arrhythmia database: <jats:italic>(i)<\/jats:italic> the original dataset and <jats:italic>(ii)<\/jats:italic> the balanced dataset. MobileNetv2 came in first in both experiments with high performance and promising results for PVCs\u2019 final diagnosis. The final results showed 99.90% of accuracy in the first experiment and 99.00% in the second one, despite no feature detection techniques were used. The approach we used, which was focused on classification without using feature extraction and cross-validation techniques, allowed us to provide excellent performance and obtain better results. Finally, this research defines as first step toward understanding the explanations for deep learning models\u2019 incorrect classifications.<\/jats:p>","DOI":"10.1371\/journal.pone.0268555","type":"journal-article","created":{"date-parts":[[2022,8,18]],"date-time":"2022-08-18T17:49:47Z","timestamp":1660844987000},"page":"e0268555","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":33,"title":["Classification of QRS complexes to detect Premature Ventricular Contraction using machine learning techniques"],"prefix":"10.1371","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4285-9502","authenticated-orcid":true,"given":"Fabiola","family":"De Marco","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Filomena","family":"Ferrucci","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michele","family":"Risi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Genoveffa","family":"Tortora","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2022,8,18]]},"reference":[{"issue":"10","key":"pone.0268555.ref001","doi-asserted-by":"crossref","first-page":"e1332","DOI":"10.1016\/S2214-109X(19)30318-3","article-title":"World Health Organization cardiovascular disease risk charts: Revised models to estimate risk in 21 global region","volume":"7","author":"The WHO CVD Risk Chart Working Group","year":"2019","journal-title":"The Lancet Global Health"},{"issue":"10","key":"pone.0268555.ref002","doi-asserted-by":"crossref","first-page":"e1288","DOI":"10.1016\/S2214-109X(19)30365-1","article-title":"Cardiovascular disease risk prediction models: challenges and perspectives","volume":"7","author":"F Farzadfar","year":"2019","journal-title":"The Lancet Global Health"},{"issue":"10","key":"pone.0268555.ref003","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0024386","article-title":"Improving ECG classification accuracy using an ensemble of neural network modules","volume":"6","author":"M. 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