Electrocardiogram beat-classification based on a ResNet network

dc.contributor.author Machado,A en
dc.contributor.author Cláudia Vanessa Brito en
dc.contributor.author Sousa,A en
dc.contributor.other 7516 en
dc.date.accessioned 2020-01-07T14:58:15Z
dc.date.available 2020-01-07T14:58:15Z
dc.date.issued 2019 en
dc.description.abstract When dealing with electrocardiography (ECG) the main focus relies on the classification of the heart's electric activity and deep learning has been proving its value over the years classifying the heartbeats, exhibiting great performance when doing so. Following these assumptions, we propose a deep learning model based on a ResNet architecture with convolutional 1D layers to classify the beats into one of the 4 classes: normal, atrial premature contraction, premature ventricular contraction and others. Experimental results with MIT-BIH Arrhythmia Database confirmed that the model is able to perform well, obtaining an accuracy of 96% when using stochastic gradient descent (SGD) and 83% when using adaptive moment estimation (Adam), SGD also obtained F1-scores over 90% for the four classes proposed. A larger dataset was created and tested as unforeseen data for the trained model, proving that new tests should be done to improve the accuracy of it. © 2019 International Medical Informatics Association (IMIA) and IOS Press. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0). en
dc.identifier.uri http://repositorio.inesctec.pt/handle/123456789/10661
dc.identifier.uri http://dx.doi.org/10.3233/shti190182 en
dc.language eng en
dc.rights info:eu-repo/semantics/embargoedAccess en
dc.title Electrocardiogram beat-classification based on a ResNet network en
dc.type Publication en
dc.type conferenceObject en
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