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Accurate detection of paroxysmal atrial fibrillation with certified-GAN and neural architecture search
Department of Electrical Engineering, Tarbiat Modares University, Tehran, Iran.
Shiraz University of Medical Science, Shiraz, Iran.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
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2023 (English)In: Scientific Reports, E-ISSN 2045-2322, Vol. 13, no 1Article in journal (Refereed) Published
Abstract [en]

This paper presents a novel machine learning framework for detecting PxAF, a pathological characteristic of electrocardiogram (ECG) that can lead to fatal conditions such as heart attack. To enhance the learning process, the framework involves a generative adversarial network (GAN) along with a neural architecture search (NAS) in the data preparation and classifier optimization phases. The GAN is innovatively invoked to overcome the class imbalance of the training data by producing the synthetic ECG for PxAF class in a certified manner. The effect of the certified GAN is statistically validated. Instead of using a general-purpose classifier, the NAS automatically designs a highly accurate convolutional neural network architecture customized for the PxAF classification task. Experimental results show that the accuracy of the proposed framework exhibits a high value of 99.0% which not only enhances state-of-the-art by up to 5.1%, but also improves the classification performance of the two widely-accepted baseline methods, ResNet-18, and Auto-Sklearn, by [Formula: see text] and [Formula: see text].

Place, publisher, year, edition, pages
NLM (Medline) , 2023. Vol. 13, no 1
Keywords [en]
Atrial Fibrillation, Electrocardiography, Humans, Machine Learning, Neural Networks, Computer, artificial neural network, human
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:mdh:diva-63915DOI: 10.1038/s41598-023-38541-8ISI: 001030642400009PubMedID: 37452165Scopus ID: 2-s2.0-85164756079OAI: oai:DiVA.org:mdh-63915DiVA, id: diva2:1784492
Available from: 2023-07-26 Created: 2023-07-26 Last updated: 2023-09-13Bibliographically approved

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Loni, MohammadDaneshtalab, MasoudSjödin, Mikael

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