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• W4384283075 abstract "Abstract 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 $$2.2%$$ <mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML> <mml:mrow> <mml:mn>2.2</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> and $$6.1%$$ <mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML> <mml:mrow> <mml:mn>6.1</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> ." @default.
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• W4384283075 date "2023-07-14" @default.
• W4384283075 modified "2023-09-25" @default.
• W4384283075 title "Accurate detection of paroxysmal atrial fibrillation with certified-GAN and neural architecture search" @default.
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• W4384283075 doi "https://doi.org/10.1038/s41598-023-38541-8" @default.
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