Název:
Atrial Fibrillation Classification Using Deep Convolution Networks
Autoři:
Novotna, Petra Typ dokumentu: Příspěvky z konference
Jazyk:
eng
Nakladatel: Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií
Abstrakt:
We propose the usage of three deep convolutional neural networks architectures for classification of a single lead electrocardiogram (ECG) recordings and evaluate them on the atrial fibrillation (AFIB) classification, for which data set was provided by the Department of Biomedical Engineering, BUT. The compared networks are based on ResNet, VGG net and AlexNet. Single lead signals are transformed into the form of spectrogram. AFIB data was augmented for the purpose of similar size of both respected classes and for successful classification. The most successful architecture, based on AlexNet, was found to perform obtaining an accuracy of 92 % and F1 score of 56 % on the hidden testing set.
Klíčová slova:
alexnet; atrial fibrillation; convolution; deep learning; ECG; neural networks; resnet; signal processing classification; vgg Zdrojový dokument: Proceedings I of the 26st Conference STUDENT EEICT 2020: General papers, ISBN 978-80-214-5867-3
Instituce: Vysoké učení technické v Brně
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Informace o dostupnosti dokumentu:
Plný text je dostupný v Digitální knihovně VUT. Původní záznam: http://hdl.handle.net/11012/200593