Original title: Deep Convolutional Neural Network Model For Classification Of Atrial Fibrillation
Authors: Budíková, Barbora
Document type: Papers
Language: cze
Publisher: Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií
Abstract: Atrial fibrillation is a very common heart pathology, which is usually detected from electrocardiogram (ECG). This article presents recognition of atrial fibrillation in ECG using deep convolutional neural network. Data used for training the network includes physiological ECG, atrial fibrillation and nine other pathologies. The detection is performed by algorithm in Python language and is being assessed by accuracy and F1 measure.
Keywords: atrial fibrillation; convolutional neural network; detection; ECG
Host item entry: Proceedings I of the 26st Conference STUDENT EEICT 2020: General papers, ISBN 978-80-214-5867-3

Institution: Brno University of Technology (web)
Document availability information: Fulltext is available in the Brno University of Technology Digital Library.
Original record: http://hdl.handle.net/11012/200560

Permalink: http://www.nusl.cz/ntk/nusl-447612


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