The Effect of Data Augmentation on Classification of Atrial Fibrillation in Short Single-Lead ECG Signals Using Deep Neural Networks

Hatamian, FN, Ravikumar, N, Vesal, S et al. (3 more authors) (2020) The Effect of Data Augmentation on Classification of Atrial Fibrillation in Short Single-Lead ECG Signals Using Deep Neural Networks. In: ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 04-08 May 2020, Barcelona, Spain. IEEE , pp. 1264-1268. ISBN 978-1-5090-6632-2

Abstract

Metadata

Authors/Creators:
  • Hatamian, FN
  • Ravikumar, N
  • Vesal, S
  • Kemeth, FP
  • Struck, M
  • Maier, A
Copyright, Publisher and Additional Information: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Keywords: atrial fibrillation , data augmentation , GMM , DCGAN
Dates:
  • Accepted: 24 January 2020
  • Published (online): 14 May 2020
  • Published: May 2020
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds)
Depositing User: Symplectic Publications
Date Deposited: 26 Jul 2021 13:48
Last Modified: 26 Jul 2021 16:09
Status: Published
Publisher: IEEE
Identification Number: https://doi.org/10.1109/icassp40776.2020.9053800

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