Fetching the paper…
Reading the bibliography…
Electrocardiogram (ECG) recordings have long been vital in diagnosing different cardiac conditions.
Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
Anh Nguyen, Jason Yosinski, and Jeff Clune · 2015
Earlier work this paper cites.
Real-time QRS detector using stationary wavelet transform for automated ECG analysis
Vignesh Kalidas and Lakshman Tamil · 2017
Cited alongside, same era.
Machine learning for ECG diagnosis and risk stratification of occlusion myocardial infarction
Salah S. Al-Zaiti, Christian Martin-Gill, Jessica K. Zègre-Hemsey, Zeineb Bouzid, Ziad Faramand, Mohammad O. Alrawashdeh, Richard E. Gregg, Stephanie Helman, Nathan T. Riek, Karina Kraevsky-Phillips, Gilles Clermont, Murat Akcakaya, Susan M. Sereika, Peter Van Dam, Stephen W. Smith, Yochai Birnbaum, Samir Saba, Ervin Sejdic, and Clifton W. Callaway
Cited in the paper.
Post hoc sample size estimation for deep learning architectures for ECG-classification
Lucas Bickmann, Lucas Plagwitz, and Julian Varghese
Cited in the paper.
Opportunities and obstacles for deep learning in biology and medicine
Travers Ching, Daniel S. Himmelstein, Brett K. Beaulieu-Jones, Alexandr A. Kalinin, Brian T. Do, Gregory P. Way, Enrico Ferrero, Paul-Michael Agapow, Michael Zietz, Michael M. Hoffman, Wei Xie, Gail L. Rosen, Benjamin J. Lengerich, Johnny Israeli, Jack Lanchantin, Stephen Woloszynek, Anne E. Carpenter, Avanti Shrikumar, Jinbo Xu, Evan M. Cofer, Christopher A. Lavender, Srinivas C. Turaga, Amr M. Alexandari, Zhiyong Lu, David J. Harris, Dave DeCaprio, Yanjun Qi, Anshul Kundaje, Yifan Peng, Laura K. Wiley, Marwin H. S. Segler, Simina M. Boca, S. Joshua Swamidass, Austin Huang, Anthony Gitter, and Casey S. Greene
Cited in the paper.
Fast and reliable QRS alignment technique for high-frequency analysis of signal-averaged ECG
O. J. Escalona, R. H. Mitchell, D. E. Balderson, and D. W. G. Harron
Cited in the paper.
Estimation of energy consumption in machine learning
Eva García-Martín, Crefeda Faviola Rodrigues, Graham Riley, and Håkan Grahn
Cited in the paper.
PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals
Ary L. Goldberger, Luis AN Amaral, Leon Glass, Jeffrey M. Hausdorff, Plamen Ch Ivanov, Roger G. Mark, Joseph E. Mietus, George B. Moody, Chung-Kang Peng, and H. Eugene Stanley
Cited in the paper.
Quantitative investigation of QRS detection rules using the MIT/BIH arrhythmia database
Patrick S. Hamilton and Willis J. Tompkins
Cited in the paper.
ECG classification using 1-d convolutional deep residual neural network
Fahad Khan, Xiaojun Yu, Zhaohui Yuan, and Atiq ur Rehman
Cited in the paper.
Captum: A unified and generic model interpretability library for PyTorch
Narine Kokhlikyan, Vivek Miglani, Miguel Martin, Edward Wang, Bilal Alsallakh, Jonathan Reynolds, Alexander Melnikov, Natalia Kliushkina, Carlos Araya, Siqi Yan, and Orion Reblitz-Richardson
Cited in the paper.
Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton
Cited in the paper.
NeuroKit2: A python toolbox for neurophysiological signal processing
Dominique Makowski, Tam Pham, Zen J. Lau, Jan C. Brammer, François Lespinasse, Hung Pham, Christopher Schölzel, and S. H. Annabel Chen
Cited in the paper.
XceptionTime: Independent time-window xceptiontime architecture for hand gesture classification
Elahe Rahimian, Soheil Zabihi, Seyed Farokh Atashzar, Amir Asif, and Arash Mohammadi · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…