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There has been an increased interest in applying deep neural networks to automatically interpret and analyze the 12-lead electrocardiogram (ECG).
Concept learning and the problem of small disjuncts
Robert C. Holte, Liane Acker, and Bruce W. Porter · 1989
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Fetal ecg extraction from single-channel maternal ecg using singular value decomposition
Partha Pratim Kanjilal, Sarbani Palit, and Goutam Saha · 1997
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Knowledge-based ecg interpretation: a critical review
Mahantapas Kundu, Mita Nasipuri, and Dipak Kumar Basu · 2000
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The impact of the mit-bih arrhythmia database
George B. Moody and Roger G. Mark · 2001
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Smote: Synthetic minority over-sampling technique
N. Chawla, K. Bowyer, Lawrence O. Hall, and W. Philip Kegelmeyer · 2002
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C4.5, class imbalance, and cost sensitivity: Why under-sampling beats over-sampling
Chris Drummond and Robert C. Holte · 2003
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Boosted classification trees and class probability/quantile estimation
David Mease, Abraham J. Wyner, and Andreas Buja · 2007
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Adasyn: Adaptive synthetic sampling approach for imbalanced learning
Haibo He, Yang Bai, Edwardo A. Garcia, and Shutao Li · 2008
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Learning from imbalanced data
Haibo He and Edwardo A. Garcia · 2009
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Optimal transport: old and new , volume 338
Cédric Villani · 2009
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Barycenters in the wasserstein space
Martial Agueh and Guillaume Carlier · 2011
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Sinkhorn distances: Lightspeed computation of optimal transport
Marco Cuturi · 2013
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V. Le · 2014
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2015
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Convolutional neural networks for patient-specific ecg classification
Serkan Kiranyaz, Turker Ince, Ridha Hamila, and M. Gabbouj · 2015
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Adversarial machine learning at scale
Alexey Kurakin, Ian Goodfellow, and Samy Bengio · 2016
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A deep convolutional neural network model to classify heartbeats
U. Rajendra Acharya, Shu Lih Oh, Yuki Hagiwara, Jen Hong Tan, Muhammad Adam, Arkadiusz Gertych, and Ru San Tan · 2017
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Geodesic pca in the wasserstein space by convex pca
Jérémie Bigot, Raúl Gouet, Thierry Klein, and Alfredo López · 2017
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2017
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Robustness of classifiers to universal perturbations: A geometric perspective
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Omar Fawzi, Pascal Frossard, and Stefano Soatto · 2017
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Certifying some distributional robustness with principled adversarial training
Aman Sinha, Hongseok Namkoong, Riccardo Volpi, and John Duchi · 2017
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Ashish Vaswani, Noam M. Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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Robust physical-world attacks on deep learning visual classification
Kevin Eykholt, Ivan Evtimov, Earlence Fernandes, Bo Li, Amir Rahmati, Chaowei Xiao, Atul Prakash, Tadayoshi Kohno, and Dawn Song · 2018
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Speaker recognition from raw waveform with sincnet
Mirco Ravanelli and Yoshua Bengio · 2018
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Generalizing to unseen domains via adversarial data augmentation
Riccardo Volpi, Hongseok Namkoong, Ozan Sener, John C Duchi, Vittorio Murino, and Silvio Savarese · 2018
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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cissé, Yann Dauphin, and David Lopez-Paz · 2018
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Unlabeled data improves adversarial robustness
Yair Carmon, Aditi Raghunathan, Ludwig Schmidt, John C Duchi, and Percy S Liang · 2019
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Certified adversarial robustness via randomized smoothing
Jeremy Cohen, Elan Rosenfeld, and Zico Kolter · 2019
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Deep learning for electromyographic hand gesture signal classification using transfer learning
Ulysse Côté-Allard, Cheikh Latyr Fall, Alexandre Drouin, Alexandre Campeau-Lecours, Clément Gosselin, Kyrre Glette, François Laviolette, and Benoit Gosselin · 2019
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Multimodal emotion recognition using deep canonical correlation analysis
Wei Liu, Jie-Lin Qiu, Wei-Long Zheng, and Bao-Liang Lu · 2019
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Provably robust deep learning via adversarially trained smoothed classifiers
Hadi Salman, Jerry Li, Ilya Razenshteyn, Pengchuan Zhang, Huan Zhang, Sebastien Bubeck, and Greg Yang · 2019
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A novel deep learning based gated recurrent unit with extreme learning machine for electrocardiogram (ecg) signal recognition
S. ClementVirgeniya and E. Ramaraj · 2021
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Improving adversarial robustness via unlabeled out-of-domain data
Zhun Deng, Linjun Zhang, Amirata Ghorbani, and James Zou · 2021
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Minibatch optimal transport distances; analysis and applications
Kilian Fatras, Younes Zine, Szymon Majewski, Rémi Flamary, Rémi Gribonval, and Nicolas Courty · 2021
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Pot: Python optimal transport
Rémi Flamary, Nicolas Courty, Alexandre Gramfort, Mokhtar Z. Alaya, Aurélie Boisbunon, Stanislas Chambon, Adrien Corenflos, Nathalie T. H. Gayraud, Hicham Janati, Ievgen Redko, Antoine Rolet, Antony Schutz, Danica J. Sutherland, Romain Tavenard, Alexander Tong, Titouan Vayer, and Andreas Mueller · 2021
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Artificial intelligence-enabled assessment of the heart rate corrected qt interval using a mobile electrocardiogram device
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Multiple instance learning for ecg risk stratification
Divya Shanmugam, Davis Blalock, and John Guttag · 2019
Cited alongside, same era.
Fusing transformer model with temporal features for ecg heartbeat classification
Genshen Yan, Shen Liang, Yanchun Zhang, and Fan Liu · 2019
Cited alongside, same era.
Adversarially robust generalization just requires more unlabeled data
Runtian Zhai, Tianle Cai, Di He, Chen Dan, Kun He, John Hopcroft, and Liwei Wang · 2019
Cited alongside, same era.
Machine learning-based prediction of acute coronary syndrome using only the pre-hospital 12-lead electrocardiogram
Salah Al‐Zaiti, Lucas Besomi, Zeineb Bouzid, Ziad Faramand, Stephanie O. Frisch, Christian Martin-Gill, Richard E. Gregg, Samir F. Saba, Clifton Callaway, and Ervin Sejdić · 2020
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Ecgadv: Generating adversarial electrocardiogram to misguide arrhythmia classification system
Huangxun Chen, Chenyu Huang, Qianyi Huang, Qian Zhang, and Wei Wang · 2020
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Sharp statistical guaratees for adversarially robust gaussian classification
Chen Dan, Yuting Wei, and Pradeep Ravikumar · 2020
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Fuzz testing based data augmentation to improve robustness of deep neural networks
Xiang Gao, Ripon K. Saha, Mukul R. Prasad, and Abhik Roychoudhury · 2020
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John R. Giudicessi, Matthew Schram, J. Martijn Bos, Conner Galloway, Jacqueline Baras Shreibati, Patrick W. Johnson, Rickey E. Carter, Levi W Disrud, Robert B Kleiman, Zachi I. Attia, Peter A. Noseworthy, Paul A. Friedman, David E. Albert, and Michael J. Ackerman · 2021
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k-mixup regularization for deep learning via optimal transport
Kristjan Greenewald, Anming Gu, Mikhail Yurochkin, Justin Solomon, and Edward Chien · 2021
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Ecg-adv-gan: Detecting ecg adversarial examples with conditional generative adversarial networks
Khondker Fariha Hossain, Sharif Amit Kamran, Alireza Tavakkoli, Lei Pan, Xingjun Ma, Sutharshan Rajasegarar, and Chandan Karmaker · 2021
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Smoothmix: Training confidence-calibrated smoothed classifiers for certified robustness
Jongheon Jeong, Sejun Park, Minkyu Kim, Heung-Chang Lee, Do-Guk Kim, and Jinwoo Shin · 2021
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Electrocardiogram-based deep learning and clinical risk factors to predict atrial fibrillation
Shaan Khurshid, Samuel N. Friedman, Christopher Reeder, Paolo Di Achille, Nathaniel Diamant, Pulkit Singh, Lia X. Harrington, Xin Wang, Mostafa A. Al-Alusi, Gopal Sarma, Andrea S. Foulkes, Patrick T. Ellinor, Christopher D Anderson, Jennifer E. Ho, Anthony A. Philippakis, Puneet Batra, and Steven A. Lubitz · 2021
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Tss: Transformation-specific smoothing for robustness certification
Linyi Li, Maurice Weber, Xiaojun Xu, Luka Rimanic, Bhavya Kailkhura, Tao Xie, Ce Zhang, and Bo Li · 2021
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Multi-label classification of multi-lead ecg based on deep 1d convolutional neural networks with residual and attention mechanism
Yamin Liu, Hanshuang Xie, Qineng Cao, Jiayi Yan, Fan Wu, Huaiyu Zhu, and Yun Pan · 2021
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Real-time frequency-independent single-lead and single-beat myocardial infarction detection
Harold Martin, Ulyana Morar, Walter Izquierdo, Mercedes Cabrerizo, Anastasio Cabrera, and Malek Adjouadi · 2021
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Fundamental tradeoffs in distributionally adversarial training
Mohammad Mehrabi, Adel Javanmard, Ryan A Rossi, Anup Rao, and Tung Mai · 2021
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In-depth benchmarking of deep neural network architectures for ecg diagnosis
Naoki Nonaka and Jun Seita · 2021
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Deep neural networks can predict new-onset atrial fibrillation from the 12-lead ecg and help identify those at risk of atrial fibrillation–related stroke
Sushravya Raghunath, John M. Pfeifer, Alvaro E. Ulloa-Cerna, Arun Nemani, Tanner Carbonati, Linyuan Jing, David P. vanMaanen, Dustin N. Hartzel, Jeffery A. Ruhl, Braxton F. Lagerman, Daniel B. Rocha, Nathan J. Stoudt, Gargi Schneider, Kipp W. Johnson, Noah Zimmerman, Joseph B. Leader, H. Lester Kirchner, Christoph J. Griessenauer, Ashraf Hafez, Christopher W. Good, Brandon K. Fornwalt, and Christopher M. Haggerty · 2021
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Aecg-decompnet: abdominal ecg signal decomposition through deep-learning model
Arash Rasti-Meymandi and Aboozar Ghaffari · 2021
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Ecg signal classification using deep learning techniques based on the ptb-xl dataset
Sandra Śmigiel, Krzysztof Pałczyński, and Damian Ledziński · 2021
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Transformer-based spatial-temporal feature learning for eeg decoding
Yonghao Song, Xueyu Jia, Lie Yang, and Longhan Xie · 2021
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Deep learning for ecg analysis: Benchmarks and insights from ptb-xl
Nils Strodthoff, Patrick Wagner, Tobias Schaeffter, and Wojciech Samek · 2021
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Investigating the robustness of deep learning to electrocardiogram noise
Jenny Venton · 2021
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Robustness of convolutional neural networks to physiological electrocardiogram noise
Jenny Venton, PM Harris, A Sundar, NAS Smith, and PJ Aston · 2021
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Transfer learning for ecg classification
Kuba Weimann and Tim O. F. Conrad · 2021
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Functional optimal transport: map estimation and domain adaptation for functional data
Jiacheng Zhu, Aritra Guha, Dat Do, Mengdi Xu, XuanLong Nguyen, and Ding Zhao · 2021
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Data augmentation for electrocardiograms
Aniruddh Raghu, Divya Shanmugam, Eugene Pomerantsev, John Guttag, and Collin M Stultz · 2022
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Certifying out-of-domain generalization for blackbox functions
Maurice Weber, Linyi Li, Boxin Wang, Zhikuan Zhao, Bo Li, and Ce Zhang · 2022
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Prompting decision transformer for few-shot policy generalization
Mengdi Xu, Yikang Shen, Shun Zhang, Yuchen Lu, Ding Zhao, Joshua Tenenbaum, and Chuang Gan · 2022
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Feature purification: How adversarial training performs robust deep learning
Allen-Zeyuan Zhu and Yuanzhi Li · 2022
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Physiomtl: Personalizing physiological patterns using optimal transport multi-task regression
Jiacheng Zhu, Gregory Darnell, Agni Kumar, Ding Zhao, Bo Li, Xuanlong Nguyen, and Shirley You Ren · 2022
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