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Electrocardiogram (ECG) diagnosis remains challenging due to limited labeled data and the need to capture subtle yet clinically meaningful variations in rhythm and morphology.
UK Biobank: An Open Access Resource for Identifying the Causes of A Wide Range of Complex Diseases of Middle and Old Age
Cathie Sudlow, John Gallacher, Naomi Allen, Valerie Beral, Paul Burton, John Danesh, Paul Downey, Paul Elliott, Jane Green, Martin Landray, et al · 2015
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MIMIC-III, A Freely Accessible Critical Care Database
Alistair EW Johnson, Tom J Pollard, Lu Shen, Li-wei H Lehman, Mengling Feng, Mohammad Ghassemi, Benjamin Moody, Peter Szolovits, Leo Anthony Celi, and Roger G Mark. 2016 · 2016
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Detecting abnormality in heart dynamics from multifractal analysis of ECG signals
Snehal M Shekatkar, Yamini Kotriwar, KP Harikrishnan, and G Ambika. 2017 · 2017
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Attention is all you need
A Vaswani. 2017 · 2017
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Bert: Pre-training of Deep Bidirectional Transformers for Language Understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
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Incidence and prevalence of cardiovascular disease in English primary care: a cross-sectional and follow-up study of the Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC)
William Hinton, Andrew McGovern, Rachel Coyle, Thang S Han, Pankaj Sharma, Ana Correa, Filipa Ferreira, and Simon de Lusignan. 2018 · 2018
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An Open Access Database for Evaluating the Algorithms of Electrocardiogram Rhythm and Morphology Abnormality Detection
Feifei Liu, Chengyu Liu, Lina Zhao, Xiangyu Zhang, Xiaoling Wu, Xiaoyan Xu, Yulin Liu, Caiyun Ma, Shoushui Wei, Zhiqiang He, et al · 2018
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Unsupervised Scalable Representation Learning for Multivariate Time Series
Jean-Yves Franceschi, Aymeric Dieuleveut, and Martin Jaggi. 2019 · 2019
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Wav2Vec 2.0: A Framework for Self-Supervised Learning of Speech Representations
Alexei Baevski, Yuhao Zhou, Abdelrahman Mohamed, and Michael Auli. 2020 · 2020
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A Simple Framework for Contrastive Learning of Visual Representations. In Proceedings of the International Conference on Machine Learning
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020 · 2020
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An Image is Worth 16x16 Words: Transformers for Image Recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
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CloCS: Contrastive Learning of Cardiac Signals
Dani Kiyasseh, Tingting Zhu, and David A Clifton. 2020 · 2020
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Self-Supervised ECG Representation Learning for Emotion Recognition
Pritam Sarkar and A. Etemad. 2020a · 2020
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PTB-XL, a large publicly available electrocardiography dataset
Patrick Wagner, Nils Strodthoff, Ralf-Dieter Bousseljot, Dieter Kreiseler, Fatima I Lunze, Wojciech Samek, and Tobias Schaeffter. 2020 · 2020
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A 12-lead electrocardiogram database for arrhythmia research covering more than 10,000 patients
Jianwei Zheng, Jianming Zhang, Sidy Danioko, Hai Yao, Hangyuan Guo, and Cyril Rakovski. 2020 · 2020
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Online False Discovery Rate Control for Anomaly Detection in Time Series
Quentin Rebjock, Baris Kurt, Tim Januschowski, and Laurent Callot. 2021 · 2021
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CODE-15%: A Large Scale Annotated Dataset of 12-lead ECGs
Antônio H. Ribeiro, Gabriela M.M. Paixao, Emilly M. Lima, Manoel Horta Ribeiro, Marcelo M. Pinto Filho, Paulo R. Gomes, Derick M. Oliveira, Wagner Meira Jr, Thömas B Schon, and Antonio Luiz P. Ribeiro. 2021a · 2021
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SaMI-Trop: 12-lead ECG Traces with Age and Mortality Annotations
Antonio Luiz P. Ribeiro, Antônio H. Ribeiro, Gabriela M.M. Paixao, Emilly M. Lima, Manoel Horta Ribeiro, Marcelo M. Pinto Filho, Paulo R. Gomes, Derick M. Oliveira, Wagner Meira Jr, Thömas B Schon, and Ester C Sabino. 2021b · 2021
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Adjusting for Autocorrelated Errors in Neural Networks for Time Series
Fan-Keng Sun, Chris Lang, and Duane Boning. 2021 · 2021
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Neighborhood Contrastive Learning Applied to Online Patient Monitoring. In Proceedings of the International Conference on Machine Learning
Hugo Yèche, Gideon Dresdner, Francesco Locatello, Matthias Hüser, and Gunnar Rätsch. 2021 · 2021
Evaluating Self-Supervised Learning via Risk Decomposition
Yann Dubois, Tatsunori Hashimoto, and Percy Liang. 2023 · 2023
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Aditya Grover et al · 2023
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Contrastive Masked Autoencoders are Stronger Vision Learners
Zhicheng Huang, Xiaojie Jin, Chengze Lu, Qibin Hou, Ming-Ming Cheng, Dongmei Fu, Xiaohui Shen, and Jiashi Feng. 2023 · 2023
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Practical Intelligent Diagnostic Algorithm for Wearable 12-lead ECG via Self-Supervised Learning on Large-scale Dataset
Jiewei Lai, Huixin Tan, Jinliang Wang, Lei Ji, Jun Guo, Baoshi Han, Yajun Shi, Qianjin Feng, and Wei Yang. 2023 · 2023
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Towards Enhancing Time Series Contrastive Learning: A Dynamic Bad Pair Mining Approach. In Proceedings of the Twelfth International Conference on Learning Representations
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Masked Autoencoders are Scalable Vision Learners. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick. 2022 · 2022
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Efficient data augmentation policy for electrocardiograms. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management . 4153–4157
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Utilizing Expert Features for Contrastive Learning of Time-Series Representations. In Proceedings of the International Conference on Machine Learning
Manuel T Nonnenmacher, Lukas Oldenburg, Ingo Steinwart, and David Reeb. 2022 · 2022
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Cross-modal autoencoder framework learns holistic representations of cardiovascular state
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Real-time patient-specific ECG classification by 1-D convolutional neural networks
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A lightweight 1D convolutional neural network model for arrhythmia diagnosis from electrocardiogram signal
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