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Electrocardiograms (ECG) are widely employed as a diagnostic tool for monitoring electrical signals originating from a heart.
The infinite gaussian mixture model
Carl Rasmussen · 1999
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Laurens Van der Maaten and Geoffrey Hinton · 2008
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A public domain dataset for human activity recognition using smartphones
Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra, Jorge Luis Reyes-Ortiz, et al · 2013
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Af classification from a short single lead ecg recording: The physionet/computing in cardiology challenge 2017
Gari D Clifford, Chengyu Liu, Benjamin Moody, H Lehman Li-wei, Ikaro Silva, Qiao Li, AE Johnson, and Roger G Mark · 2017
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Ecg arrhythmia classification using a 2-d convolutional neural network
Tae Joon Jun, Hoang Minh Nguyen, Daeyoun Kang, Dohyeun Kim, Daeyoung Kim, and Young-Hak Kim · 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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A novel application of deep learning for single-lead ecg classification
Sherin M Mathews, Chandra Kambhamettu, and Kenneth E Barner · 2018
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Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
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Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network
Awni Y Hannun, Pranav Rajpurkar, Masoumeh Haghpanahi, Geoffrey H Tison, Codie Bourn, Mintu P Turakhia, and Andrew Y Ng · 2019
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova · 2019
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 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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Accuracy and knowledge in 12-lead ecg placement among nursing students and nurses: A web-based italian study
Noemi Giannetta, Giuseppe Campagna, Flavio Di Muzio, Emanuele Di Simone, Sara Dionisi, and Marco Di Muzio · 2020
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Bootstrap your own latent-a new approach to self-supervised learning
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, et al · 2020
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Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
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Self-supervised visual feature learning with deep neural networks: A survey
Longlong Jing and Yingli Tian · 2020
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Toward improving ecg biometric identification using cascaded convolutional neural networks
Yazhao Li, Yanwei Pang, Kongqiao Wang, and Xuelong Li · 2020
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Data augmentation for electrocardiogram classification with deep neural network
Naoki Nonaka and Jun Seita · 2020
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Automatic diagnosis of the 12-lead ecg using a deep neural network
Antônio H Ribeiro, Manoel Horta Ribeiro, Gabriela MM Paixão, Derick M Oliveira, Paulo R Gomes, Jéssica A Canazart, Milton PS Ferreira, Carl R Andersson, Peter W Macfarlane, Wagner Meira Jr, et al · 2020
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Self-supervised ecg representation learning for emotion recognition
Pritam Sarkar and Ali Etemad · 2020
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Deep learning for ecg analysis: Benchmarks and insights from ptb-xl
Nils Strodthoff, Patrick Wagner, Tobias Schaeffter, and Wojciech Samek · 2020
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Intra-inter subject self-supervised learning for multivariate cardiac signals
Xiang Lan, Dianwen Ng, Shenda Hong, and Mengling Feng · 2022
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Using the apple watch to record multiple-lead electrocardiograms in detecting myocardial infarction: Where are we now?
Ke Li, Abdelmotagaly Elgalad, Cristiano Cardoso, and Emerson C Perin · 2022
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Self-supervised representation learning from 12-lead ecg data
Temesgen Mehari and Nils Strodthoff · 2022
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Lead-agnostic self-supervised learning for local and global representations of electrocardiogram
Jungwoo Oh, Hyunseung Chung, Joon-myoung Kwon, Dong-gyun Hong, and Edward Choi · 2022
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What do self-supervised vision transformers learn?
Namuk Park, Wonjae Kim, Byeongho Heo, Taekyung Kim, and Sangdoo Yun · 2022
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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
Cited alongside, same era.
Emerging properties in self-supervised vision transformers
Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin · 2021
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An empirical study of training self-supervised vision transformers. in 2021 ieee
X Chen, S Xie, and K He · 2021
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3kg: Contrastive learning of 12-lead electrocardiograms using physiologically-inspired augmentations
Bryan Gopal, Ryan Han, Gautham Raghupathi, Andrew Ng, Geoff Tison, and Pranav Rajpurkar · 2021
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Automated detection of acute myocardial infarction using asynchronous electrocardiogram signals—preview of implementing artificial intelligence with multichannel electrocardiographs obtained from smartwatches: retrospective study
Changho Han, Youngjae Song, Hong-Seok Lim, Yunwon Tae, Jong-Hwan Jang, Byeong Tak Lee, Yeha Lee, Woong Bae, and Dukyong Yoon · 2021
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Clocs: Contrastive learning of cardiac signals across space, time, and patients
Dani Kiyasseh, Tingting Zhu, and David A Clifton · 2021
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Will two do? varying dimensions in electrocardiography: the physionet/computing in cardiology challenge 2021
Matthew A Reyna, Nadi Sadr, Erick A Perez Alday, Annie Gu, Amit J Shah, Chad Robichaux, Ali Bahrami Rad, Andoni Elola, Salman Seyedi, Sardar Ansari, et al · 2021
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Masked autoencoder-based self-supervised learning for electrocardiograms to detect left ventricular systolic dysfunction
Shinnosuke Sawano, Satoshi Kodera, Hirotoshi Takeuchi, Issei Sukeda, Susumu Katsushika, and Issei Komuro · 2022
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Analysis of augmentations for contrastive ecg representation learning
Sahar Soltanieh, Ali Etemad, and Javad Hashemi · 2022
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Deep learning for automatic detection of periodic limb movement disorder based on electrocardiogram signals
Erdenebayar Urtnasan, Jong-Uk Park, Jung-Hun Lee, Sang-Baek Koh, and Kyoung-Joung Lee · 2022
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Contrastive heartbeats: Contrastive learning for self-supervised ecg representation and phenotyping
Crystal T Wei, Ming-En Hsieh, Chien-Liang Liu, and Vincent S Tseng · 2022
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Simmim: A simple framework for masked image modeling
Zhenda Xie, Zheng Zhang, Yue Cao, Yutong Lin, Jianmin Bao, Zhuliang Yao, Qi Dai, and Han Hu · 2022
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Maefe: Masked autoencoders family of electrocardiogram for self-supervised pretraining and transfer learning
Huaicheng Zhang, Wenhan Liu, Jiguang Shi, Sheng Chang, Hao Wang, Jin He, and Qijun Huang · 2022
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Spatiotemporal self-supervised representation learning from multi-lead ecg signals
Rui Hu, Jie Chen, and Li Zhou · 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
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Towards better time series contrastive learning: A dynamic bad pair mining approach
Xiang Lan, Hanshu Yan, Shenda Hong, and Mengling Feng · 2023
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scl-st: Supervised contrastive learning with semantic transformations for multiple lead ecg arrhythmia classification
Duc Le, Sang Truong, Patel Brijesh, Donald Adjeroh, and Ngan Le · 2023
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Yuzhen Qin, Li Sun, Hui Chen, Wei-qiang Zhang, Wenming Yang, Jintao Fei, and Guijin Wang · 2023
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