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The utilization of deep learning on electrocardiogram (ECG) analysis has brought the advanced accuracy and efficiency of cardiac healthcare diagnostics.
Ambulatory ecg and analysis of heart rate variability in parkinson’s disease
TH Haapaniemi, Ville Pursiainen, JT Korpelainen, HV Huikuri, KA Sotaniemi, and VV Myllylä · 2001
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ECG workout: Exercises in arrhythmia interpretation
Jane Huff · 2006
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Recommendations for the standardization and interpretation of the electrocardiogram: part i: the electrocardiogram and its technology: a scientific statement from the american heart association electrocardiography and arrhythmias committee, council on clinical cardiology; the american college of cardiology foundation; and the heart rhythm society endorsed by the international society for computerized electrocardiology
Paul Kligfield, Leonard S Gettes, James J Bailey, Rory Childers, Barbara J Deal, E William Hancock, Gerard Van Herpen, Jan A Kors, Peter Macfarlane, David M Mirvis, et al · 2007
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Fragmented ecg as a risk marker in cardiovascular diseases
Rahul Jain, Robin Singh, Sundermurthy Yamini, and Mithilesh K Das · 2014
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12-lead ECG: The art of interpretation
Tomas B Garcia · 2015
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Show and tell: A neural image caption generator
Oriol Vinyals, Alexander Toshev, Samy Bengio, and Dumitru Erhan · 2015
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A review of ecg-based diagnosis support systems for obstructive sleep apnea
Oliver Faust, U Rajendra Acharya, EYK Ng, and Hamido Fujita · 2016
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Deep learning for ecg classification
Boris Pyakillya, Natasha Kazachenko, and Nikolay Mikhailovsky · 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
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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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Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
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A deep learning approach for ecg-based heartbeat classification for arrhythmia detection
Giovanna Sannino and Giuseppe De Pietro · 2018
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Classification of myocardial infarction with multi-lead ecg signals and deep cnn
Ulas Baran Baloglu, Muhammed Talo, Ozal Yildirim, Ru San Tan, and U Rajendra Acharya · 2019
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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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Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov · 2019
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Artificial intelligence for the electrocardiogram
Ana Mincholé and Blanca Rodriguez · 2019
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A systematic review of detecting sleep apnea using deep learning
Sheikh Shanawaz Mostafa, Fábio Mendonça, Antonio G. Ravelo-García, and Fernando Morgado-Dias · 2019
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Fusing transformer model with temporal features for ecg heartbeat classification
Genshen Yan, Shen Liang, Yanchun Zhang, and Fan Liu · 2019
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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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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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A wide and deep transformer neural network for 12-lead ecg classification
Annamalai Natarajan, Yale Chang, Sara Mariani, Asif Rahman, Gregory Boverman, Shruti Vij, and Jonathan Rubin · 2020
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Self-supervised ecg representation learning for emotion recognition
Pritam Sarkar and Ali Etemad · 2020
Cited alongside, same era.
Deep learning for ecg analysis: Benchmarks and insights from ptb-xl
Nils Strodthoff, Patrick Wagner, Tobias Schaeffter, and Wojciech Samek · 2020
Cited alongside, same era.
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.
Bertscore: Evaluating text generation with bert
Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q. Weinberger, and Yoav Artzi · 2020
Cited alongside, same era.
Optimal multi-stage arrhythmia classification approach
Jianwei Zheng, Huimin Chu, Daniele Struppa, Jianming Zhang, Sir Magdi Yacoub, Hesham El-Askary, Anthony Chang, Louis Ehwerhemuepha, Islam Abudayyeh, Alexander Barrett, et al · 2020
Cited alongside, same era.
Classification of cardiac abnormalities from ecg signals using se-resnet
Self-supervised representation learning from 12-lead ecg data
Temesgen Mehari and Nils Strodthoff · 2022
Later among the works it cites.
Enhancing dynamic ecg heartbeat classification with lightweight transformer model
Lingxiao Meng, Wenjun Tan, Jiangang Ma, Ruofei Wang, Xiaoxia Yin, and Yanchun Zhang · 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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Ofa: Unifying architectures, tasks, and modalities through a simple sequence-to-sequence learning framework
Peng Wang, An Yang, Rui Men, Junyang Lin, Shuai Bai, Zhikang Li, Jianxin Ma, Chang Zhou, Jingren Zhou, and Hongxia Yang · 2022
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torch_ecg: An ECG Deep Learning Framework Implemented using PyTorch, 2022
Hao Wen and Jingsu Kang · 2022
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Zhaowei Zhu, Han Wang, Tingting Zhao, Yangming Guo, Zhuoyang Xu, Zhuo Liu, Siqi Liu, Xiang Lan, Xingzhi Sun, and Mengling Feng · 2020
Cited alongside, same era.
A transformer architecture for stress detection from ecg
Behnam Behinaein, Anubhav Bhatti, Dirk Rodenburg, Paul Hungler, and Ali Etemad · 2021
Cited alongside, same era.
Time-series representation learning via temporal and contextual contrasting
Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen, Min Wu, Chee Keong Kwoh, Xiaoli Li, and Cuntai Guan · 2021
Cited alongside, same era.
Datasheets for datasets
Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé Iii, and Kate Crawford · 2021
Cited alongside, same era.
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
Cited alongside, same era.
Low-dimensional denoising embedding transformer for ecg classification
Jian Guan, Wenbo Wang, Pengming Feng, Xinxin Wang, and Wenwu Wang · 2021
Cited alongside, same era.
Scaling up visual and vision-language representation learning with noisy text supervision
Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig · 2021
Cited alongside, same era.
Michihiro Yasunaga, Jure Leskovec, and Percy Liang · 2022
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Lit: Zero-shot transfer with locked-image text tuning
Xiaohua Zhai, Xiao Wang, Basil Mustafa, Andreas Steiner, Daniel Keysers, Alexander Kolesnikov, and Lucas Beyer · 2022
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Self-supervised time series representation learning via cross reconstruction transformer
Wenrui Zhang, Ling Yang, Shijia Geng, and Shenda Hong · 2022
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Mimic-iv-ecg-diagnostic electrocardiogram matched subset
Brian Gow, Tom Pollard, Larry A Nathanson, Alistair Johnson, Benjamin Moody, Chrystinne Fernandes, Nathaniel Greenbaum, Seth Berkowitz, Dana Moukheiber, Parastou Eslami, et al · 2023
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Ecg representation learning with multi-modal ehr data
Sravan Kumar Lalam, Hari Krishna Kunderu, Shayan Ghosh, Harish Kumar, Samir Awasthi, Ashim Prasad, Francisco Lopez-Jimenez, Zachi I Attia, Samuel Asirvatham, Paul Friedman, et al · 2023
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Imitate: Clinical prior guided hierarchical vision-language pre-training
Che Liu, Sibo Cheng, Miaojing Shi, Anand Shah, Wenjia Bai, and Rossella Arcucci · 2023
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Ecg biometric recognition: Review, system proposal, and benchmark evaluation
Pietro Melzi, Ruben Tolosana, and Ruben Vera-Rodriguez · 2023
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Langchain: A framework for developing applications powered by language models
Mendable · 2023
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Embedding - openai
OpenAI · 2023
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Convnext v2: Co-designing and scaling convnets with masked autoencoders
Sanghyun Woo, Shoubhik Debnath, Ronghang Hu, Xinlei Chen, Zhuang Liu, In So Kweon, and Saining Xie · 2023
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A multi-view multi-scale neural network for multi-label ecg classification
Shunxiang Yang, Cheng Lian, Zhigang Zeng, Bingrong Xu, Junbin Zang, and Zhidong Zhang · 2023
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Cxr-clip: Toward large scale chest x-ray language-image pre-training
Kihyun You, Jawook Gu, Jiyeon Ham, Beomhee Park, Jiho Kim, Eun K Hong, Woonhyuk Baek, and Byungseok Roh · 2023
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Ecg-sl: Electrocardiogram (ecg) segment learning, a deep learning method for ecg signal
Han Yu, Huiyuan Yang, and Akane Sano · 2023
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Frozen language model helps ecg zero-shot learning
Jun Li, Che Liu, Sibo Cheng, Rossella Arcucci, and Shenda Hong · 2024
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Zero-shot ecg classification with multimodal learning and test-time clinical knowledge enhancement
Che Liu, Zhongwei Wan, Cheng Ouyang, Anand Shah, Wenjia Bai, and Rossella Arcucci · 2024
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Med-unic: Unifying cross-lingual medical vision-language pre-training by diminishing bias
Zhongwei Wan, Che Liu, Mi Zhang, Jie Fu, Benyou Wang, Sibo Cheng, Lei Ma, César Quilodrán-Casas, and Rossella Arcucci · 2024
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