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Electrocardiogram (ECG) is the most crucial monitoring modality to diagnose cardiovascular events.
Speech recognition with deep recurrent neural networks
A. Graves, A.-r. Mohamed, and G. Hinton · 2013
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Neural machine translation by jointly learning to align and translate
D. Bahdanau, K. Cho, and Y. Bengio · 2014
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Dropout: a simple way to prevent neural networks from overfitting
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
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Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2015
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Comparing feature-based classifiers and convolutional neural networks to detect arrhythmia from short segments of ecg
F. Andreotti, O. Carr, M. A. Pimentel, A. Mahdi, and M. De Vos · 2017
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Af classification from a short single lead ecg recording: the physionet/computing in cardiology challenge 2017
G. D. Clifford, C. Liu, B. Moody, H. L. Li-wei, I. Silva, Q. Li, A. Johnson, and R. G. Mark · 2017
Cited alongside, same era.
Identifying normal, af and other abnormal ecg rhythms using a cascaded binary classifier
S. Datta, C. Puri, A. Mukherjee, R. Banerjee, A. D. Choudhury, R. Singh, A. Ukil, S. Bandyopadhyay, A. Pal, and S. Khandelwal · 2017
Cited alongside, same era.
Encase: An ensemble classifier for ecg classification using expert features and deep neural networks
S. Hong, M. Wu, Y. Zhou, Q. Wang, J. Shang, H. Li, and J. Xie · 2017
Cited alongside, same era.
Cardiac rhythm classification from a short single lead ecg recording via random forest
R. Mahajan, R. Kamaleswaran, J. A. Howe, and O. Akbilgic · 2017
Cited alongside, same era.
Arrhythmia classification from the abductive interpretation of short single-lead ecg records
T. Teijeiro, C. A. García, D. Castro, and P. Félix · 2017
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova · 2018
Later among the works it cites.
An artificial intelligence-enabled ecg algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction
Z. I. Attia, P. A. Noseworthy, F. Lopez-Jimenez, S. J. Asirvatham, A. J. Deshmukh, B. J. Gersh, R. E. Carter, X. Yao, A. A. Rabinstein, B. J. Erickson, et al · 2019
Later among the works it cites.
Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network
A. Y. Hannun, P. Rajpurkar, M. Haghpanahi, G. H. Tison, C. Bourn, M. P. Turakhia, and A. Y. Ng · 2019
Later among the works it cites.
Domain knowledge guided deep atrial fibrillation classification and its visual interpretation
X. Li, B. Qian, J. Wei, X. Zhang, S. Chen, Q. Zheng, and n. none · 2019
Later among the works it cites.
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Detection of atrial fibrillation in ecg hand-held devices using a random forest classifier
M. Zabihi, A. B. Rad, A. K. Katsaggelos, S. Kiranyaz, S. Narkilahti, and M. Gabbouj · 2017
Cited alongside, same era.
Autoaugment: Learning augmentation policies from data
E. D. Cubuk, B. Zoph, D. Mane, V. Vasudevan, and Q. V. Le · 2018
Cited alongside, same era.
E. D. Cubuk, B. Zoph, J. Shlens, and Q. V. Le · 2020
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Precision medicine and artificial intelligence: A pilot study on deep learning for hypoglycemic events detection based on ecg
M. Porumb, S. Stranges, A. Pescapè, and L. Pecchia · 2020
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