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Electrocardiogram (ECG) datasets tend to be highly imbalanced due to the scarcity of abnormal cases.
Arrhythmia detection and classification using morphological and dynamic features of ECG signals
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Removal of artifacts from electrocardiogram using digital filter
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Electrocardiogram Classification Using Reservoir Computing With Logistic Regression
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Deep learning approach for active classification of electrocardiogram signals
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PGANs: Personalized generative adversarial networks for ECG synthesis to improve patient-specific deep ECG classification
Golany T, Radinsky K · 2019
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ECG Arrhythmias Detection Using Auxiliary Classifier Generative Adversarial Network and Residual Network
Wang P, Hou B, Shao S, Yan R · 2019
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Synthesis of Realistic ECG using Generative Adversarial Networks. arXiv 2019
Delaney A, Brophy E, Ward T · 2019
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Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network
Zhu F, Ye F, Fu Y, Liu Q, Shen B · 2019
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Wulan N, Wang W, Sun P, Wang K, Xia Y, Zhang H · 2020
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Real-valued (medical) time series generation with recurrent conditional gans
Esteban C, Hyland SL, Rätsch G · 2017
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Creswell A, White T, Dumoulin V, Arulkumaran K, Sengupta B, Bharath A. Generative adversarial networks: An overview. IEEE Signal Process. Mag., 35, 53–65; 2017
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https://github.com/MousaviSajad/ECG-Heartbeat-Classification-seq2seq-model
Mousavi S. ECG Heartbeat Classification Seq2Seq Model; 2019 · 2019
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Hong S, Zhou Y, Shang J, Xiao C, Sun J · 2020
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Synthesis of standard 12-lead electrocardiograms using two dimensional generative adversarial network
Zhang YH, Babaeizadeh S · 2021
Closest in time.
https://github.com/lxdv/ecg-classification/blob/master/README.md
Lyashuk A. ECG Classification; 2021 · 2021
Closest in time.