Publications citing Classification of 12-lead ECGs: The PhysioNet/Computing in Cardiology Challenge 2020 v1.0.2

Reduced-Lead ECG Classifier Model Trained with DivideMix and Model Ensemble
Hiroshi Seki, T. Nakano, Koshiro Ikeda et al. Computing in cardiology, 2021. Cites: paper

Reduced-Lead Electrocardiogram Classification Using Wavelet Analysis and Deep Learning
Adrian K. Cornely, Alondra Carrillo, Grace Mirsky. Computing in cardiology, 2021. Cites: paper

Robust and Task-Aware Training of Deep Residual Networks for Varying-Lead ECG Classification
Hansheng Ren, Miao Xiong, Bryan Hooi. Computing in cardiology, 2021. Cites: paper

Segment, Perceive and Classify - Multitask Learning of the Electrocardiogram in a Single Neural Network
Philipp Sodmann, Marcus Vollmer, Lars Kaderali. Computing in cardiology, 2021. Cites: paper

Semi-Supervised Learning for ECG Classification
Rui André Rodrigues, Paula Couto. Computing in cardiology, 2021. Cites: paper

Spatio-Temporal ECG Network for Detecting Cardiac Disorders from Multi-Lead ECGs
Long Chen, Zheheng Jiang, Tiago Paggi de Almeida et al. Computing in cardiology, 2021. Cites: paper

Swarm Decomposition Enhances the Discrimination of Cardiac Arrhythmias in Varied-Lead ECG Using ResNet-BiLSTM Network Activations
Mohanad Alkhodari, Georgios K. Apostolidis, Charilaos A. Zisou et al. Computing in cardiology, 2021. Cites: paper

Towards Generalization of Cardiac Abnormality Classification Using ECG Signal
Xiaoyu Li, Chen Li, Xian Xu et al. Computing in cardiology, 2021. Cites: paper

Towards High Generalization Performance on Electrocardiogram Classification
Hyeongrok Han, Seongjae Park, Seonwoo Min et al. Computing in cardiology, 2021. Cites: paper

Two Might Do: A Beat-by-Beat Classification of Cardiac Abnormalities Using Deep Learning with Domain-Specific Features
Berken Utku Demirel, Adnan Harun Dogan, Mohammad Abdullah Al Faruque. Computing in cardiology, 2021. Cites: paper

Two Will Do: CNN With Asymmetric Loss, Self-Learning Label Correction, and Hand-Crafted Features for Imbalanced Multi-Label ECG Data Classification
Cristina Gallego Vázquez, Alexander Breuss, Oriella Gnarra et al. Computing in cardiology, 2021. Cites: paper

Using Mel-Frequency Cepstrum and Amplitude-Time Heart Variability as XGBoost Handcrafted Features for Heart Disease Detection
SS Krivenko, Anatolii A. Pulavskyi, Sergii Kryvenko et al. Computing in cardiology, 2021. Cites: paper

Wavelet Transform Based Detection of the First-Degree Atrioventricular Block
Radovan Smíšek, Ivo Viščor, Adam Ivora et al. Computing in cardiology, 2021. Cites: paper

Will Two Do? Varying Dimensions in Electrocardiography: The PhysioNet/Computing in Cardiology Challenge 2021
Matthew A. Reyna, Nadi Sadr, Erick Andres Perez Alday et al. Computing in cardiology, 2021. Cites: paper

VIbCReg: Variance-Invariance-better-Covariance Regularization for Self-Supervised Learning on Time Series
Dae-Soo Lee, Erlend Aune. arXiv (Cornell University), 2021. Cites: paper

Synthesis of standard 12‑lead electrocardiograms using two-dimensional generative adversarial networks
Yuhe Zhang, Saeed Babaeizadeh. Journal of Electrocardiology, 2021. Cites: paper

k-Labelsets Method for Multi-Label ECG Signal Classification Based on SE-ResNet
Jihye Yoo, Yeongbong Jin, Bonggyun Ko et al. Applied Sciences, 2021. Cites: paper

Synthesis of Realistic ECG Waveforms Using a Composite Generative Adversarial Network for Classification of Atrial Fibrillation
Rohan Banerjee, Avik Ghose. European Signal Processing Conference (EUSIPCO), 2021. Cites: paper

A bi-atrial statistical shape model for large-scale in silico studies of human atria: Model development and application to ECG simulations
Claudia Nagel, Steffen Schuler, Olaf Dössel et al. Medical Image Analysis, 2021. Cites: paper

12-Lead ECG Reconstruction via Koopman Operators
Tomer Golany, Kira Radinsky, Daniel Z. Freedman et al. International Conference on Machine Learning, 2021. Cites: paper

Machine learning with electrocardiograms: A call for guidelines and best practices for ‘stress testing’ algorithms
Raymond Bond, Dewar Finlay, Salah S. Al‐Zaiti et al. Journal of Electrocardiology, 2021. Cites: paper

A clinical deep learning framework for continually learning from cardiac signals across diseases, time, modalities, and institutions
Dani Kiyasseh, Tingting Zhu, David A. Clifton. Nature Communications, 2021. Cites: paper

Multi-class Cardiovascular Disease Detection and Classification from 12-Lead ECG Signals Using an Inception Residual Network
Jian Ni, Yingtao Jiang, Shengjie Zhai et al. IEEE Annual International Computer Software and Applications Conference (COMPSAC), 2021. Cites: paper

Detection and classification of arrhythmia using an explainable deep learning model
Yong‐Yeon Jo, Joon‐myoung Kwon, Ki‐Hyun Jeon et al. Journal of Electrocardiology, 2021. Cites: paper

ATD: Augmenting CP Tensor Decomposition by Self Supervision
Chaoqi Yang, Qian, Cheng, Navjot Singh et al. PubMed, 2021. Cites: paper

Identification of 27 abnormalities from multi-lead ECG signals: an ensembled SE_ResNet framework with Sign Loss function
Zhaowei Zhu, Lan Xiang, Tingting Zhao et al. Physiological Measurement, 2021. Cites: paper

Recurrence Plot-Based Approach for Cardiac Arrhythmia Classification Using Inception-ResNet-v2
Hua Zhang, Chengyu Liu, Zhimin Zhang et al. Frontiers in Physiology, 2021. Cites: paper

Multilabel 12-Lead Electrocardiogram Classification Using Beat to Sequence Autoencoders
Alexander William Wong, Amir Salimi, Abram Hindle et al. IEEE International Conference on Acoustics Speech and Signal Processing, 2021. Cites: paper

Certainty in QRS detection with artificial neural networks
Jonas Chromik, Lukas Pirl, Jossekin Beilharz et al. Biomedical Signal Processing and Control, 2021. Cites: paper

A SCALABLE HYBRID MODEL FOR ATRIAL FIBRILLATION DETECTION
Hao Wen, Wenjian Yu, Yuanqing Wu et al. Journal of Mechanics in Medicine and Biology, 2021. Cites: paper

Transfer learning for ECG classification
Kuba Weimann, Tim Conrad. Scientific Reports, 2021. Cites: dataset

Detection of heartbeat abnormalities from phonocardiography using machine learning
Batyrkhan Sultanovich Omarov, Khaled Gamry, Aidar Batyrbekov et al. International Conference on Cloud Computing, Data Science & Engineering (Confluence), 2021. Cites: paper

An Arbitrarily Reconfigurable Extreme Learning Machine Inference Engine for Robust ECG Anomaly Detection
Yu-Chuan Chuang, Yi-Ta Chen, Huai-Ting Li et al. IEEE Open Journal of Circuits and Systems, 2021. Cites: paper

An End-to-End 12-Leading Electrocardiogram Diagnosis System Based on Deformable Convolutional Neural Network With Good Antinoise Ability
Lang Qin, Yuntao Xie, Xinwen Liu et al. IEEE Transactions on Instrumentation and Measurement, 2021. Cites: paper

12-lead ECG Arrythmia Classification Using Convolutional Neural Network for Mutually Non-Exclusive Classes
Mateusz Soliński, Michał Łepek, Antonina Pater et al. Computing in cardiology, 2020. Cites: paper

A Bio-toolkit for Multi-Cardiac Abnormality Diagnosis Using ECG Signal and Deep Learning
Akash Kirodiwal, Apoorva Srivastava, Ashutosh Kumar Dash et al. Computing in cardiology, 2020. Cites: paper

Adaptive lead weighted ResNet trained with different duration signals for classifying 12-lead ECGs
Zhibin Zhao, Hui Fang, Samuel David Relton et al. Computing in cardiology, 2020. Cites: paper

A Deep Learning Solution for Automatized Interpretation of 12-Lead ECGs
Álvaro Huerta Herráiz, Arturo Martínez‐Rodrigo, José Joaquín Rieta et al. Computing in cardiology, 2020. Cites: paper

A Deep Neural Network and Reconstructed Phase Space Approach to Classifying 12-lead ECGs
David Joseph Kaftan, Richard J. Povinelli. Computing in cardiology, 2020. Cites: paper

A Novel Convolutional Neural Network for Arrhythmia Detection From 12-lead Electrocardiograms
Zhengling He, Pengfei Zhang, Lirui Xu et al. Computing in cardiology, 2020. Cites: paper

A Real-time ECG Classification Scheme Using Anti-aliased Blocks with Low Sampling Rate
Yunkai Yu, Zhihong Yang, Zhicheng Yang et al. Computing in cardiology, 2020. Cites: paper

Arrhythmia classification of 12-lead Electrocardiograms by Hybrid Scattering-LSTM networks
Philip A. Warrick, Masun Nabhan Homsi, Vincent Lostanlen et al. Computing in cardiology, 2020. Cites: paper

Arrhythmia Detection and Classification of 12-lead ECGs Using a Deep Neural Network
wenxiao jia, Xiao Dong Xu, Xian Ming Xu et al. Computing in cardiology, 2020. Cites: paper

A Topology Informed Random Forest Classifier for ECG Classification
Paul Samuel Ignacio, Jay-Anne Bulauan, John Rick Manzanares. Computing in cardiology, 2020. Cites: paper

Automated Classification of Electrocardiograms Using Wavelet Analysis and Deep Learning
Andrew Demonbreun, Grace Mirsky. Computing in cardiology, 2020. Cites: paper

Automated Comprehensive Interpretation of 12-lead Electrocardiograms Using Pre-trained Exponentially Dilated Causal Convolutional Neural Networks
Max N. Bos, Rutger R van de Leur, Jeroen F Vranken et al. Computing in cardiology, 2020. Cites: paper

Automatic 12-lead ECG Classification Using a Convolutional Network Ensemble
Antônio H. Ribeiro, Daniel Gedon, Daniel Martins Teixeira et al. Computing in cardiology, 2020. Cites: paper

Automatic 12-lead ECG Classification Using Deep Neural Networks
Wenjie Cai, Shuaicong Hu, Jingying Yang et al. Computing in cardiology, 2020. Cites: paper

Automatic Classification of Arrhythmias by Residual Network and BiGRU With Attention Mechanism
Runnan He, Kuanquan Wang, Na Zhao et al. Computing in cardiology, 2020. Cites: paper

Automatic Concurrent Arrhythmia Classification Using Deep Residual Neural Networks
Deepankar Nankani, Pallabi Saikia, Rashmi Dutta Baruah. Computing in cardiology, 2020. Cites: paper

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