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

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

Synthesis of standard 12-lead electrocardiograms using two dimensional generative adversarial network
Yuhe Zhang, Saeed Babaeizadeh. arXiv (Cornell University), 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

A Wide and Deep Transformer Neural Network for 12-Lead ECG Classification
Annamalai Natarajan, Yale Chang, Sara Mariani et al. Computing in cardiology, 2020. Cites: paper

Bag of Tricks for Electrocardiogram Classification with Deep Neural Networks
Seonwoo Min, Hyun-Soo Choi, Hyeongrok Han et al. Computing in cardiology, 2020. Cites: paper

Can Laplacian Eigenmaps be used for differentiation between healthy subjects and patients with corrected Tetralogy of Fallot?
Ben Jacobs, Amalia Villa Gómez, Jonathan Moeyersons et al. Computing in cardiology, 2020. Cites: paper

Cardiac Abnormality Detection in 12-lead ECGs with Deep Convolutional Neural Networks Using Data Augmentation
Lucas Weber, Maksym Gaiduk, Wilhelm Daniel Scherz et al. Computing in cardiology, 2020. Cites: paper

Cardiac Arrhythmias Identification by Parallel CNNs and ECG Time-Frequency Representation
Jonathan Roberto Torres Castillo, Karina Ríos, M. Castañeda. Computing in cardiology, 2020. Cites: paper

Cardiac Pathologies Detection and Classification in 12-lead ECG
Radovan Smíšek, Andrea Němcová, Lucie Maršánová et al. Computing in cardiology, 2020. Cites: paper

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