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

Domain-principled Inference with ResNet-Transformer Model for 12-lead ECG Classification
Sai Chander Racha, Trisrota Deb, Ishan Sahu et al. International Joint Conference on Neural Networks (IJCNN), 2022. Cites: paper

Issues in the automated classification of multilead ecgs using heterogeneous labels and populations
Matthew A. Reyna, Nadi Sadr, Erick Andres Perez Alday et al. Physiological Measurement, 2022. Cites: paper

Multiclass Convolutional Neural Networks for Atrial Fibrillation Classification
Agnese Sbrollini, Selene Tomassini, Enrico Emaldi et al. Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2022. Cites: paper

Rethinking Algorithm Performance Metrics for Artificial Intelligence in Diagnostic Medicine
Matthew A. Reyna, Elaine Okanyene Nsoesie, Gari D. Clifford. JAMA, 2022. Cites: paper

State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review
Γεώργιος Πετμεζάς, Leandros Stefanopoulos, Vassilis Kilintzis et al. JMIR Medical Informatics, 2022. Cites: paper

Classification of multi-lead ECG with deep residual convolutional neural networks
Wenjie Cai, Fanli Liu, Bolin Xu et al. Physiological Measurement, 2022. Cites: paper

Arrhythmia classification of 12-lead and reduced-lead electrocardiograms via recurrent networks, scattering, and phase harmonic correlation
Philip A. Warrick, Vincent Lostanlen, Michael Eickenberg et al. Physiological Measurement, 2022. Cites: paper

A large-scale multi-label 12-lead electrocardiogram database with standardized diagnostic statements
Hui Liu, Dan Chen, Da Chen et al. Scientific Data, 2022. Cites: paper

Brief Overview of Neural Networks for Medical Applications
Máté Hireš, Peter Bugata, Matej Gazda et al. Acta Electrotechnica et Informatica, 2022. Cites: paper

Reliable Detection of Myocardial Ischemia Using Machine Learning Based on Temporal-Spatial Characteristics of Electrocardiogram and Vectorcardiogram
Xiaoye Zhao, Jucheng Zhang, Yinglan Gong et al. Frontiers in Physiology, 2022. Cites: paper

Multi-label classification of reduced-lead ECGs using an interpretable deep convolutional neural network
Nima L. Wickramasinghe, Mohamed Athif. Physiological Measurement, 2022. Cites: paper

A Lightweight and Interpretable Model to Classify Bundle Branch Blocks from ECG Signals
Hugues Turbé, Mina Bjelogrlic, Mehdi Namdar et al. Studies in health technology and informatics, 2022. Cites: paper

From 12 to 1 ECG lead: multiple cardiac condition detection mixing a hybrid machine learning approach with a one-versus-rest classification strategy
Santiago Jiménez-Serrano, Miguel Rodrigo, Conrado Javier Calvo et al. Physiological Measurement, 2022. Cites: paper

Abnormality classification from electrocardiograms with various lead combinations
Zhuoyang Xu, Yangming Guo, Tingting Zhao et al. Physiological Measurement, 2022. Cites: paper

Detection of bradycardia from electrocardiogram signals using feature extraction and snapshot ensembling
Subhadeep Sengupta, Veena Mayya, S. Sowmya Kamath. International Journal of Information Technology, 2022. Cites: paper

A deep residual inception network with channel attention modules for multi-label cardiac abnormality detection from reduced-lead ECG
Apoorva Srivastava, Sawon Pratiher, Sazedul Alam et al. Physiological Measurement, 2022. Cites: paper

Comparison of neural basis expansion analysis for interpretable time series (N-BEATS) and recurrent neural networks for heart dysfunction classification
Bartosz Puszkarski, Krzysztof Hryniów, Grzegorz Sarwas. Physiological Measurement, 2022. Cites: paper

An Intelligent ECG-Based Tool for Diagnosing COVID-19 via Ensemble Deep Learning Techniques
Omneya Attallah. Biosensors, 2022. Cites: paper

A multi-variate heart disease optimization and recognition framework
Hossam Magdy Balaha, Ahmed Osama Shaban, Eman M. El-Gendy et al. Neural Computing and Applications, 2022. Cites: paper

Federated Learning for Electronic Health Records
Trung Kien Dang, Lan Xiang, Jianshu Weng et al. ACM Transactions on Intelligent Systems and Technology, 2022. Cites: paper

A study on several critical problems on arrhythmia detection using varying-dimensional electrocardiography
Jingsu Kang, Hao Wen. Physiological Measurement, 2022. Cites: paper

Golden Standard or Obsolete Method? Review of ECG Applications in Clinical and Experimental Context
Tibor Stračina, Marina Ronzhina, Richard Ředina et al. Frontiers in Physiology, 2022. Cites: paper

Automatic ECG classification and label quality in training data
Ľubomír Antoni, Erik Bruoth, Peter Bugata et al. Physiological Measurement, 2022. Cites: paper

Compelling new electrocardiographic markers for automatic diagnosis
Cristina Rueda, Itziar Fernández, Yolanda Larriba et al. Computer Methods and Programs in Biomedicine, 2022. Cites: paper

Predict alone, decide together: cardiac abnormality detection based on single lead classifier voting
Pierre Gabriel Aublin, Mouïn Ben Ammar, Jérémy Fix et al. Physiological Measurement, 2022. Cites: paper

Classification of ECG using ensemble of residual CNNs with or without attention mechanism
Petr Nejedlý, Adam Ivora, Ivo Viščor et al. Physiological Measurement, 2022. Cites: paper

State-of-the-art Deep Learning Methods on Electrocardiogram Data: A Systematic Review (Preprint)
Γεώργιος Πετμεζάς, Leandros Stefanopoulos, Vassiis Kilintzis et al. 2022. Cites: paper

Personalized Neural Network for Patient-Specific Health Monitoring in IoT: A Metalearning Approach
Zhenge Jia, Yiyu Shi, Jingtong Hu. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2022. Cites: paper

Generalizable Beat-by-Beat Arrhythmia Detection by Using Weakly Supervised Deep Learning
Yang Liu, Qince Li, Runnan He et al. Frontiers in Physiology, 2022. Cites: paper

Analysis of an adaptive lead weighted ResNet for multiclass classification of 12-lead ECGs
Zhen Zhao, Darcy Murphy, Hugh Gifford et al. Physiological Measurement, 2022. Cites: paper

Practical Lessons on 12-Lead ECG Classification: Meta-Analysis of Methods From PhysioNet/Computing in Cardiology Challenge 2020
Shenda Hong, Wenrui Zhang, Chenxi Sun et al. Frontiers in Physiology, 2022. Cites: paper

Abnormal heart rhythms
Anirban Dutta Choudhury, Rohan Banerjee, Sanjay Kimbahune et al. Elsevier eBooks, 2022. Cites: paper

Assessing the Reidentification Risks Posed by Deep Learning Algorithms Applied to ECG Data
Arin Ghazarian, Jianwei Zheng, Daniele Carlo Struppa et al. IEEE Access, 2022. Cites: paper

Cordelia: An Application for Automatic ECG Diagnostics
Ľubomír Antoni, Erik Bruoth, Peter Bugata et al. Lecture notes in computer science, 2022. Cites: paper

Development and Optimization of an Electrode Exchange Detection Algorithm Using Error Estimators (MSE and PRD)
Edward Carello Figueroa Tejada, Elvis Supo, Carlos Esteban Mamani Huisa et al. SSRN Electronic Journal, 2022. Cites: paper

MLCM: Multi-Label Confusion Matrix
Mohammadreza Heydarian, Thomas E. Doyle, Reza Samavi. IEEE Access, 2022. Cites: paper

Multilabel 12-Lead ECG Classification Based on Leadwise Grouping Multibranch Network
Xiaoyun Xie, Hui Liu, Da Chen et al. IEEE Transactions on Instrumentation and Measurement, 2022. Cites: paper

Norwegian Endurance Athlete ECG Database
Bjørn-Jostein Singstad. IEEE Open Journal of Engineering in Medicine and Biology, 2022. Cites: paper

Self-supervised representation learning from 12-lead ECG data
Temesgen Mehari, Nils Strodthoff. Computers in Biology and Medicine, 2021. Cites: paper

Multi-source unsupervised domain adaptation for ECG classification
Fucheng Deng, Shikui Tu, Lei Xu. IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2021. Cites: paper

BaT: Beat-aligned Transformer for Electrocardiogram Classification
Xiaoyu Li, Chen Li, Yuhua Wei et al. Proceedings, 2021. Cites: paper

Data Augmentation for 12-Lead ECG Beat Classification
Edmund Do, Jack Boynton, Byung Suk Lee et al. SN Computer Science, 2021. Cites: paper

Automatic Multi-Label ECG Classification with Category Imbalance and Cost-Sensitive Thresholding
Yang Liu, Qince Li, Kuanquan Wang et al. Biosensors, 2021. Cites: paper

Body Surface Potential Mapping: Contemporary Applications and Future Perspectives
Jake Bergquist, Lindsay Rupp, Brian Zenger et al. Hearts, 2021. Cites: paper

Segment Origin Prediction: A Self-supervised Learning Method for Electrocardiogram Arrhythmia Classification
Chuankai Luo, Guijin Wang, Zijian Ding et al. Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2021. Cites: paper

Opening the black box: interpretability of machine learning algorithms in electrocardiography
Matteo Bodini, Massimo Walter Rivolta, Roberto Sassi. Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences, 2021. Cites: paper

Optimal ECG-lead selection increases generalizability of deep learning on ECG abnormality classification
Changxin Lai, Shijie Zhou, Natalia A. Trayanova. Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences, 2021. Cites: paper

Robustness of convolutional neural networks to physiological electrocardiogram noise
Jenny Venton, P M Harris, A. Sundar et al. Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences, 2021. Cites: paper

Convolutional neural network for classification of eight types of arrhythmia using 2D time–frequency feature map from standard 12-lead electrocardiogram
Da Un Jeong, Ki Moo Lim. Scientific Reports, 2021. Cites: paper

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