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

Spectral F-wave index for automatic identification of atrial fibrillation in very short electrocardiograms
Agnese Sbrollini, Ilaria Marcantoni, Micaela Morettini et al. Biomedical Signal Processing and Control, 2021. Cites: paper

Classification of Bundle Branch Blocks with QRS Templates Extracted from 12-lead ECGs
Hannes Welle, Claudia Nagel, Axel Loewe et al. Current Directions in Biomedical Engineering, 2021. Cites: paper

Potential of Rule-Based Methods and Deep Learning Architectures for ECG Diagnostics
Giovanni Bortolan, Ivaylo Ivanov Christov, Iana Ivaylova Simova. Diagnostics, 2021. Cites: paper

3-D ECG images with Deep Learning Approach for Identification of Cardiac Abnormalities from a Variable Number of Leads
Giovanni Bortolan. Computing in cardiology, 2021. Cites: paper

A Branched Deep Neural Network for End-to-end Classification from ECGs with Varying Dimensions
Han Ming Duan, Junchao Fan, Bin Xiao et al. Computing in cardiology, 2021. Cites: paper

A Mixed-Domain Self-Attention Network for Multilabel Cardiac Irregularity Classification Using Reduced-Lead Electrocardiogram
Hao-Chun Yang, Wan‐Ting Hsieh, Trista Pei-Chun Chen. Computing in cardiology, 2021. Cites: paper

An Ensemble Learning Approach to Detect Cardiac Abnormalities in ECG Data Irrespective of Lead Availability
Tim Uhlemann, Joshua Prim, Nils Gumpfer et al. Computing in cardiology, 2021. Cites: paper

An InceptionTime-Inspired Convolutional Neural Network to Detect Cardiac Abnormalities in Reduced-Lead ECG Data
H. Crocker, Aaron W. Costall. Computing in cardiology, 2021. Cites: paper

A Novel Multi-Scale Convolutional Neural Network for Arrhythmia Classification on Reduced-Lead ECGs
Pan Xia, Zhengling He, Yusi Zhu et al. Computing in cardiology, 2021. Cites: paper

Arrhythmia Classification of Reduced-Lead Electrocardiograms by Scattering-Recurrent Networks
Philip A. Warrick, Vincent Lostanlen, Michael Eickenberg et al. Computing in cardiology, 2021. Cites: paper

A Two-Phase Multilabel ECG Classification Using One-Dimensional Convolutional Neural Network and Modified Labels
Ľubomír Antoni, Erik Bruoth, Peter Bugata et al. Computing in cardiology, 2021. Cites: paper

Automated Diagnosis of Reduced-Lead Electrocardiograms Using a Shared Classifier
HT Jessen, Rutger R van de Leur, Pieter A. Doevendans et al. Computing in cardiology, 2021. Cites: paper

Automatic Classification of Full- and Reduced-Lead Electrocardiograms Using Morphological Feature Extraction
Alexander Hammer, Matthieu Scherpf, Hannes Ernst et al. Computing in cardiology, 2021. Cites: paper

Automatic Diagnosis of Cardiac Disease from Twelve-Lead and Reduced-Lead ECGs Using Multilabel Classification
Prathic Sundararajan, Kevin Moses, Cristhian Potes et al. Computing in cardiology, 2021. Cites: paper

Cardiac Abnormalities Detection Through 12-Lead/Reduced-Lead ECG Spectrograms and 2D-CRNN
Jonathan Roberto Torres Castillo, M. Castañeda. Computing in cardiology, 2021. Cites: paper

Cardiac Abnormalities Recognition in ECG Using a Convolutional Network with Attention and Input with an Adaptable Number of Leads
Tomáš Vičar, Petra Novotná, Jakub Hejč et al. Computing in cardiology, 2021. Cites: paper

Cardiac Arrhythmias Classification in Kardiovize Population Study
Martin Pešl, Jakub Hejč, Tomáš Kulík et al. Computing in cardiology, 2021. Cites: paper

Channel Self-Attention Deep Learning Framework for Multi-Cardiac Abnormality Diagnosis from Varied-Lead ECG Signals
Apoorva Srivastava, Ajith Hari, Sawon Pratiher et al. Computing in cardiology, 2021. Cites: paper

Classification of ECG Signals with Different Lead Systems Using AutoML
Matteo Bodini, Massimo Walter Rivolta, Roberto Sassi. Computing in cardiology, 2021. Cites: paper

Classifying Different Dimensional ECGs Using Deep Residual Convolutional Neural Networks
Wenjie Cai, Fanli Liu, Xuan Wang et al. Computing in cardiology, 2021. Cites: paper

Combining ResNet Model with Handcrafted Temporal Features for ECG Classification with Varying Number of Leads
Stefano Magni, Andrea Sansonetti, Chiara Salvi et al. Computing in cardiology, 2021. Cites: paper

Convolution-Free Waveform Transformers for Multi-Lead ECG Classification
Annamalai Natarajan, Gregory Boverman, Yale Chang et al. Computing in cardiology, 2021. Cites: paper

Deep Discriminative Domain Generalization with Adversarial Feature Learning for Classifying ECG Signals
Zuogang Shang, Zhibin Zhao, Hui Fang et al. Computing in cardiology, 2021. Cites: paper

Detecting Cardiac Abnormalities with Multi-Lead ECG Signals: A Modular Network Approach
Ryan Clark, Mohammadreza Heydarian, Kashif Siddiqui et al. Computing in cardiology, 2021. Cites: paper

Diagnosis of Cardiac Abnormalities Applying Scattering Transform and Fourier-Bessel Expansion on ECG Signals
Nidhi Kalidas Sawant, Shivnarayan Patidar. Computing in cardiology, 2021. Cites: paper

ECG Classification Combining Conventional Signal Analysis, Random Forests and Neural Networks - a Stacked Learning Scheme
Martin Baumgartner, Martin Kropf, Lukas Haider et al. Computing in cardiology, 2021. Cites: paper

Ensemble Learning of Modified Residual Networks for Classifying ECG with Different Set of Leads
Federico Muscato, Valentina Corino, Luca Tommaso Mainardi. Computing in cardiology, 2021. Cites: paper

Generative Pre-Trained Transformer for Cardiac Abnormality Detection
Pierre Louis Gaudilliere, Halla Sigurthorsdottir, Clémentine Aguet et al. Computing in cardiology, 2021. Cites: paper

Hybrid Arrhythmia Detection on Varying-Dimensional Electrocardiography: Combining Deep Neural Networks and Clinical Rules
Hao Wen, Jingsu Kang. Computing in cardiology, 2021. Cites: paper

Incorporating Demographic and Heartbeat Features with Multichannel ECG for Cardiac Abnormality Detection using Parallel CNN and GAP Network
Deepankar Nankani, Rashmi Dutta Baruah. Computing in cardiology, 2021. Cites: paper

Investigating the Robustness of Deep Learning to Electrocardiogram Noise
Jenny Venton, Philip J. Aston. Computing in cardiology, 2021. Cites: paper

Learning ECG Representations for Multi-Label Classification of Cardiac Abnormalities
Jangwon Suh, Jimyeong Kim, Eunjung Lee et al. Computing in cardiology, 2021. Cites: paper

Leveraging Period-Specific Variations in ECG Topology for Classification Tasks
Paul Samuel Ignacio. Computing in cardiology, 2021. Cites: paper

MTFNet: A Morphological and Temporal Features Network for Multiple Leads ECG Classification
Lebing Pan, Weibai Pan, Mengxue Li et al. Computing in cardiology, 2021. Cites: paper

Multi-Label Cardiac Abnormalities Classification on Selected Leads of ECG Signals
Zhuoyang Xu, Yangming Guo, Tingting Zhao et al. Computing in cardiology, 2021. Cites: paper

Multi-Label Cardiac Abnormality Classification from Electrocardiogram Using Deep Convolutional Neural Networks
Nima L. Wickramasinghe, Mohamed Athif. Computing in cardiology, 2021. Cites: paper

Multi-label Classification of Cardiac Abnormalities for Multi-lead ECG Recordings Based on Auto-encoder Features and a Neural Network Classifier
Onno Linschmann, Maurice Rohr, Steffen K. Leonhardt et al. Computing in cardiology, 2021. Cites: paper

Multi-Label Classification of Multi-lead ECG Based on Deep 1D Convolutional Neural Networks With Residual and Attention Mechanism
Yamin Liu, Hanshuang Xie, Qineng Cao et al. Computing in cardiology, 2021. Cites: paper

Multi-Label Classification on 12, 6, 4, 3 and 2 Lead Electrocardiography Signals Using Convolutional Recurrent Neural Networks
Niels Osnabrugge, Kata Keresztesi, Felix Rustemeyer et al. Computing in cardiology, 2021. Cites: paper

Multi-Label ECG Classification Using Convolutional Neural Networks in a Classifier Chain
Bjørn-Jostein Singstad, Eraraya Morenzo Muten, Pal Haugar Brekke. Computing in cardiology, 2021. Cites: paper

Multiple Cardiac Disease Detection from Minimal-Lead ECG Combining Feedforward Neural Networks with a One-vs-Rest Approach
Santiago Jiménez-Serrano, Miguel Rodrigo, Conrado Javier Calvo et al. Computing in cardiology, 2021. Cites: paper

N-BEATS for Heart Dysfunction Classification
Bartosz Puszkarski, Krzysztof Hryniów, Grzegorz Sarwas. Computing in cardiology, 2021. Cites: paper

Pathologies Prediction on Short ECG Signals with Focus on Feature Extraction Based on Beat Morphology and Image Deformation
Jeffrey van Prehn, Svetoslav Ivanov, Georgi I. Nalbantov. Computing in cardiology, 2021. Cites: paper

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

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