Publications from Classification of 12-lead ECGs: The PhysioNet/Computing in Cardiology Challenge 2020

The following paper describes the PhysioNet/Computing in Cardiology Challenge. Please cite this publication when referencing the Challenge.

The following papers were presented at the Computing in Cardiology Conference.

Classification of 12-lead ECGs: the PhysioNet/Computing in Cardiology Challenge 2020
Matthew Reyna¹, Erick Andres Perez Alday¹, Annie Gu¹, Chengyu Liu², Salman Seyedi¹, Ali Bahrami Rad¹, Andoni Elola³, Qiao Li¹, Ashish Sharma¹, Gari Clifford^4
1Emory University, ²Southeast University, ³University of the Basque Country, ⁴Emory University and Georgia Institute of Technology
Multi-Class Classification of Pathologies Found on Short ECG Signals
Georgi Nalbantov, Svetoslav Ivanov, Jeffrey van Prehn
Data Science Consulting Ltd.
Automated Comprehensive Interpretation of 12-lead Electrocardiograms Using Pre-trained Exponentially Dilated Causal Convolutional Neural Networks
Max Bos¹, Rutger van de Leur², Jeroen Vranken¹, Deepak Gupta¹, Pim van der Harst², Pieter Doevendans², René van Es^2
1Informatics Institute, University of Amsterdam, ²UMC Utrecht
Multilabel 12-Lead Electrocardiogram Classification Using Gradient Boosting Tree Ensemble
Alexander William Wong¹, Weijie Sun², Sunil Vasu Kalmady², Padma Kaul¹, Abram Hindle^1
1University of Alberta, ²Canadian VIGOUR Centre
Combining Scatter Transform and Deep Neural Networks for Multilabel ECG Signal Classification
Maximilian Oppelt¹, Maximilian Riehl¹, Felix Kemeth², Jan Steffan^1
1Department of Image Processing and Medical Engineering, Fraunhofer Institute for Integrated Circuits IIS, ²Whiting School of Engineering, The Johns Hopkins University
Classification of 12-lead ECGs using digital biomarkers and representation learning
David Assaraf¹, Jeremy Levy¹, Janmajay Singh², Armand Chocron¹, Joachim A. Behar^3
1Faculty of Biomedical Engineering, ²Independent researcher, ³Technion-IIT
Automatic 12-lead ECG Classification Using a Convolutional Network Ensemble
Antonio H. Ribeiro¹, Daniel Gedon², Daniel Martins Teixeira¹, Manoel Horta Ribeiro³, Antonio Luiz Ribeiro¹, Thomas B. Schön², Wagner Meira Jr^4
1Universidade Federal de Minas Gerais, ²Uppsala University, ³École Polytechnique Fédérale de Lausanne, ⁴Universidade Federal de Minas Gerasi
ECG Abnormalities Recognition Using Convolutional Network With Global Skip Connections and Custom Loss Function
Tomas Vicar¹, Jakub Hejc¹, Petra Novotna¹, Marina Ronzhina¹, Oto Janousek^2
1Department of Biomedical Engineering, Brno University of Technology, ²Department of Biomedical Engineering, Brno University of Technology, Brno, Czech Republic
Classification of 12-lead ECGs Using Intra-Heartbeat Discrete-time Fourier Transform and Inter-Heartbeat Attention
Ibrahim Hammoud, IV Ramakrishnan, Petar Djuric
Stony Brook University
On the Application of Convolutional Neural Networks for 12-lead ECG Multi-label Classification using Datasets from Multiple Centers
Davide Borra, Alice Andalò, Stefano Severi, Cristiana Corsi
University of Bologna
Classification of 12-lead ECGs Using Gradient Boosting on Features Acquired With Domain-Specific and Domain-Agnostic Methods
Durmus Umutcan Uguz, Felix Berief, Steffen Leonhardt, Christoph Hoog Antink
RWTH Aachen University
Classification of 12-Lead Electrocardiograms Using Residual Neural Networks and Transfer Learning
Sardar Ansari¹, Christopher Gillies¹, Brandon Cummings¹, Jonathan Motyka¹, Guan Wang¹, Kevin Ward¹, Hamid Ghanbari^2
1Emergency Medicine, University of Michigan, ²Internal Medicine-Cardiology, University of Michigan
Multi-label Classification of Electrocardiogram With Modified Residual Networks
Shan Yang, Heng Xiang, Qingda Kong, Chunli Wang
Chengdu Spaceon Electronics CO., LTD.
Utilization of Residual CNN-GRU with Attention Mechanism for Classification of 12-lead ECG
Petr Nejedly¹, Adam Ivora¹, Ivo Viscor¹, Josef Halamek², Pavel Jurak³, Filip Plesinger^3
1Institute of Scientific Instruments of the Czech Academy of Science, ²Institute of Scientific Instruments, CAS, CZ, ³Institute of Scientific Instruments of the CAS
Arrhythmia Detection and Classification of 12-lead ECGs Using a Deep Neural Network
wenxiao jia, Xiao Xu, Xian Xu, Yuyao Sun, Xiaoshuang Liu
pingan health technology
Automatic 12-lead ECG Classification Using Deep Neural Networks
Wenjie Cai, Shuaicong Hu, Jingying Yang, Jianjian Cao
University of Shanghai for Science and Technology
Automatic Classification of Arrhythmias by Residual Network and BiGRU With Attention Mechanism
Runnan He¹, Kuanquan Wang¹, Na Zhao¹, Qiang Sun², Yacong Li¹, Qince Li¹, Henggui Zhang^3
1Harbin Institute of Technology, ²Beijing Electric Power Hospital, ³University of Manchester
Selected Features for Classification of 12-lead ECGs
Marek Żyliński¹ and Gerard Cybulski^2
1Warsaw University of Technology, ²Department of Mechatronics, WUT
Electrocardiogram Classification by Modified EfficientNet with Data Augmentation
Naoki Nonaka and Jun Seita
RIKEN
A Deep Neural Network and Reconstructed Phase Space Approach to Classifying 12-lead ECGs
David Kaftan¹ and Richard Povinelli^2
1Marquette Energy Analytics, ²Marquette University
SE-ECGNet: Multi-scale SE-Net for Multi-lead ECG Data
Jiabo Chen¹, Tianlong Chen², Bin Xiao¹, Xiuli Bi¹, Yongchao Wang¹, Weisheng Li¹, Han Duan¹, Junhui Zhang³, Xu Ma^4
1Chongqing University of Posts and Telecommunications, ²Southwest Jiaotong University, ³The First Affiliated Hospital of Chongqing Medical University, ⁴Human Genetics Resource Center National Research Institute for Family Planning
Deep Multi-Label Multi-Instance Classification on 12-Lead ECG
Yingjing Feng and Edward Vigmond
LIRYC - University of Bordeaux
A Wide and Deep Transformer Neural Network for 12-Lead ECG Classification
Annamalai Natarajan, Yale Chang, Sara Mariani, Asif Rahman, Gregory Boverman, Shruti Vij, Jonathan Rubin
Philips Research North America
Adaptive lead weighted ResNet trained with different duration signals for classifying 12-lead ECGs
Zhibin Zhao¹, Hui Fang², Samuel Relton³, Ruqiang Yan¹, Yuhong Liu⁴, Zhijing Li¹, Jing Qin⁵, David Wong^6
1Xi'an Jiaotong University, ²Loughborough University, ³University of Leeds, ⁴Chengdu Medical College, ⁵Dalian University, ⁶University of Manchester
Rule-Based methods and Deep Learning Networks for Automatic Classification of ECG
giovanni bortolan¹, Ivaylo Christov², Iana Simova^3
1IN-CNR, ²Institute of Biophysics and Biomedical Engineering - Bulg. Accad. of Sci, ³Acibadem City Clinic Cardiovascular Center- University Hospital, Sofia
12-lead ECG Arrythmia Classification Using Convolutional Neural Network for Mutually Non-Exclusive Classes
Mateusz Soliński¹, Michał Łepek¹, Antonina Pater¹, Katarzyna Muter², Przemysław Wiszniewski³, Dorota Kokosińska¹, Judyta Salamon⁴, Zuzanna Puzio^1
1Faculty of Physics, Warsaw University of Technology, ²Faculty of Electronics and Information Technology, Warsaw University of Technology, ³Faculty of Electrical Engineering, Warsaw University of Technology, ⁴Warsaw University of Technology
Identification of Cardiac Arrhythmias from 12-lead ECG using Beat-wise Analysis and a Combination of CNN and LSTM
Mohanad Alkhodari, Leontios J. Hadjileontiadis, Ahsan H. Khandoker
Khalifa University
Multi-label Arrhythmia Classification From 12-Lead Electrocardiograms
Po-Ya Hsu¹, Po-Han Hsu¹, Tsung-Han Lee¹, Hsin-Li Liu^2
1University of California San Diego, ²Central Taiwan University of Science and Techonology
Diagnostic of Multiple Cardiac Disorders from 12-lead ECGs Using Graph Convolutional Network Based Multi-label Classification
Zheheng Jiang, Tiago Paggi de Almeida, Fernando Schlindwein, G. André Ng, Huiyu Zhou, Xin Li
University of Leicester
Automated Classification of Electrocardiograms Using Wavelet Analysis and Deep Learning
Andrew Demonbreun and Grace Mirsky
Benedictine University
Multi-label Classification of Abnormalities in 12-Lead ECG Using Deep Learning
Ao Ran¹, Dongsheng Ruan², Yuan Zheng³, Huafeng Liu^4
1Zhejiang University, ²College of computer science and technology,Zhejiang University, ³School of Aeronautics and Astronautics,Zhejiang University, ⁴College of Optical Engineering, Zhejiang University, China
ECG Arrhythmia Classification using Non-Linear Features and Convolutional Neural Networks
Sebastian Cajas, Pedro Astaiza, David Santiago Garcia Chicangana, Camilo Segura, Diego Lopez
Universidad del Cauca
Multi-Stream Deep Neural Network for 12-Lead ECG Classification
Martin Baumgartner, Dieter Hayn, Andreas Ziegl, Alphons Eggerth, Günter Schreier
AIT Austrian Institute of Technology
Impact of Neural Architecture Design on Cardiac Abnormality Classification Using 12-lead ECG Signals
Najmeh Fayyazifar¹, Selam Ahderom¹, David Suter¹, Andrew Maiorana², Girish dwivedi^3
1Edith Cowan University, ²Curtin University, ³University of Western Australia
A Real-time ECG Classification Scheme Using Anti-aliased Blocks with Low Sampling Rate
Yunkai Yu¹, Zhihong Yang², Zhicheng Yang³, Peiyao Li⁴, Yuyang You^1
1Beijing Institute of Technology, ²Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences, ³PAII Inc., ⁴Tsinghua University
Cardiac Pathologies Detection and Classification in 12-lead ECG
Radovan Smisek, Andrea Nemcova, Lucie Marsanova, Lukas Smital, Martin Vítek, Jiri Kozumplik
Brno University of Technology, Faculty of Electrical Engineering and Communication, Department of Biomedical Engineering
Interpretable XGBoost Based Classification of 12-lead ECGs Applying Information Theory Measures From Neuroscience
Hardik Rajpal¹, Madalina Sas¹, Rebecca Joakim², Chris Lockwood³, Nicholas S. Peters¹, Max Falkenberg^1
1Imperial College London, ²Wexham Park Hospital, ³Independent Researcher
A Novel Convolutional Neural Network for Arrhythmia Detection From 12-lead Electrocardiograms
Zhengling He, Pengfei Zhang, Lirui Xu, Zhongrui Bai, Hao Zhang, Weisong Li, Pan Xia, Xianxiang Chen
Aerospace Information Research Institute, Chinese Academy of Sciences
ECG Classification With a Convolutional Recurrent Neural Network
Halla Sigurthorsdottir, Jérôme Van Zaen, Ricard Delgado-Gonzalo, Mathieu Lemay
Swiss Center for Electronics and Microtechnology (CSEM)
Detecting Cardiac Abnormalities from 12-lead ECG Signals Using Feature Extraction, Dimensionality Reduction, and Machine Learning Classification
Garrett Perkins¹, J. Chase McGlinn¹, Muhammad Rizwan², Bradley Whitaker^1
1Montana State University, ²University of Management & Technology
A Bio-toolkit for Multi-Cardiac Abnormality Diagnosis Using ECG Signal and Deep Learning
Akash Kirodiwal¹, Apoorva Srivastava¹, Ashutosh Dash¹, Ayantika Saha², Gopi Vamsi Penaganti³, Sawon Pratiher⁴, sazedul alam⁵, Amit Patra⁶, Nirmalya Ghosh⁷, Nilanjan Banerjee^8
1Electrical Engineering Department,IIT Kharagpur, ²Department of Electrical Engineering IIT, Kharagpur India, ³Department of Electrical Engineering IIT Kharagpur, ⁴Electrical Engineering Department, IIT Kharagpur, ⁵PhD student, CSEE, UMBC., ⁶Professor, IIT kharagpur, ⁷Assistant Professor, IIT Kharagpur, ⁸Associate professor, CSEE, UMBC
Cardiac Abnormality Detection in 12-lead ECGs with Deep Convolutional Neural Networks Using Data Augmentation
Lucas Weber¹, Maksym Gaiduk¹, Wilhelm Daniel Scherz¹, Ralf Seepold^2
1HTWG Konstanz, ²HTWG Konstanz, I.M. Sechenov First Moscow State Medical University
Explainable Deep Neural Network for Identifying Cardiac Abnormalities Using Class Activation Map
Yu-Cheng Lin, Yun-Chieh Lee, Wen-Chiao Tsai, Win-Ken Beh, An-Yeu Wu
Graduate Institute of Electronic Engineering, National Taiwan University
Classification of 12 Lead ECG Signal Using 1D-CNN With Class Dependent Threshold
Rohit Pardasani¹ and Navchetan Awasthi^2
1GE Healthcare, ²Massachusetts General Hospital, Harvard University
Classification of Cardiac Abnormalities From ECG Signals Using SE-ResNet
Zhaowei Zhu¹, Han Wang², Tingting Zhao¹, Yangming Guo¹, Zhuoyang Xu¹, Zhuo Liu¹, Siqi Liu³, Xiang Lan², Xingzhi Sun¹, Mengling Feng^2
1Ping An Technology, ²National University of Singapore, National University Health System, ³NUS Graduate School for Integrative Sciences & Engineering
MADNN: A Multi-scale Attention Deep Neural Network for Arrythmia Classification
Ran Duan, Xiaodong He, Ouyang Zhuoran
Edan Diagnostics Ltd.
Multi-Label Classification of 12-lead ECGs by Using Residual CNN and Class-Wise Attention
Yang Liu¹, Kuanquan Wang¹, Yongfeng Yuan¹, Qince Li¹, Yacong Li¹, Yongpeng Xu², Henggui Zhang^3
1Harbin Institute of Technology, ²Yongjia County Public Security Bureau, ³University of Manchester
A Topology Informed Random Forest Classifier for ECG Classification
Paul Samuel Ignacio, Jay-Anne Bulauan, John Rick Manzanares
University of the Philippines Baguio
A Deep Learning Solution for Automatized Interpretation of 12-Lead ECGs
Alvaro Huerta Herraiz¹, Arturo Martinez-Rodrigo², José J Rieta³, Raul Alcaraz^2
1Research Group in Electronic, Biomedical and Telecommunication Engineering, University of Castilla-La Mancha, Spain., ²University of Castilla-La Mancha, ³BioMIT.org, Universitat Politecnica Valencia
Bag of Tricks for Electrocardiogram Classification with Deep Neural Networks
Seonwoo Min¹, Hyun-Soo Choi², Hyeongrok Han³, Minji Seo³, Jin-Kook Kim⁴, Junsang Park⁴, Sunghoon Jung⁴, Il-Young Oh⁵, Byunghan Lee⁶, Sungroh Yoon^7
1Seoul National University, ²T3K, SK Telecom, ³Department of Electrical and Computer engineering, Seoul National University, ⁴HUINNO Co., Ltd., ⁵Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, ⁶Department of Electronic and IT Media Engineering, Seoul National University of Science and Technology, ⁷Department of Electrical and Computer engineering, Department of Biological Sciences, Interdisciplinary Program in Bioinformatics, Interdisciplinary Program in Artificial Intelligence, ASRI, INMC, and Institute of Engineering Research, Seoul National University
Detection of Cardiac Arrhythmias From Varied Length Multichannel Electrocardiogram Recordings Using Deep Convolutional Neural Networks
Marwen Sallem¹, Adnen Saadaoui², Amina Ghrissi³, Vicente Zarzoso^4
1PacketAI, ²Vneuron, ³Université Côte d’Azur, CNRS, I3S Laboratory, ⁴Université Côte d'Azur
Prediction of Apnoea and Non-apnoea Arousals from the Polysomnogram using a Neural Network Classifier
Philip de Chazal, John Du, Nadi Sadr
University of Sydney
Rhythm classification of 12-lead ECGs using deep neural network and class-activation maps for improved explainability
Sebastian Goodfellow¹, Dmitrii Shubin², Danny Eytan², Andrew Goodwin², Anusha Jega², Azadeh Assadi², Mjaye Mazwi², Robert Greer², Sujay Nagaraj², Peter Laussen², William Dixon², Carson McLean^3
1The Hospital For Sick Children, ²Hospital for Sick Children, ³University of Toronto
ECG Segmentation using a Neural Network as the Basis for Detection of Cardiac Pathologies
Philipp Sodmann and Marcus Vollmer
Universität Greifswald
Classification of 12-lead ECG with an Ensemble Machine Learning Approach
Matteo Bodini¹, Massimo W Rivolta², Roberto Sassi^3
1Università degli Studi di Milano, ²Dipartimento di Informatica, Università degli Studi di Milano, ³Università degli Studi di Milano, Dipartimento di Informatica
Convolutional Recurrent Neural Network and LightGBM Ensemble Model for 12-lead ECG Classification
Charilaos Zisou, Andreas Sochopoulos, Konstantinos Kitsios
Aristotle University of Thessaloniki
Automatic Concurrent Arrhythmia Classification Using Deep Residual Neural Networks
Deepankar Nankani, Pallabi Saikia, Rashmi Dutta Baruah
Computer Science and Engineering Department, Indian Institute of Technology Guwahati
ECG Morphological Decomposition for Automatic Rhythm Identification
Guadalupe García Isla, Rita Laureanti, Valentina Corino, Luca Mainardi
Politecnico di Milano
Classification of 12-lead ECG Signals with Adversarial Multi-Source Domain Generalization
Hosein Hasani, Adeleh Bitarafan, Mahdieh Soleymani
Sharif University of Technology
Cardiac Arrhythmias Identification by Parallel CNNs and ECG Time-Frequency Representation
Jonathan Roberto Torres Castillo, K De Los Rios, Miguel Ángel Padilla Castañeda
Universidad Nacional Autónoma de México
Arrhythmia classification of 12-lead Electrocardiograms by Hybrid Scattering-LSTM networks
Philip Warrick¹, Masun Nabhan Homsi², Vincent Lostanlen³, Michael Eikenberg⁴, Joakim Andén^5
1Perigen, ²Helmholtz Centre for Environmental Research - UFZ, ³New York University, ⁴Flatiron Institute, ⁵KTH Royal Institute of Technology