Publications from Will Two Do? Varying Dimensions in Electrocardiography: The PhysioNet/Computing in Cardiology Challenge 2021
The following paper describes the PhysioNet/Computing in Cardiology Challenge. Please cite this publication when referencing the Challenge.
Classification of 12-lead ECGs: the PhysioNet/Computing in Cardiology Challenge 2020.
Perez Alday EA, Gu A, Shah AJ, Robichaux C, Wong AI, Liu C, Liu F, Rad AB, Elola A, Seyedi S, Li Q, Sharma A, Clifford GD, Reyna MA. Computing in Cardiology 2020.
The following papers were presented at the Computing in Cardiology Conference.
Will Two Do? Varying Dimensions in Electrocardiography: the PhysioNet/Computing in Cardiology Challenge 2021Matthew Reyna1, Nadi Sadr1, Erick Perez Alday1, Annie Gu1, Amit Shah1, Chad Robichaux1, Ali Rad1, Andoni Elola2, Salman Seyedi1, Sardar Ansari3, Hamid Ghanbari3, Qiao Li1, Ashish Sharma1, Gari Clifford4
1Emory University, 2University of the Basque Country UPV/EHU, 3University of Michigan, 4Emory University and Georgia Institute of Technology
Pathologies Prediction on Short ECG Signals with Focus on Feature Extraction Based on Beat Morphology and Image Deformation
Jeffrey Prehn1, Svetoslav Ivanov2, Georgi Nalbantov1
1Data Science Consulting Ltd., 2DSC
A Two-Phase Multilabel ECG Classification Using One-Dimensional Convolutional Neural Network and Modified Labels
Peter Bugata1, Peter Bugata Jr.1, Vladimira Kmecova1, Monika Stankova1, David Gajdos1, David Hudak1, Richard Stana2, Simon Horvat2, Lubomir Antoni2, Gabriela Vozarikova2, Erik Bruoth2, Alexander Szabari2
1VSL Software, a.s., 2Pavol Jozef Šafárik University
Two will do: Convolutional Neural Network with Asymmetric Loss and Self-Learning Label Correction for Imbalanced Multi-Label ECG Data Classification
Cristina Gallego Vázquez1, Alexander Breuss1, Oriella Gnarra1, Julian Portmann2, Giulia Da Poian1
1Sensory‐Motor Systems (SMS) Lab, Department of Health Sciences and Technology (D‐HEST), Institute of Robotics and Intelligent Systems (IRIS), ETH Zurich, 2Department of Computer Science, ETH Zurich
Learning ECG Representations for Multi-Label Classification of Cardiac Abnormalities
Jangwon Suh1, Jimyeong Kim1, Eunjung Lee1, Jaeill Kim1, Duhun Hwang1, Jungwon Park1, Junghoon Lee1, Jaeseung Park1, Seo-Yoon Moon1, Yeonsu Kim1, Min Kang1, Soonil Kwon2, Eue-Keun Choi3, Wonjong Rhee1
1Seoul National University, 2Seoul National University Hospital, 3Seoul National University College of Medicine
Reduced-Lead ECG Classifier Model Trained with DivideMix and Model Ensemble
Hiroshi Seki1, Takashi Nakano1, Koshiro Ikeda1, Shinji Hirooka1, Takaaki Kawasaki1, Mitsutomo Yamada1, Shumpei Saito1, Toshitaka Yamakawa2, Shimpei Ogawa1
1AMI inc., 2Kumamoto University
Convolution-Free Waveform Transformers for Multi-Lead ECG Classification
Annamalai Natarajan1, Gregory Boverman1, Yale Chang1, Corneliu Antonescu2, Jonathan Rubin1
1Philips Research North America, 2University of Arizona Banner Health
Classification of ECG using Ensemble of Residual CNNs with Attention Mechanism
Petr Nejedly1, Adam Ivora2, Ivo Viscor2, Zuzana Koscova2, Radovan Smisek3, Pavel Jurak2, Filip Plesinger2
1Institute of Scientific Instruments of the Czech Academy of Science, 2Institute of Scientific Instruments of the CAS, 3Brno University of Technology, Faculty of Electrical Engineering and Communication, Department of Biomedical Engineering
Combining a ResNet Model with Handcrafted Temporal Features for ECG Classification with Varying Number of Leads
Stefano Magni, Andrea Sansonetti, Chiara Salvi, Tiziana Tabiadon, Guadalupe García Isla
Politecnico di Milano
MTFNet: A Morphological and Temporal Features Network for multiple leads ECG Classification
Lebing Pan, Weibai Pan, Mengxue Li, Yuxia Guan, Ying An
Central South University
Towards Generalization of Cardiac Abnormality Classification Using ECG Signal
Xiaoyu Li1, Chen Li1, Xian Xu2, Yuhua Wei1, Jishang Wei3, Yuyao Sun2, Buyue Qian4, Xiao Xu2
1Xi'an Jiaotong University, 2Ping An Health Technology, 3HP Labs, 4The First Affiliated Hospital of Xi'an Jiaotong University
3-D ECG images with Deep Learning Approach for Identification of Cardiac Abnormalities from a Variable Number of Leads
giovanni bortolan
IN-CNR
A Branched Deep Neural Network for End-to-end Classification from ECGs with Varying Dimensions
Han Duan1, Junchao Fan1, Junhui Zhang2, Bin Xiao1, Xiuli Bi1, Xu Ma3
1Chongqing Key Laboratory of Image Cognition, Chongqing University of Posts and Telecommunications, Chongqing 400065, China, 2Health Management Center, the First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China, 3Human Genetics Resource Center, National Health Commission, Beijing 100181, China
Hybrid Arrhythmia Detection on Varying-Dimensional Electrocardiography: Combining Deep Neural Networks and Clinical Rules
Hao WEN1 and Jingsu KANG2
1Beihang University, 2Tianjin Medical University
Using mel-frequency cepstrum and amplitude-time heart variability as XGBoost handcrafted features for heart disease detection
Sergey Krivenko1, Anatolii Pulavskyi1, Liudmyla Kryvenko2, Olha Krylova2, Stanislav Krivenko3
1HealthEntire, 2Kharkiv National Medical University, 3Kharkiv National University of radioelectronics
Two Might Do: A Beat-by-Beat Classification of Cardiac Abnormalities using Deep Learning with Domain-Specific Features
Berken Utku Demirel1, Adnan Harun Dogan2, Mohammad Abdullah Al Faruque3
1University of California Irvine, 2Middle East Technical University, 3University of California, Irvine
Multi-label Classification of Electrocardiogram using a Deep Neural Network
Hanshuang Xie1, Qineng cao2, Jiayi Yan3, hong z3
1Hangzhou Proton Technology Co Ltd, 2Hangzhou Proton Technology Co., Ltd. in Hangzhou, 3Hangzhou Proton Technology Co.,Ltd.
Swarm Decomposition Enhances the Discrimination of Cardiac Arrhythmias in Varied-lead ECG Using ResNet-BiLSTM Network Activations
Mohanad Alkhodari1, Georgios Apostolidis2, Charilaos Zisou2, Leontios Hadjileontiadis1, Ahsan Khandoker1
1Khalifa University, 2Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki
Cardiac Abnormality Detection based on an Ensemble Voting of Single-Lead Classifier Predictions
Pierre Aublin1, Julien Oster1, Mouin Ben Ammar2, Jérémy Fix3, Michel Barret4
1INSERM, 2ENSTA, 3CentraleSupélec, 4International Research Lab Georgia Tech - CNRS IRL 2958 CentraleSupélec
Cardiac Abnormalities Recognition in ECG Using a Convolutional Network with Attention and Input with an Adaptable Number of Leads
Tomas Vicar1, Petra Novotna1, Jakub Hejc2, Oto Janousek3, Marina Ronzhina1
1Department of Biomedical Engineering, Brno University of Technology, 2International Clinical Research Center, St. Anne’s University Hospital, Brno, Czech Republic, 3Department of Biomedical Engineering, Brno University of Technology, Brno, Czech Republic
Semi-supervised Learning for ECG Classification
Rui Rodrigues
FCT Nova
N-BEATS for Heart Disfunction Classification
Bartosz Puszkarski, Krzysztof Hryniów, Grzegorz Sarwas
Warsaw University of Technology
Improving Machine Learning Education during the COVID-Pandemic using past Computing in Cardiology Challenges
Maurice Rohr1, Filip Plesinger2, Veronika Bulkova3, Christoph Hoog Antink4
1Technische Universität Darmstadt, 2Institute of Scientific Instruments of the CAS, 3Medical Data Transfer, s.r.o., 4TU Darmstadt
Automatic Diagnosis of Cardiac Disease from Twelve-lead and Reduced-lead ECGs using Multi-label Classification
Prathic Sundararajan, Kevin Moses, Cristhian Potes, Saman Parvaneh
Edwards Lifesciences
Multi-Label Cardiac Abnormalities Classification on Selected Leads of ECG Signals
Zhuoyang Xu, Yangming Guo, Tingting Zhao, Zhuo Liu, Xingzhi Sun
Ping An Technology
Cardiac Anomalies Detection Through 2D-CNN and ECG Spectrograms
Jonathan Roberto Torres Castillo and Miguel Padilla Castañeda
Universidad Nacional Autónoma de México
Multi-label ECG classification using Convolutional Neural Networks in a Classifier Chain
Bjørn-Jostein Singstad1, Pål Brekke1, Eraraya Muten2
1Oslo University Hospital, 2Institut Teknologi Bandung
A Mixed-Domain Self-Attention Network for Multilabel Cardiac Irregularity Classification Using Reduced-Lead Electrocardiogram
Hao-Chun Yang1, Wan-Ting Hsieh2, Trista Pei-Chun Chen2
1National Tsing Hua University, 2Inventec Corporation
Towards High Generalization Performance on Electrocardiogram Classification
Hyeongrok Han1, Seongjae Park2, Seonwoo Min1, Hyun-Soo Choi3, Eunji Kim1, Hyunki Kim1, Sangha Park1, Jin-Kook Kim2, Junsang Park2, Junho An2, Kwnanglo Lee2, Wonsun Jeong2, Sangil Chon2, Kwonwoo Ha2, Myungkyu Han2, Sungroh Yoon1
1Seoul National University, 2HUINNO Co., Ltd, 3Kangwon National University
Deep Discriminative Domain Generalization with Adversarial Feature Learning for Classifying ECG Signals
Zuogang Shang1, Zhibin Zhao2, Hui Fang3, Samuel Relton4, Darcy Murphy5, Zoe Hancox4, Ruqiang Yan2, David Wong5
1Xi’an Jiaotong University, 2the Department of Mechanical Engineering, Xi’an Jiaotong University, 3the Department of Computer Science, Loughborough University, 4Leeds Institute of Health Sciences, University of Leeds, 5the Department of Computer Science and the Center for Health Informatics, University of Mancheste
A Novel Multi-Scale Convolutional Neural Network for Arrhythmia Classification on Reduced-lead ECGs
Pan Xia1, Zhengling He2, Yusi Zhu1, Zhongrui Bai3, Xianya Yu1, Yuqi Wang4, Fanglin Geng1, Lidong Du1, Xianxiang Chen1, Peng Wang1, Zhen Fang1
1Aerospace Information Research Institute, Chinese Academy of Sciences, 2University of Chinese Academy of Sciences, 3School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, 4Institute of Microelectronics of Chinese Academy of Sciences
Classifying different dimensional ECGs using deep residual convolutional neural networks
Wenjie Cai, Fanli Liu, Xuan Wang, Bolin Xu, Yaohui Wang
University of Shanghai for Science and Technology
Multiple Cardiac Disease Detection from Minimal-Lead ECG Combining Feedforward Neural Networks with a One-vs-Rest Approach
Santiago Jiménez-Serrano1, Miguel Rodrigo2, Conrado J. Calvo3, José Millet Roig1, Francisco Castells1
1Instituto ITACA - Universitat Politècnica de València, 2Electronics Engineering Department, Universitat de València, 3Universitat Politècnica de València
Robust and Task-Aware Training of Deep Residual Networks for Varying-Lead ECG Classification
Hansheng Ren, Miao Xiong, Bryan Hooi
National University of Singapore
Rethinking ECG Classification with Neural Networks as a Sequence-to-Sequence Task
Philipp Sodmann1, Lars Kaderali1, Marcus Vollmer2
1Universität Greifswald, 2Institute of Bioinformatics, University Medicine Greifswald
Multi-label Classification on 12, 6, 3 and 2 Lead ECG Signals using Convolutional Recurrent Neural Networks
Niels Osnabrugge1, Felix Rustemeyer1, Francesca Battipaglia2, Christos Kaparakis1, Kata Keresztesi1, Joel Karel1, Pietro Bonizzi3
1Maastricht University, 2hearMAASters (Maastricht University), 3Department of Data Science and Knowledge Engineering, Maastricht University
Detecting Cardiac Abnormalities with Multi-Lead ECG Signals: A Modular Network Approach
Ryan Clark1, Mohammadreza Heydarian2, Mohammad Siddiqui1, Sajjad Rashidiani3, Md Asif Khan3, Thomas Doyle3
1School of Biomedical Engineering, McMaster University, 2Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON, Canada, 3Department of Electrical and Computer Engineering, McMaster University
An InceptionTime-Inspired Convolutional Neural Network to Detect Cardiac Abnormalities in Reduced-Lead ECG Data
Harry Crocker and Aaron Costall
University of Bath
An Ensemble Learning Approach to Detect Cardiac Abnormalities in ECG Data Irrespective of Lead Availability
Tim Uhlemann1, Sebastian Wegener2, Joshua Prim1, Nils Gumpfer1, Dimitri Grün2, Jennifer Hannig1, Till Keller2, Michael Guckert1
1Cognitive Information Systems, Kompetenzzentrum für Informationstechnologie, Technische Hochschule Mittelhessen - University of Applied Sciences, 2Department of Internal Medicine I, Cardiology, Justus-Liebig-University Gießen
Ensemble Learning of Modified Residual Networks for Classifying ECG with Different Set of Leads
Federico Muscato1, Valentina Corino2, Luca Mainardi2
1Department of Electronics, Information and Bioengineering, Politecnico di Milano, 2Politecnico di Milano
Diagnosis of Cardiac Abnormalities Applying Scattering Transform and Fourier-Bessel Expansion on ECG Signals
Nidhi Sawant and Shivnarayan Patidar
National Institute of Technology Goa
Reduced-Lead Electrocardiogram Classification using Wavelet Analysis and Deep Learning
Adrian Cornely, Alondra Carrillo, Grace Mirsky
Benedictine University
Generative Pre-Trained Transformer for Cardiac Abnormality Detection
Pierre Louis Gaudilliere, Halla Sigurthorsdottir, Jérôme Van Zaen, Clémentine Aguet, Mathieu Lemay, Ricard Delgado-Gonzalo
Swiss Center for Electronics and Microtechnology (CSEM)
Automated Diagnosis of Reduced-lead Electrocardiograms using a Shared Classifier
Hidde Jessen, Rutger van de Leur, Pieter Doevendans, Rene van Es
UMC Utrecht
Classification of ECG Signals with Different Lead Systems Using AutoML
Matteo Bodini1, Massimo W Rivolta2, Roberto Sassi2
1Università degli Studi di Milano, 2Dipartimento di Informatica, Università degli Studi di Milano
Spatio-temporal ECG Network for Detecting Cardiac Disorders from Multi-lead ECGs
Long Chen1, Zheheng Jiang2, Tiago P. Almeida1, Fernando S. Schlindwein1, Jakevir S. Shoker1, G. Andre Ng1, Huiyu Zhou1, Xin Li1
1University of Leicester, 2Lancaster University
Multi-Label Classification of Cardiac Abnormalities for Multi-Lead ECG Recordings Based on Auto-Encoder Features and a Neural Network Classifier
Onno Linschmann1, Maurice Rohr2, Klaus Leonhardt1, Christoph Hoog Antink3
1RWTH Aachen, 2Technische Universität Darmstadt, 3TU Darmstadt
ECG classification combining conventional signal analysis, random forests and neural networks – a stacked learning scheme
Martin Kropf1, Martin Baumgartner2, Sai Pavan Kumar Veeranki3, Lukas Haider2, Dieter Hayn4, Günter Schreier2
1Charité Berlin, 2AIT Austrian Institute of Technology, 3Technical University Graz, 4AIT Austrian Institute of Technology and Ludwig Boltzmann Institute for Digital Health and Prevention
Arrhythmia Classification of Reduced-Lead Electrocardiograms by Scattering-Recurrent Networks
Philip Warrick1, Vincent Lostanlen2, Michael Eickenberg3, Masun Nabhan Homsi4, Adrian Rodriguez5, Joakim Anden5
1PeriGen Canada, McGill University, 2LS2N, CNRS, École Centrale de Nantes, 3Flatiron Institute, 4Helmholtz Centre for Environmental Research - UFZ, 5KTH Royal Institute of Technology
Automatic Classification of 12-, 6-, 4-, 3-, and 2-Lead Electrocardiograms Using Morphological Feature Extraction
Alexander Hammer1, Matthieu Scherpf2, Hannes Ernst1, Jonas Weiß1, Daniel Schwensow1, Martin Schmidt1
1TU Dresden, 2TU Dresden, Institute of Biomedical Engineering, Dresden, Germany
Incorporating Clinical and Heartbeat Level Features with Multichannel ECG for Cardiac Abnormality Detection Using Parallel CNN and GAP Network
Deepankar Nankani and Rashmi Dutta Baruah
Department of Computer Science and Engineering, Indian Institute of Technology Guwahati
Channel self-Attention Deep Learning Framework for Multi-Cardiac Abnormality Diagnosis from Varied-lead ECG Signals
Apoorva Srivastava1, Ajith Hari1, Sawon Pratiher2, sazedul alam3, Nirmalya Ghosh1, Nilanjan Banerjee4, Amit Patra1
1Indian Institute of Technology, Kharagpur, 2IIT Kharagpur, 3PhD student, CSEE, UMBC., 4Assistant Professor, UMBC
Leveraging Period-specific Variations in ECG Topology for Classification Tasks
Paul Samuel Ignacio
University of the Philippines Baguio
Multi-label Cardiac Abnormality Classification from Electrocardiogram using Deep Convolutional Neural Networks
Nima Wickramasinghe1 and Mohamed Athif2
1Department of Electronic and Telecommunication Engineering, University of Moratuwa, 2Department of Biomedical Engineering, Boston University
High-Order Cardiomyopathy Human Heart Model and Mesh Generation
Fariba Mohammadi1, Suzanne Shontz1, Cristian Linte2
1University of Kansas, 2Rochester Institute of Technology
Demystifying Heart Failure with Mid-Range Ejection Fraction using Machine Learning
Achal Dixit and Soumi Chattopadhyay
Indian Institute of Information Technology Guwahati
Spatiotemporal Quantification of In Vitro Cardiomyocyte Contraction Dynamics Using Video Microscopy-based Software Tool
Antti Ahola and Jari Hyttinen
Tampere University
Dynamics of Ventricular Electrophysiology are Unmasked through Noninvasive Electrocardiographic Imaging
Job Stoks1, Bianca Van Rees1, Uyen Chau Nguyen2, Ralf Peeters1, Paul Volders1, Matthijs Cluitmans1
1Maastricht University, 2Cardiovascular Research Institute Maastricht (CARIM)
Electrocardiographic Imaging in Atrial Fibrillation: Selection of the Optimal Tikhonov-Regularization Parameter
Rubén Molero Alabau, Carlos Fambuena Santos, Andreu M. Climent, Maria de la Salud Guillem Sánchez
Universitat Politècnica de València