Classification of 12-lead ECGs: the PhysioNet/Computing in Cardiology Challenge 2020

Citations

For this year’s Challenge, please cite:

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^*. Classification of 12-lead ECGs: the PhysioNet/Computing in Cardiology Challenge 2020. Physiol Meas. 2020 Nov 11. doi: 10.1088/1361-6579/abc960

Focus Issue Papers

The Physiological Measurement Focus Collection: Classification of Multilead ECGs includes the above paper.

Conference Papers

The conference papers for Computing in Cardiology 2020 are available on the CinC and IEEE websites.

The scores for the teams are available here, including scores for official entries that were scored on the validation and test data, scores for unofficial entries that were scored on the validation and test data but did not satisfy one or more of the Challenge rules or were unsuccessful on one or more of the test databases, additional metrics per database, and per class metrics for the official entries.

Abstract	Team Name	Title	Author(s)
7	SpaceOn Flattop	Multi-label Classification of Electrocardiogram With Modified Residual Networks	Shan Yang, Heng Xiang, Qingda Kong and Chunli Wang
32	ISIBrno	Utilization of Residual CNN-GRU with Attention Mechanism for Classification of 12-lead ECG	Petr Nejedly, Adam Ivora, Ivo Viscor, Josef Halamek, Pavel Jurak and Filip Plesinger
35	PALab	Arrhythmia Detection and Classification of 12-lead ECGs Using a Deep Neural Network	wenxiao jia, Xiao Xu, Xian Xu, Yuyao Sun and Xiaoshuang Liu
39	nebula	Automatic 12-lead ECG Classification Using Deep Neural Networks	Wenjie Cai, Shuaicong Hu, Jingying Yang and Jianjian Cao
44	DeepHeart	Automatic Classification of Arrhythmias by Residual Network and BiGRU With Attention Mechanism	Runnan He, Kuanquan Wang, Na Zhao, Qiang Sun, Yacong Li, Qince Li and Henggui Zhang
61	Alba_W.O.	Selected Features for Classification of 12-lead ECGs	Marek Żyliński and Gerard Cybulski
63	NN-MIH	Electrocardiogram Classification by Modified EfficientNet with Data Augmentation	Naoki Nonaka and Jun Seita
71	DSC	Multi-Class Classification of Pathologies Found on Short ECG Signals	Georgi Nalbantov, Svetoslav Ivanov and Jeffrey van Prehn
72	NTU-Accesslab	Explainable Deep Neural Network for Identifying Cardiac Abnormalities Using Class Activation Map	Yu-Cheng Lin, Yun-Chieh Lee, Wen-Chiao Tsai, Win-Ken Beh and An-Yeu Wu
74	Marquette	A Deep Neural Network and Reconstructed Phase Space Approach to Classifying 12-lead ECGs	David Kaftan and Richard Povinelli
85	CQUPT_ECG	SE-ECGNet: Multi-scale SE-Net for Multi-lead ECG Data	Jiabo Chen, Tianlong Chen, Bin Xiao, Xiuli Bi, Yongchao Wang, Weisheng Li, Han Duan, Junhui Zhang and Xu Ma
95	ECGLearner	Deep Multi-Label Multi-Instance Classification on 12-Lead ECG	Yingjing Feng and Edward Vigmond
107	prna	A Wide and Deep Transformer Neural Network for 12-Lead ECG Classification	Annamalai Natarajan, Yale Chang, Sara Mariani, Asif Rahman, Gregory Boverman, Shruti Vij and Jonathan Rubin
112	Between a ROC and a heart place	Adaptive lead weighted ResNet trained with different duration signals for classifying 12-lead ECGs	Zhibin Zhao, Hui Fang, Samuel Relton, Ruqiang Yan, Yuhong Liu, Zhijing Li, Jing Qin and David Wong
116	Gio_Ivo	Rule-Based methods and Deep Learning Networks for Automatic Classification of ECG	giovanni bortolan, Ivaylo Christov and Iana Simova
124	BioS	12-lead ECG Arrythmia Classification Using Convolutional Neural Network for Mutually Non-Exclusive Classes	Mateusz Soliński, Michał Łepek, Antonina Pater, Katarzyna Muter, Przemysław Wiszniewski, Dorota Kokosińska, Judyta Salamon and Zuzanna Puzio
127	Care4MyHeart	Identification of Cardiac Arrhythmias from 12-lead ECG using Beat-wise Analysis and a Combination of CNN and LSTM	Mohanad Alkhodari, Leontios J. Hadjileontiadis and Ahsan H. Khandoker
128	CVC	Multilabel 12-Lead Electrocardiogram Classification Using Gradient Boosting Tree Ensemble	Alexander William Wong, Weijie Sun, Sunil Vasu Kalmady, Padma Kaul and Abram Hindle
130	Code Team	Automatic 12-lead ECG Classification Using a Convolutional Network Ensemble	Antonio H. Ribeiro, Daniel Gedon, Daniel Martins Teixeira, Manoel Horta Ribeiro, Antonio Luiz Ribeiro, Thomas B. Schön and Wagner Meira Jr
133	Triage	Combining Scatter Transform and Deep Neural Networks for Multilabel ECG Signal Classification	Maximilian Oppelt, Maximilian Riehl, Felix Kemeth and Jan Steffan
134	JuJuRock	Multi-label Arrhythmia Classification From 12-Lead Electrocardiograms	Po-Ya Hsu, Po-Han Hsu, Tsung-Han Lee and Hsin-Li Liu
135	Leicester-Fox	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 and Xin Li
138	Eagles	Automated Classification of Electrocardiograms Using Wavelet Analysis and Deep Learning	Andrew Demonbreun and Grace Mirsky
139	EASTBLUE	Multi-label Classification of Abnormalities in 12-Lead ECG Using Deep Learning	Ao Ran, Dongsheng Ruan, Yuan Zheng and Huafeng Liu
144	CardioLux - Unicauca	ECG Arrhythmia Classification using Non-Linear Features and Convolutional Neural Networks	Sebastian Cajas, Pedro Astaiza, David Santiago Garcia Chicangana, Camilo Segura and Diego Lopez
148	easyG	Multi-Stream Deep Neural Network for 12-Lead ECG Classification	Martin Baumgartner, Dieter Hayn, Andreas Ziegl, Alphons Eggerth and Günter Schreier
161	ECU	Impact of Neural Architecture Design on Cardiac Abnormality Classification Using 12-lead ECG Signals	Najmeh Fayyazifar, Selam Ahderom, David Suter, Andrew Maiorana and Girish dwivedi
162	Triology	A Real-time ECG Classification Scheme Using Anti-aliased Blocks with Low Sampling Rate	Yunkai Yu, Zhihong Yang, Zhicheng Yang, Peiyao Li and Yuyang You
171	HITTING	Cardiac Pathologies Detection and Classification in 12-lead ECG	Radovan Smisek, Andrea Nemcova, Lucie Marsanova, Lukas Smital, Martin Vítek and Jiri Kozumplik
185	Madhardmax	Interpretable XGBoost Based Classification of 12-lead ECGs Applying Information Theory Measures From Neuroscience	Hardik Rajpal, Madalina Sas, Rebecca Joakim, Chris Lockwood, Nicholas S. Peters and Max Falkenberg
189	BUTTeam	ECG Abnormalities Recognition Using Convolutional Network With Global Skip Connections and Custom Loss Function	Tomas Vicar, Jakub Hejc, Petra Novotna, Marina Ronzhina and Oto Janousek
196	MetaHeart	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 and Xianxiang Chen
198	Pink Irish Hat	ECG Classification With a Convolutional Recurrent Neural Network	Halla Sigurthorsdottir, Jérôme Van Zaen, Ricard Delgado-Gonzalo and Mathieu Lemay
202	Technion_AIMLAB	Classification of 12-lead ECGs using digital biomarkers and representation learning	David Assaraf, Jeremy Levy, Janmajay Singh, Armand Chocron and Joachim A. Behar
217	Whitaker’s Lab	Detecting Cardiac Abnormalities from 12-lead ECG Signals Using Feature Extraction, Dimensionality Reduction, and Machine Learning Classification	Garrett Perkins, J. Chase McGlinn, Muhammad Rizwan and Bradley Whitaker
225	Cardio-Challengers	A Bio-toolkit for Multi-Cardiac Abnormality Diagnosis Using ECG Signal and Deep Learning	Akash Kirodiwal, Apoorva Srivastava, Ashutosh Dash, Ayantika Saha, Gopi Vamsi Penaganti, Sawon Pratiher, sazedul alam, Amit Patra, Nirmalya Ghosh and Nilanjan Banerjee
227	Team UIO	Convolutional Neural Network and Rule-Based Algorithms for Classifying 12-lead ECGs	Bjørn-Jostein Singstad and Christian Tronstad
229	UC_Lab_Kn	Cardiac Abnormality Detection in 12-lead ECGs with Deep Convolutional Neural Networks Using Data Augmentation	Lucas Weber, Maksym Gaiduk, Wilhelm Daniel Scherz and Ralf Seepold
236	PhysioNet Challenge Organizers	Classification of 12-lead ECGs: the PhysioNet/Computing in Cardiology Challenge 2020	Matthew Reyna, Erick Andres Perez Alday, Annie Gu, Chengyu Liu, Salman Seyedi, Ali Bahrami Rad, Andoni Elola, Qiao Li, Ashish Sharma and Gari Clifford
253	UMCUVA	Automated Comprehensive Interpretation of 12-lead Electrocardiograms Using Pre-trained Exponentially Dilated Causal Convolutional Neural Networks	Max Bos, Rutger van de Leur, Jeroen Vranken, Deepak Gupta, Pim van der Harst, Pieter Doevendans and René van Es
277	AI Strollers	Classification of 12 Lead ECG Signal Using 1D-CNN With Class Dependent Threshold	Rohit Pardasani and Navchetan Awasthi
281	HeartBeats	Classification of Cardiac Abnormalities From ECG Signals Using SE-ResNet	Zhaowei Zhu, Han Wang, Tingting Zhao, Yangming Guo, Zhuoyang Xu, Zhuo Liu, Siqi Liu, Xiang Lan, Xingzhi Sun and Mengling Feng
282	Minibus	MADNN: A Multi-scale Attention Deep Neural Network for Arrythmia Classification	Ran Duan, Xiaodong He and Ouyang Zhuoran
285	ECGMaster	Multi-Label Classification of 12-lead ECGs by Using Residual CNN and Class-Wise Attention	Yang Liu, Kuanquan Wang, Yongfeng Yuan, Qince Li, Yacong Li, Yongpeng Xu and Henggui Zhang
297	Cordi-Ak	A Topology Informed Random Forest Classifier for ECG Classification	Paul Samuel Ignacio, Jay-Anne Bulauan and John Rick Manzanares
305	ELBIT	A Deep Learning Solution for Automatized Interpretation of 12-Lead ECGs	Alvaro Huerta Herraiz, Arturo Martinez-Rodrigo, José J Rieta and Raul Alcaraz
307	SBU_AI	Classification of 12-lead ECGs Using Intra-Heartbeat Discrete-time Fourier Transform and Inter-Heartbeat Attention	Ibrahim Hammoud, IV Ramakrishnan and Petar Djuric
328	DSAIL_SNU	Bag of Tricks for Electrocardiogram Classification with Deep Neural Networks	Seonwoo Min, Hyun-Soo Choi, Hyeongrok Han, Minji Seo, Jin-Kook Kim, Junsang Park, Sunghoon Jung, Il-Young Oh, Byunghan Lee and Sungroh Yoon
339	MIndS	Detection of Cardiac Arrhythmias From Varied Length Multichannel Electrocardiogram Recordings Using Deep Convolutional Neural Networks	Marwen Sallem, Adnen Saadaoui, Amina Ghrissi and Vicente Zarzoso
349	CardiUniBo	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 and Cristiana Corsi
353	LaussenLabs	Rhythm classification of 12-lead ECGs using deep neural network and class-activation maps for improved explainability	Sebastian Goodfellow, Dmitrii Shubin, Danny Eytan, Andrew Goodwin, Anusha Jega, Azadeh Assadi, Mjaye Mazwi, Robert Greer, Sujay Nagaraj, Peter Laussen, William Dixon and Carson McLean
356	heartly-ai	ECG Segmentation using a Neural Network as the Basis for Detection of Cardiac Pathologies	Philipp Sodmann and Marcus Vollmer
363	Desafinado	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 and Christoph Hoog Antink
374	MCIRCC	Classification of 12-Lead Electrocardiograms Using Residual Neural Networks and Transfer Learning	Sardar Ansari, Christopher Gillies, Brandon Cummings, Jonathan Motyka, Guan Wang, Kevin Ward and Hamid Ghanbari
406	BiSP Lab	Classification of 12-lead ECG with an Ensemble Machine Learning Approach	Matteo Bodini, Massimo W Rivolta and Roberto Sassi
417	AUTh Team	Convolutional Recurrent Neural Network and LightGBM Ensemble Model for 12-lead ECG Classification	Charilaos Zisou, Andreas Sochopoulos and Konstantinos Kitsios
424	deepzx987	Automatic Concurrent Arrhythmia Classification Using Deep Residual Neural Networks	Deepankar Nankani, Pallabi Saikia and Rashmi Dutta Baruah
435	Germinating	ECG Morphological Decomposition for Automatic Rhythm Identification	Guadalupe García Isla, Rita Laureanti, Valentina Corino and Luca Mainardi
445	Sharif AI Team	Classification of 12-lead ECG Signals with Adversarial Multi-Source Domain Generalization	Hosein Hasani, Adeleh Bitarafan and Mahdieh Soleymani
456	UIDT-UNAM	Cardiac Arrhythmias Identification by Parallel CNNs and ECG Time-Frequency Representation	Jonathan Roberto Torres Castillo, K De Los Rios and Miguel Ángel Padilla Castañeda
462	BitScattered	Arrhythmia classification of 12-lead Electrocardiograms by Hybrid Scattering-LSTM networks	Philip Warrick, Masun Nabhan Homsi, Vincent Lostanlen, Michael Eikenberg and Joakim Andén

Supported by the National Institute of Biomedical Imaging and Bioengineering (NIBIB) under NIH grant number R01EB030362.

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