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Papers, code, and scores for the 2020 Challenge are available on this page.
To refer to the 2020 Challenge, please cite:
Please also cite the standard PhysioNet citation. You can find followup articles to the 2020 Challenge in the 2021 Challenge and in the Journal of Physiological Measurement Focus Issue on Classification of Multilead ECGs.
The conference papers for Computing in Cardiology 2020 are available on the CinC and IEEE websites.
The ranks of the teams and their papers and entry code are available in the table below.
| Rank | Team name | Team member(s) | Paper | Code | | 1 | prna | Annamalai Natarajan, Yale Chang, Sara Mariani, Asif Rahman, Gregory Boverman, Shruti Vij and Jonathan Rubin | A Wide and Deep Transformer Neural Network for 12-Lead ECG Classification | Link (86MB) | | 2 | Between a ROC and a heart place | Zhibin Zhao, Hui Fang, Samuel Relton, Ruqiang Yan, Yuhong Liu, Zhijing Li, Jing Qin and David Wong | Adaptive lead weighted ResNet trained with different duration signals for classifying 12-lead ECGs | Link (30MB) | | 3 | HeartBeats | Zhaowei Zhu, Han Wang, Tingting Zhao, Yangming Guo, Zhuoyang Xu, Zhuo Liu, Siqi Liu, Xiang Lan, Xingzhi Sun and Mengling Feng | Classification of Cardiac Abnormalities From ECG Signals Using SE-ResNet | Link (145MB) | | 4 | Triage | Maximilian Oppelt, Maximilian Riehl, Felix Kemeth and Jan Steffan | Combining Scatter Transform and Deep Neural Networks for Multilabel ECG Signal Classification | Link (79MB) | | 5 | Sharif AI Team | Hosein Hasani, Adeleh Bitarafan and Mahdieh Soleymani | Classification of 12-lead ECG Signals with Adversarial Multi-Source Domain Generalization | Link (25MB) | | 6 | DSAIL_SNU | Seonwoo Min, Hyun-Soo Choi, Hyeongrok Han, Minji Seo, Jin-Kook Kim, Junsang Park, Sunghoon Jung, Il-Young Oh, Byunghan Lee and Sungroh Yoon | Bag of Tricks for Electrocardiogram Classification with Deep Neural Networks | Link (99MB) | | 7 | UMCUVA | Max Bos, Rutger van de Leur, Jeroen Vranken, Deepak Gupta, Pim van der Harst, Pieter Doevendans and René van Es | Automated Comprehensive Interpretation of 12-lead Electrocardiograms Using Pre-trained Exponentially Dilated Causal Convolutional Neural Networks | Link (286MB) | | 8 | CQUPT_ECG | Jiabo Chen, Tianlong Chen, Bin Xiao, Xiuli Bi, Yongchao Wang, Weisheng Li, Han Duan, Junhui Zhang and Xu Ma | SE-ECGNet: Multi-scale SE-Net for Multi-lead ECG Data | Link (221KB) | | 9 | ECU | Najmeh Fayyazifar, Selam Ahderom, David Suter, Andrew Maiorana and Girish dwivedi | Impact of Neural Architecture Design on Cardiac Abnormality Classification Using 12-lead ECG Signals | Link (151KB) | | 10 | PALab | wenxiao jia, Xiao Xu, Xian Xu, Yuyao Sun and Xiaoshuang Liu | Arrhythmia Detection and Classification of 12-lead ECGs Using a Deep Neural Network | Link (402KB) | | 11 | HITTING | Radovan Smisek, Andrea Nemcova, Lucie Marsanova, Lukas Smital, Martin Vítek and Jiri Kozumplik | Cardiac Pathologies Detection and Classification in 12-lead ECG | Link (58MB) | | 12 | Gio_Ivo | giovanni bortolan, Ivaylo Christov and Iana Simova | Rule-Based methods and Deep Learning Networks for Automatic Classification of ECG | Link (87MB) | | 13 | AUTh Team | Charilaos Zisou, Andreas Sochopoulos and Konstantinos Kitsios | Convolutional Recurrent Neural Network and LightGBM Ensemble Model for 12-lead ECG Classification | Link (52MB) | | 14 | BioS | Mateusz Soliński, Michał Łepek, Antonina Pater, Katarzyna Muter, Przemysław Wiszniewski, Dorota Kokosińska, Judyta Salamon and Zuzanna Puzio | 12-lead ECG Arrythmia Classification Using Convolutional Neural Network for Mutually Non-Exclusive Classes | Link (58MB) | | 15 | UC_Lab_Kn | Lucas Weber, Maksym Gaiduk, Wilhelm Daniel Scherz and Ralf Seepold | Cardiac Abnormality Detection in 12-lead ECGs with Deep Convolutional Neural Networks Using Data Augmentation | Link (208KB) | | 16 | Cardio-Challengers | Akash Kirodiwal, Apoorva Srivastava, Ashutosh Dash, Ayantika Saha, Gopi Vamsi Penaganti, Sawon Pratiher, sazedul alam, Amit Patra, Nirmalya Ghosh and Nilanjan Banerjee | A Bio-toolkit for Multi-Cardiac Abnormality Diagnosis Using ECG Signal and Deep Learning | Link (987KB) | | 17 | JuJuRock | Po-Ya Hsu, Po-Han Hsu, Tsung-Han Lee and Hsin-Li Liu | Multi-label Arrhythmia Classification From 12-Lead Electrocardiograms | Link (36MB) | | 18 | Minibus | Ran Duan, Xiaodong He and Ouyang Zhuoran | MADNN: A Multi-scale Attention Deep Neural Network for Arrythmia Classification | Link (17MB) | | 19 | Desafinado | Durmus Umutcan Uguz, Felix Berief, Steffen Leonhardt and Christoph Hoog Antink | Classification of 12-lead ECGs Using Gradient Boosting on Features Acquired With Domain-Specific and Domain-Agnostic Methods | Link (255MB) | | 20 | Team UIO | Bjørn-Jostein Singstad and Christian Tronstad | Convolutional Neural Network and Rule-Based Algorithms for Classifying 12-lead ECGs | Link (19KB) | | 21 | Eagles | Andrew Demonbreun and Grace Mirsky | Automated Classification of Electrocardiograms Using Wavelet Analysis and Deep Learning | Link (60MB) | | 22 | BUTTeam | Tomas Vicar, Jakub Hejc, Petra Novotna, Marina Ronzhina and Oto Janousek | ECG Abnormalities Recognition Using Convolutional Network With Global Skip Connections and Custom Loss Function | Link (124MB) | | 23 | DSC | Georgi Nalbantov, Svetoslav Ivanov and Jeffrey van Prehn | Multi-Class Classification of Pathologies Found on Short ECG Signals | Link (5.4MB) | | 24 | Pink Irish Hat | Halla Sigurthorsdottir, Jérôme Van Zaen, Ricard Delgado-Gonzalo and Mathieu Lemay | ECG Classification With a Convolutional Recurrent Neural Network | Link (7.6MB) | | 25 | Madhardmax | Hardik Rajpal, Madalina Sas, Rebecca Joakim, Chris Lockwood, Nicholas S. Peters and Max Falkenberg | Interpretable XGBoost Based Classification of 12-lead ECGs Applying Information Theory Measures From Neuroscience | Link (7.0MB) | | 26 | Care4MyHeart | Mohanad Alkhodari, Leontios J. Hadjileontiadis and Ahsan H. Khandoker | Identification of Cardiac Arrhythmias from 12-lead ECG using Beat-wise Analysis and a Combination of CNN and LSTM | Link (43MB) | | 27 | MCIRCC | Sardar Ansari, Christopher Gillies, Brandon Cummings, Jonathan Motyka, Guan Wang, Kevin Ward and Hamid Ghanbari | Classification of 12-Lead Electrocardiograms Using Residual Neural Networks and Transfer Learning | Link (30MB) | | 28 | heartly-ai | Philipp Sodmann and Marcus Vollmer | ECG Segmentation using a Neural Network as the Basis for Detection of Cardiac Pathologies | Link (42MB) | | 29 | Code Team | Antonio H. Ribeiro, Daniel Gedon, Daniel Martins Teixeira, Manoel Horta Ribeiro, Antonio Luiz Ribeiro, Thomas B. Schön and Wagner Meira Jr | Automatic 12-lead ECG Classification Using a Convolutional Network Ensemble | Link (276KB) | | 30 | ISIBrno | Petr Nejedly, Adam Ivora, Ivo Viscor, Josef Halamek, Pavel Jurak and Filip Plesinger | Utilization of Residual CNN-GRU with Attention Mechanism for Classification of 12-lead ECG | Link (248KB) | | 31 | Alba_W.O. | Marek Żyliński and Gerard Cybulski | Selected Features for Classification of 12-lead ECGs | Link (58MB) | | 32 | AI Strollers | Rohit Pardasani and Navchetan Awasthi | Classification of 12 Lead ECG Signal Using 1D-CNN With Class Dependent Threshold | Link (41MB) | | 33 | ECGLearner | Yingjing Feng and Edward Vigmond | Deep Multi-Label Multi-Instance Classification on 12-Lead ECG | Link (413KB) | | 34 | Leicester-Fox | Zheheng Jiang, Tiago Paggi de Almeida, Fernando Schlindwein, G. André Ng, Huiyu Zhou and Xin Li | Diagnostic of Multiple Cardiac Disorders from 12-lead ECGs Using Graph Convolutional Network Based Multi-label Classification | Link (39MB) | | 35 | deepzx987 | Deepankar Nankani, Pallabi Saikia and Rashmi Dutta Baruah | Automatic Concurrent Arrhythmia Classification Using Deep Residual Neural Networks | Link (555KB) | | 36 | CVC | Alexander William Wong, Weijie Sun, Sunil Vasu Kalmady, Padma Kaul and Abram Hindle | Multilabel 12-Lead Electrocardiogram Classification Using Gradient Boosting Tree Ensemble | Link (12MB) | | 37 | Cordi-Ak | Paul Samuel Ignacio, Jay-Anne Bulauan and John Rick Manzanares | A Topology Informed Random Forest Classifier for ECG Classification | Link (891KB) | | 38 | MIndS | Marwen Sallem, Adnen Saadaoui, Amina Ghrissi and Vicente Zarzoso | Detection of Cardiac Arrhythmias From Varied Length Multichannel Electrocardiogram Recordings Using Deep Convolutional Neural Networks | Link (47MB) | | 39 | easyG | Martin Baumgartner, Dieter Hayn, Andreas Ziegl, Alphons Eggerth and Günter Schreier | Multi-Stream Deep Neural Network for 12-Lead ECG Classification | Link (399KB) | | 40 | BiSP Lab | Matteo Bodini, Massimo W Rivolta and Roberto Sassi | Classification of 12-lead ECG with an Ensemble Machine Learning Approach | Link (340KB) | | 41 | Technion_AIMLAB | David Assaraf, Jeremy Levy, Janmajay Singh, Armand Chocron and Joachim A. Behar | Classification of 12-lead ECGs using digital biomarkers and representation learning | Link (474KB) |
Unranked submissions:
| Team name | Team member(s) | Paper | Code |
|---|---|---|---|
| AAIST | --- | --- | Link (8.3KB) |
| AImsterdam | --- | --- | Link (27MB) |
| BERCLAB UND | --- | --- | Link (12MB) |
| BME_Feng | --- | --- | Link (7.3MB) |
| BraveHeart400 | --- | --- | Link (147MB) |
| BRIC | --- | --- | Link (276KB) |
| Chapman | --- | --- | Link (24KB) |
| Connected_Health | --- | --- | Link (29KB) |
| Health team Szeged | --- | --- | Link (19KB) |
| IBMTpeakyFinders | --- | --- | Link (9.0MB) |
| Kimball_IRL | --- | --- | Link (27KB) |
| LaussenLabs | 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 | Rhythm classification of 12-lead ECGs using deep neural network and class-activation maps for improved explainability | Link (13MB) |
| LIST_AIHealthCare | --- | --- | Link (26KB) |
| Marquette | David Kaftan and Richard Povinelli | A Deep Neural Network and Reconstructed Phase Space Approach to Classifying 12-lead ECGs | Link (21MB) |
| Medics | --- | --- | Link (58KB) |
| MetaHeart | Zhengling He, Pengfei Zhang, Lirui Xu, Zhongrui Bai, Hao Zhang, Weisong Li, Pan Xia and Xianxiang Chen | A Novel Convolutional Neural Network for Arrhythmia Detection From 12-lead Electrocardiograms | Link (25MB) |
| Metformin-121 | --- | --- | Link (16KB) |
| ML Warriors | --- | --- | Link (3.6MB) |
| NACAS_12X | --- | --- | Link (100MB) |
| nebula | Wenjie Cai, Shuaicong Hu, Jingying Yang and Jianjian Cao | Automatic 12-lead ECG Classification Using Deep Neural Networks | Link (45MB) |
| NN-MIH | Naoki Nonaka and Jun Seita | Electrocardiogram Classification by Modified EfficientNet with Data Augmentation | Link (59MB) |
| NTU-Accesslab | Yu-Cheng Lin, Yun-Chieh Lee, Wen-Chiao Tsai, Win-Ken Beh and An-Yeu Wu | Explainable Deep Neural Network for Identifying Cardiac Abnormalities Using Class Activation Map | Link (339KB) |
| Orange Peel | --- | --- | Link (12KB) |
| SBU_AI | Ibrahim Hammoud, IV Ramakrishnan and Petar Djuric | Classification of 12-lead ECGs Using Intra-Heartbeat Discrete-time Fourier Transform and Inter-Heartbeat Attention | Link (13MB) |
| SpaceOn Flattop | Shan Yang, Heng Xiang, Qingda Kong and Chunli Wang | Multi-label Classification of Electrocardiogram With Modified Residual Networks | Link (364MB) |
| try again | --- | --- | Link (11KB) |
| UniA4Life | --- | --- | Link (80KB) |
The final scores for the 2020 PhysioNet/CinC Challenge are in the following tables:
- The official scores
- The unofficial scores
- The metrics per database of the official entries
- The per-class scoring metrics of the official entries on the validation data
We introduced a new scoring metric for this Challenge. We used this scoring metric to evaluate and rank the Challenge entries. We included several other metrics for reference. The area under the receiver operating characteristic (AUROC), area under the precision recall curve (AUPRC), and F-measure scores are the macro-average of the scores across all classes. The accuracy metric is the fraction of correctly diagnosed recordings, i.e., all classes for the recording are correct. These metrics were computed by the evaluate_12ECG_score.py script. Please see the script for more details of these scores.
We included the scores on the following datasets:
- Validation Set: Includes recordings from the hidden CPSC and G12EC sets.
- Hidden CPSC Set: Split between the validation and test sets.
- Hidden G12EC Set: Split between the validation and test sets.
- Hidden Undisclosed Set: All recordings were part of the test sets.
- Test Set: Includes recordings from the hidden CPSC, G12EC, and undisclosed test sets.
To refer to these tables in a publication, 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. 41 (2020). doi: 10.1088/1361-6579/abc960.
In these tables, you can find the following information:
- Official entries that were scored on the validation and test data and ranked in the Challenge: physionet_2020_official_scores.csv
- Unofficial entries that were scored on the validation and test data but unranked because they did not satisfy all of the rules or were unsuccessful on one or more of the test sets: physionet_2020_unofficial_scores.csv
- Challenge and other scoring metrics on all official entries broken with scores for each database in the validation and test data: physionet_2020_metrics_perDatabase_official_entries.csv
- Per-class scoring metrics on the validation data: physionet_2020_validation_metrics_by_class_official_entries.csv