George B. Moody PhysioNet Challenge

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Quick links for this year's Challenge:

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Will Two Do? Varying Dimensions in Electrocardiography: The PhysioNet/Computing in Cardiology Challenge 2021

Citations

For this year’s Challenge, please cite:

Reyna MA, Sadr N, Perez Alday EA, Gu A, Shah AJ, Robichaux C, Rad AB, Elola A, Seyedi S, Ansari S, Ghanbari H, Li Q, Sharma A, Clifford GD. Will Two Do? Varying Dimensions in Electrocardiography: The PhysioNet/Computing in Cardiology Challenge 2021. Computing in Cardiology 2021; 48: 1-4

Reyna MA, Sadr N, Perez Alday EA, Gu Annie, Shah AJ, Robichaux C, Rad AB, Elola A, Seyedi S, Ansari S, Ghanbari H, Li Q, Sharma A, Clifford GD. Issues in the automated classification of multilead ECGs using heterogeneous labels and populations. Physiol. Meas. 2022; 43(8): 084001

Focus Issue Papers

The Physiological Measurement Focus Collection: Classification of Multilead ECGs includes the above papers from 2020 and 2021.

Conference Papers

The conference papers for Computing in Cardiology 2021 are available on the CinC and IEEE websites.

Abstract Team Name Title Author(s)
9 Gio_new_img 3-D ECG images with Deep Learning Approach for Identification of Cardiac Abnormalities from a Variable Number of Leads giovanni bortolan
13 CQUPT_Dontwant A Branched Deep Neural Network for End-to-end Classification from ECGs with Varying Dimensions Han Duan, Junchao Fan, Junhui Zhang, Bin Xiao, Xiuli Bi and Xu Ma
14 ISIBrno-AIMT Classification of ECG using Ensemble of Residual CNNs with Attention Mechanism Petr Nejedly, Adam Ivora, Ivo Viscor, Zuzana Koscova, Radovan Smisek, Pavel Jurak and Filip Plesinger
16 DSC Pathologies Prediction on Short ECG Signals with Focus on Feature Extraction Based on Beat Morphology and Image Deformation Jeffrey Prehn, Svetoslav Ivanov and Georgi Nalbantov
17 Revenger Hybrid Arrhythmia Detection on Varying-Dimensional Electrocardiography: Combining Deep Neural Networks and Clinical Rules Hao Wen and Jingsu Kang
19 snu_adsl Learning ECG Representations for Multi-Label Classification of Cardiac Abnormalities Jangwon Suh, Jimyeong Kim, Eunjung Lee, Jaeill Kim, Duhun Hwang, Jungwon Park, Junghoon Lee, Jaeseung Park, Seo-Yoon Moon, Yeonsu Kim, Min Kang, Soonil Kwon, Eue-Keun Choi and Wonjong Rhee
21 Sunset Using Mel-frequency Cepstrum and Amplitude-time Heart Variability as XGBoost Handcrafted Features for Heart Disease Detection Sergey Krivenko, Anatolii Pulavskyi, Liudmyla Kryvenko, Olha Krylova and Stanislav Krivenko
24 SMS+1 Two will do: Convolutional Neural Network with Asymmetric Loss and Self-Learning Label Correction for Imbalanced Multi-Label ECG Data Classification Cristina Gallego Vázquez, Alexander Breuss, Oriella Gnarra, Julian Portmann and Giulia Da Poian
26 prna Convolution-Free Waveform Transformers for Multi-Lead ECG Classification Annamalai Natarajan, Gregory Boverman, Yale Chang, Corneliu Antonescu and Jonathan Rubin
33 METU-19 Two Might Do: A Beat-by-Beat Classification of Cardiac Abnormalities using Deep Learning with Domain-Specific Features Berken Utku Demirel, Adnan Harun Dogan and Mohammad Abdullah Al Faruque
35 Proton 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, Jiayi Yan, Fan Wu, Huaiyu Zhu and Yun Pan
38 Care4MyHeart ResNet-BiLSTM Network Activations Mohanad Alkhodari, Georgios Apostolidis, Charilaos Zisou, Leontios Hadjileontiadis and Ahsan Khandoker
39 csu_anying MTFNet: A Morphological and Temporal Features Network for multiple leads ECG Classification Lebing Pan, Weibai Pan, Mengxue Li, Yuxia Guan and Ying An
46 iadi-ecg Cardiac Abnormality Detection based on an Ensemble Voting of Single-Lead Classifier Predictions Pierre Aublin, Julien Oster, Mouin Ben Ammar, Jérémy Fix and Michel Barret
47 BUTTeam Cardiac Abnormalities Recognition in ECG Using a Convolutional Network with Attention and Input with an Adaptable Number of Leads Tomas Vicar, Petra Novotna, Jakub Hejc, Oto Janousek and Marina Ronzhina
51 matFCT Semi-supervised Learning for ECG Classification Rui Rodrigues and Paula Couto
54 WEAIT N-BEATS for Heart Disfunction Classification Bartosz Puszkarski, Krzysztof Hryniów, and Grzegorz Sarwas
55 N/A Improving Machine Learning Education during the COVID-Pandemic using past Computing in Cardiology Challenges Maurice Rohr, Filip Plesinger, Veronika Bulkova and Christoph Hoog Antink
59 LINC Automatic Diagnosis of Cardiac Disease from Twelve-lead and Reduced-lead ECGs using Multi-label Classification Prathic Sundararajan, Kevin Moses, Cristhian Potes and Saman Parvaneh
63 HeartBeats Multi-Label Cardiac Abnormalities Classification on Selected Leads of ECG Signals Zhuoyang Xu, Yangming Guo, Tingting Zhao, Zhuo Liu and Xingzhi Sun
67 UIDT_UNAM Cardiac Anomalies Detection Through 2D-CNN and ECG Spectrograms Jonathan Roberto Torres Castillo and Miguel Padilla Castañeda
75 CardiOUS Multi-label ECG classification using Convolutional Neural Networks in a Classifier Chain Bjørn-Jostein Singstad, Eraraya Muten and Pål Brekke
76 HaoWan_AIeC A Mixed-Domain Self-Attention Network for Multilabel Cardiac Irregularity Classification Using Reduced-Lead Electrocardiogram Hao-Chun Yang, Wan-Ting Hsieh and Trista Pei-Chun Chen
78 CeZIS A Two-Phase Multilabel ECG Classification Using One-Dimensional Convolutional Neural Network and Modified Labels Peter Bugata, Peter Bugata Jr., Vladimira Kmecova, Monika Stankova, David Gajdos, David Hudak, Richard Stana, Simon Horvat, Lubomir Antoni, Gabriela Vozarikova, Erik Bruoth and Alexander Szabari
79 ami_kagoshima Reduced-Lead ECG Classifier Model Trained with DivideMix and Model Ensemble Hiroshi Seki, Takashi Nakano, Koshiro Ikeda, Shinji Hirooka, Takaaki Kawasaki, Mitsutomo Yamada, Shumpei Saito, Toshitaka Yamakawa, Shimpei Ogawa
80 DSAIL_SNU Towards High Generalization Performance on Electrocardiogram Classification Hyeongrok Han, Seongjae Park, Seonwoo Min, Hyun-Soo Choi, Eunji Kim, Hyunki Kim, Sangha Park, Jin-Kook Kim, Junsang Park, Junho An, Kwnanglo Lee, Wonsun Jeong, Sangil Chon, Kwonwoo Ha, Myungkyu Han and Sungroh Yoon
95 AI_Healthcare Deep Discriminative Domain Generalization with Adversarial Feature Learning for Classifying ECG Signals Zuogang Shang, Zhibin Zhao, Hui Fang, Samuel Relton, Darcy Murphy, Zoe Hancox, Ruqiang Yan and David Wong
98 AIRCAS_MEL1 A Novel Multi-Scale Convolutional Neural Network for Arrhythmia Classification on Reduced-lead ECGs Pan Xia, Zhengling He, Yusi Zhu, Zhongrui Bai, Xianya Yu, Yuqi Wang, Fanglin Geng, Lidong Du, Xianxiang Chen, Peng Wang, Zhen Fang
105 USST_Med Classifying Different Dimensional ECGs Using Deep Residual Convolutional Neural Networks Wenjie Cai, Fanli Liu, Xuan Wang, Bolin Xu, Yaohui Wang
109 itaca-UPV Multiple Cardiac Disease Detection from Minimal-Lead ECG Combining Feedforward Neural Networks with a One-vs-Rest Approach Santiago Jiménez-Serrano, Miguel Rodrigo, Conrado J. Calvo, José Millet Roig and Francisco Castells
122 DataLA_NUS Robust and Task-Aware Training of Deep Residual Networks for Varying-Lead ECG Classification Hansheng Ren, Miao Xiong and Bryan Hooi
129 HeartlyAI Rethinking ECG Classification with Neural Networks as a Sequence-to-Sequence Task Philipp Sodmann, Marcus Vollmer and Lars Kaderali
130 heartMAASters Multi-Label Classification on 12, 6, 4, 3 and 2 Lead Electrocardiography Signals Using Convolutional Recurrent Neural Networks Niels Osnabrugge, Kata Keresztesi, Felix Rustemeyer, Christos Kaparakis, Francesca Battipaglia, Pietro Bonizzi and Joel Karel
132 Biomedic2ai Detecting Cardiac Abnormalities with Multi-Lead ECG Signals: A Modular Network Approach Ryan Clark, Mohammadreza Heydarian, Kashif Siddiqui, Sajjad Rashidiani, Md Asif Khan and Thomas E Doyle
136 UoB_HBC An InceptionTime-Inspired Convolutional Neural Network to Detect Cardiac Abnormalities in Reduced-Lead ECG Data Harry Crocker and Aaron Costall
142 CardioIQ An Ensemble Learning Approach to Detect Cardiac Abnormalities in ECG Data Irrespective of Lead Availability Tim Uhlemann, Sebastian Wegener, Joshua Prim, Nils Gumpfer, Dimitri Grün, Jennifer Hannig, Till Keller and Michael Guckert
143 Polimi_1 Ensemble Learning of Modified Residual Networks for Classifying ECG with Different Set of Leads Federico M Muscato, Valentina D A Corino and Luca T Mainardi
155 Medics Diagnosis of Cardiac Abnormalities Applying Scattering Transform and Fourier-Bessel Expansion on ECG Signals Nidhi Kalidas Sawant and Shivnarayan Patidar
163 Eagles Reduced-Lead Electrocardiogram Classification using Wavelet Analysis and Deep Learning Adrian K Cornely, Alondra Carrillo and Grace Mirsky
170 CinCSEM Generative Pre-Trained Transformer for Cardiac Abnormality Detection Pierre Louis Gaudilliere, Halla Sigurthorsdottir, Clémentine Aguet, Jérôme Van Zaen, Mathieu Lemay and Ricard Delgado-Gonzalo
171 UMCU Automated Diagnosis of Reduced-lead Electrocardiograms using a Shared Classifier Hidde Jessen, Rutger van de Leur, Pieter Doevendans and Rene van Es
192 BiSP_Lab Classification of ECG Signals with Different Lead Systems Using AutoML Matteo Bodini, Massimo W Rivolta and Roberto Sassi
194 Leicester-Fox Spatio-temporal ECG Network for Detecting Cardiac Disorders from Multi-lead ECGs Long Chen, Zheheng Jiang, Tiago P. Almeida, Fernando S. Schlindwein, Jakevir S. Shoker, G. Andre Ng, Huiyu Zhou and Xin Li
196 AADAConglomerate 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, Klaus Steffen Leonhardt1 and Christoph Hoog Antink
198 easyG ECG Classification Combining Conventional Signal Analysis, Random Forests and Neural Networks – a Stacked Learning Scheme Martin Baumgartner, Martin Kropf, Lukas Haider, Sai Veeranki, Dieter Hayn and Günter Schreier
210 BitScattered Arrhythmia Classification of Reduced-Lead Electrocardiograms by Scattering-Recurrent Networks Philip A. Warrick, Vincent Lostanlen, Michael Eickenberg, Masun Nabhan Homsi, Adrian Campoy Rodriguez and Joakim Anden
212 Dr_Cubic Towards Generalization of Cardiac Abnormality Classification Using ECG Signal Xiaoyu Li, Chen Li, Xian Xu, Yuhua Wei, Jishang Wei, Yuyao Sun, Buyue Qian and Xiao Xu
213 ibmtPeakyFinders Automatic Classification of 12-, 6-, 4-, 3-, and 2-Lead Electrocardiograms Using Morphological Feature Extraction Alexander Hammer, Matthieu Scherpf, Hannes Ernst, Jonas Weiß, Daniel Schwensow and Martin Schmidt
231 skylark Incorporating Demographic and Heartbeat Features with Multichannel ECG for Cardiac Abnormality Detection using Parallel CNN and GAP Network Deepankar Nankani and Rashmi Dutta Baruah
234 cardiochallenger Channel Self-Attention Deep Learning Framework for Multi-Cardiac Abnormality Diagnosis from Varied-Lead ECG Signals Apoorva Srivastava, Ajith Hari, Sawon Pratiher, sazedul alam, Nirmalya Ghosh, Nilanjan Banerjee and Amit Patra
245 PhysioNauts Combining a ResNet Model with Handcrafted Temporal Features for ECG Classification with Varying Number of Leads Stefano Magni, Andrea Sansonetti, Chiara Salvi, Tiziana Tabiadon and Guadalupe García Isla
285 Cordi-Ak Leveraging Period-specific Variations in ECG Topology for Classification Tasks Paul Samuel Ignacio
352 NIMA Multi-label Cardiac Abnormality Classification from Electrocardiogram using Deep Convolutional Neural Networks Nima L Wickramasinghe and Mohamed Athif

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

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