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 number R01EB030362.

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