Predicting Neurological Recovery from Coma After Cardiac Arrest: The George B. Moody PhysioNet Challenge 2023

Citations

We ask teams to cite the description and data papers for the Challenge to ensure that your readers can better understand the context for your approaches.

For this year’s Challenge, please cite the Challenge description paper:

Reyna MA*, Amorim E*, Sameni S, Weigle J, Elola A, Bahrami Rad A, Seyedi S, Kwon H, Zheng WL, Ghassemi M, van Putten MJAM, Hofmeijer J, Gaspard N, Sivaraju A, Herman ST, Lee JW, Westover MB**, Clifford GD**. Predicting Neurological Recovery from Coma After Cardiac Arrest: The George B. Moody PhysioNet Challenge 2023. Computing in Cardiology 2023; 50: 1-4.

For this year’s Challenge, please cite the I-CARE Database publication:

Amorim E, Zheng WL, Ghassemi MM, Aghaeeaval M, Kandhare P, Karukonda V, Lee JW, Herman ST, Adithya S, Gaspard N, Hofmeijer J, van Putten MJAM, Sameni R, Reyna MA, Clifford GD, Westover MB. The International Cardiac Arrest Research (I-CARE) Consortium Electroencephalography Database. Critical Care Medicine; Oct. 2023; doi:10.1097/CCM.0000000000006074.

Additionally, please include the standard citation for PhysioNet:

Goldberger AL, Amaral LA, Glass L, Hausdorff JM, Ivanov PC, Mark RG, Mietus JE, Moody GB, Peng CK, Stanley HE. PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation; June 2023; 101(23): e215-e220.

Preparing Your Paper

Please follow the preparation and submission instructions for your CinC papers.

We have prepared templates to help you prepare your CinC paper. Please use the LaTeX template (Overleaf or download) or the Word template. These templates include important instructions, advice, and references. Please use this checklist to check your paper.

Please read and follow one of the templates. Each year, we need to provide feedback for many teams that miss some of the paper requirements, and each year, some papers are unfortunately rejected because the teams were unable to address important issues that the conference required.

Conference Papers and Code

The conference papers for Computing in Cardiology 2023 are available on the CinC and IEEE websites.

Number Team Name Paper Author(s) Code
14 MetaHeart YNNU Predicting Neurological Outcomes for Cardiac Arrest Patients from Long-Term EEG Based on Convolutional Neural Networks and Multi-Scale Transformer Pan Xia, Dongfang Zhao, Yicheng Yao, Zhongrui Bai, Yizi Shao, Saihu Lu, Fanglin Geng, Yusi Zhu, Peng Wang, Lidong Du posted soon
15 TUD EEG Model Ensembling for Predicting Neurological Recovery after Cardiac Arrest: Top-down or Bottom-up? Hongliu Yang, Ronald Tetzlaff posted soon
23 Team KU Random Forest and Attention-Based Networks in Quantifying Neurological Recovery Mostafa Moussa, Hessa Alfalahi, Mohanad Alkhodari, Leontios Hadjileontiadis, Ahsan Khandoker posted soon
35 The BEEGees Multimodal deep learning approach to predicting neurological recovery from coma after cardiac arrest Felix Krones, Ben Walker, Guy Parsons, Terry Lyons, Adam Mahdi posted soon
40 UCASFighters Predicting Neurological Recovery After Cardiac Arrest from Electroencephalogram using Residual Network and Random Forest Beibei Wang, Hao Zhang, Mengxue Yan, Lirui Xu, Haonan Zhao, Jianqiang Liu, Jihang Xue, Zhen Fang posted soon
44 UnivPittsburgh Predicting Recovery From Coma Following Cardiac Arrest With a Reduced Set of EEG Channels Nathan T Riek, Jonathan Elmer, Salah Al-Zaiti, Murat Akcakaya posted soon
48 EEG pz lmn sqz Prediction Comatose Patient Outcomes Using Deep learning-based Analysis of EEG Power Spectral Density Kyungmin Choi, Gi-Won Yoon, Sanghoon Choi and Hyeon-Hwa Choi, Segyeong Joo posted soon
49 BJUTbme Predicting Neurological Recovery Following Coma After Cardiac Arrest Using the R(2+1)D Network Based on EEG Signals Meng Gao, Rui Yu, Zhuhuang Zhou, Shuicai Wu, Guangyu Bin posted soon
54 ISIBrno-AIMT Using Embedding Extractor and Transformer Encoder for Predicting Neurological Recovery from Coma After Cardiac Arrest Jan Pavlus, Kristyna Pijackova, Zuzana Koscova, Radovan Smisek, Ivo Viscor, Vojtech Travnicek, Petr Nejedly, Filip Plesinger posted soon
57 ibmtPeakyFinders Assessing Brain Dynamics for Predicting Postanoxic Coma Recovery: An EEG Based Approach Marc Goettling, Richard Hohmuth, Franz Ehrlich, Hannes Ernst, Alexander Hammer, Matthieu Scherpf, Martin Schmidt posted soon
60 Revenger Predicting Neurological Recovery from Coma with Longitudinal Electroencephalogram Using Deep Neural Networks Jingsu Kang, Hao Wen posted soon
77 ComaToss Predicting Neurological Outcome After Cardiac Arrest Using a Pretrained Model with Electroencephalography Augmentation Dong-Kyu Kim, Hong-Cheol Yoon, Hyun-Seok Kim, Woo-Young Seo, Sung-Hoon Kim posted soon
84 MIWEAR MelicientNet: Harnessing Mel-Spectrograms and EfficientNet Architectures for Predicting Neurological Recovery Post-Cardiac Arrest Wenlong Wu, Ying Tan posted soon
88 Aircas A Method with Time-sensitive Features for The Automated Prognosis Prediction of Cardiac Arrest Patients Based on EEG Siying Li, Yonggang Zou, Xianya Yu, Xiuying Mou, Yueqi Li, Bokai Huang, Changyu Liu, Xianxiang Chen posted soon
93 ZIB Visual Predicting Coma Recovery After Cardiac Arrest With Residual Neural Networks Kuba Weimann, Tim O. F. Conrad posted soon
96 Cerenion Computationally Efficient Early Prognosis of the Outcome of Comatose Cardiac Arrest Survivors Using Slow-Wave Activity Features in EEG Miikka Salminen, Juha Partala, Eero Väyrynen, Jukka Kortelainen posted soon
99 CQUPT FP mana MMCTNet: Multi-Modal Conv-Transformer Network for Predicting Good and Poor Outcomes in Cardiac Arrest Patients Xiuli Bi, Shizhan Tang, Zonglin Yang, Xin Deng, Bin Xiao, Pietro Liò posted soon
102 FMMGroup UVa Heart attack outcome predictions using FMM models C. Canedo, A. Fernàndez-Santamónica, Y. Larriba, I. Fernàndez and C. Rueda posted soon
125 Oldenburg Predicting Recovery from Coma After Cardiac Arrest Using Low-level Features from EEG Recordings and a Small-sized LSTM Network Benjamin Cauchi, Marco Eichelberg, Andreas Hein posted soon
133 EEG Attackers MemoryInception: Predicting Neurological Recovery from EEG using Recurrent Inceptions Bjørn-Jostein Singstad, Jesper Ravn, Arian Ranjbar posted soon
142 AIrhythm HyperEnsemble Learning from Multimodal Biosignals to Robustly Predict Functional Outcome after Cardiac Arrest Morteza Zabihi, Alireza Chaman Zar, Pulkit Grover, Eric S. Rosenthal posted soon
144 AHU lab A Neurological Recovery Prediction Algorithm based on Multi-Feature Extraction and Bagging Aggregation Ke Jiang, Zirui Wang, Runze Shen, Sibo Wang, Yang Liu, Yizhuo Feng, Xiaohe Lisun, Zhenfeng Li posted soon
165 AIMED Developing a Machine Learning Pipeline for Predicting Neurological Outcomes in Comatose Cardiac Arrest Survivors Using Continuous EEG Data Quenaz Soares, Felipe M Dias, Estela Ribeiro, Jose E Krieger and Marco A Gutierrez posted soon
173 IWillSurvive Transformer Network with Time Prior for Predicting Clinical Outcome from EEG of Cardiac Arrest Patients Maurice Rohr, Tobias Schilke, Laurent Willems, Christoph Reich, Sebastian Dill, Gökhan Güney, Christoph Hoog Antink posted soon
181 SHE Lab A Temporal-Spectral Based Single-lead Electroencephalogram Feature Fusion Network may Provide Potential Clinical Biomarker for Cardiac Arrest Zhaoyang Cong, Minghui Zhao, Li Ling, Feifei Chen, Lukai Pang, Keming Cao, Jianqing Li, Chengyu Liu posted soon
183 PKU NIHDS Less is More: Reducing Overfitting in Deep Learning for EEG Classification Songchi Zhou, Shijia Geng, Jun Li, Deyun Zhang, Ziqian Xie, Chuandong Cheng, Shenda Hong posted soon
188 RPG IISC Deep-Learning-Assisted Prediction of Neurological Recovery from Coma After Cardiac Arrest Vasanth Kumar Babu, Navneet Roshan, Rahul Pandit posted soon
210 BrAInstorm Fusion of Features with Neural Networks for Prediction of Secondary Neurological Outcome After Cardiac Arrest Philip Hempel, Philip Zaschke, Miriam Goldammer, Nicolai Spicher posted soon
217 UoM EEE Autoencoder Artefact Removal for Brain Signals and Impact on Classification Performance Mengyao Li, Le Xing, Alexander J. Casson posted soon
236 am vision Frequency and Time Domain EEG Analysis for Prognostication of Postanoxic Comatose Patients Subhash Khambampati, Sushanth Reddy Dondapati, Chaithanya Kalyan Reddy Bhuma, Bharadwaj Madiraju, Rahul Krishnan Pathinarupothi posted soon
238 unimi bisp squad Recovery from Coma after Cardiac Arrest: Which Time-Window Counts the Most for Deep Learning Predictions? Filippo Uslenghi, Roberto Sassi, Massimo W. Rivolta posted soon
251 OUS IVS A Multi-channel EEG Data Analysis for Poor Neuro-prognostication in Comatose Patients with Self and Cross-channel Attention Mechanism Hemin Ali Qadir, Naimahmed Nesaragi, Per Steiner Halvorsen, Ilangko Balasingham posted soon
255 ComaToast A Machine Learning Approach for Outcome Prediction in Postanoxic Coma Patients Using Frequency Domain Features Vijay Vignesh Venkataramani, Akshit Garg, Maitreya Maity, U Deva Priyakumar posted soon
260 Swarthbeat An Optimization Approach to EEG Feature Extraction for the Prediction of Neurological Outcome Allan R. Moser, Jackie T. Le, Lys K. P. Kang posted soon
278 HeartsAndMinds Variational Autoencoders for Electroencephalogram Feature Extraction in Patients with Coma after Cardiac Arrest Adel Hassan, Liam Ferreira posted soon
297 XGBoost Combining Complementary Models: Fusing CNNs, RNNs, and XGBoost for Enhanced Outcome Prediction of Comatose Patients after Heart Attack Shuaixun Wang, Siyi Liu, Martyn G Boutelle posted soon
308 BrownBAI Leveraging Unlabeled Electroencephalographic Data to Predict Neurological Recovery for Comatose Patients Following Cardiac Arrest Isaac Sears, Augusto Garcia-Agundez, George Zerveas, William Rudman, Laura Mercurio, Corey E. Ventetuolo, Adeel Abbasi, Carsten Eickhoff posted soon
311 USYD BrainBuzz Time-Embedded EEG Sequence Learning for Comatose Patients’ Prognosis Simanto Saha, Raquib-ul Alam, Andrea Samore, Andrew Goodwin, Michael Loong-Siong Wong, Alistair McEwan, Collin Anderson posted soon
312 UFC MDCC Exploring EEG Signal Features for Predicting Post Cardiac Arrest Prognosis Antonio G. C. Santos, Joao A. L. Marques, Luís O. Rigo Jr., João P. V. Madeiro posted soon
314 Medics A tensor decomposition-based feature extraction method to predict neurological recovery from coma after cardiac arrest using EEG signals Shivnarayan Patidar, Nidhi Kalidas Sawant posted soon
324 UPFantastic EEG-Based Cardiac Arrest Outcome Estimation with Highly Interpretable Features Alvaro José Bocanegra, Anaïs Espinoso, Ralph G. Andrzejak, Oscar Camara posted soon
364 BioITACA UPV 3D CNN as an Approach to Predict the Cerebral Performance of Comatose Patients Rafael Teodoro Ors-Quixal, Elisa Ramïrez-Candela, Samuel Ruipérez-Campillo, Francisco Castells-Ramón, José Millet-Roig posted soon
372 DEIB POLIMI Predicting Comatose Patient’s Outcome Using Brain Functional Connectivity with a Random Forest Model Inês W Sampaio, Matteo Leccardi, Cristian Drudi, Jiaying Liu, Francesca Righetti, Anna M Bianchi, Riccardo Barbieri, Luca Mainardi posted soon
394 Leicester Fox Predicting Cardiac Arrest Recovery with Shallow and Deep Learning Models Ekenedirichukwu Obianom, Marko Mäkynen, Noor Qaqos, Shamsu Idris Abdullahi, Fernando S Schlindwein, G André Ng, Xin Li posted soon
422 EEGnition Functional Outcome Prediction After Cardiac Arrest Using Machine Learning and Network Dynamics of Resting-State Electroencephalography Charlotte Maschke, Kira Dolhan, Beatrice P. De Koninck, Miriam Han, Stefanie Blain-Moraes posted soon
442 Blue and Gold A Dynamical Systems Approach to Predicting Patient Outcome after Cardiac Arrest Richard J Povinelli, Mathew Dupont posted soon
457 WesternUni Prediction of Functional Recovery Post-Cardiac Arrest Using an Ensemble of Extreme Gradient-Boosted Trees Matthew Kolisnyk, Xiaoyu Wang, Chao Guo, Shigeng Xie, Karnig Kazazian, Loretta Norton, Teneille Gofton, Saptharishi Lalgudi Ganesan, Adrian M. Owen, Derek Debicki posted soon
458 FINDING MEMO Predicting Neurological Outcomes of Comatose Cardiac Arrest Patients Using Tnsformer Neural Networks with EEG Data Jefferson Dionisio, Che Lin, Lian-Yu Lin, Wen-Chau Wu posted soon

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

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