Publications from AF Classification from a short single lead ECG recording: the PhysioNet/Computing in Cardiology Challenge 2017

The papers listed below were presented at Computers in Cardiology 2017. Please cite this publication when referencing any of these papers. These papers have been made available by their authors under the terms of the Creative Commons Attribution License 3.0 (CCAL). We wish to thank all of the authors for their contributions.

This paper is an introduction to the challenge topic, with a summary of the challenge results and a discussion of their implications:

Gari Clifford, Chengyu Liu, Benjamin Moody, Li-Wei Lehman, Ikaro Silva, Qiao Li, Alistair Johnson, Roger Mark. AF Classification from a Short Single Lead ECG Recording: the Physionet Computing in Cardiology Challenge 2017.

The remaining papers were presented by participants in the Challenge, who describe their approaches to the challenge problem.

Publications listed alphabetically by author

  1. Fernando Andreotti, Oliver Carr, Marco A F Pimentel, Adam Mahdi, Maarten De Vos. Comparing Feature Based Classifiers and Convolutional Neural Networks to Detect Arrhythmia from Short Segments of ECG.
  2. Joachim A. Behar, Aviv Rosenberg, Yael Yaniv, Julien Oster. Rhythm and Quality Classification from Short ECGs Recorded using a Mobile Device.
  3. Lucia Billeci, Franco Chiarugi, Magda Costi, David Lombardi, Maurizio Varanini. Detection of AF and Other Rhythms Using RR Variability and ECG Spectrum Measures.
  4. Guangyu Bin, Minggang Shao, Guanghong Bin, Jiao Huang, Dingchang Zheng, Shuicai Wu. Detection of Atrial Fibrillation Using Decision Tree Ensemble.
  5. Pietro Bonizzi, Kurt Driessens, Joel Karel. Detection of Atrial Fibrillation Episodes from Short Single Lead Recordings by Means of Ensemble Learning.
  6. Sandeep Chandra Bollepalli, S Sastry Challa, Soumya Jana, Shivnarayan Patidar. Atrial Fibrillation Detection Using Convolutional Neural Networks.
  7. Ivaylo Christov, Vessela Krasteva, Iana Simova, Tatyana Neycheva, Ramun Schmid. Multi-parametric Analysis for Atrial Fibrillation Classification in the ECG.
  8. Erin Coppola, Prashnna Gyawali, Nihar Vanjara, Daniel Giaime, Linwei Wang. Atrial Fibrillation Classification from a Short Single Lead ECG Recording Using Hierarchical Classifier.
  9. Matthieu Da Silva-Filarder, Faezeh Marzbanrad. Combining Template-based and Feature-based Classification to Detect Atrial Fibrillation from a Short Single Lead ECG Recording.
  10. Shreyasi Datta, Chetanya Puri, Ayan Mukherjee, Rohan Banerjee, Anirban Dutta Choudhury, Rituraj Singh, Arijit Ukil, Soma Bandyopadhyay, Arpan Pal, Sundeep Khandelwal. Identifying Normal, AF and other Abnormal ECG Rhythms using a Cascaded Binary Classifier.
  11. Manuel García, Juan Ródenas, Raul Alcaraz, José J Rieta. Atrial Fibrillation Screening through Combined Timing Features of Short Single-Lead Electrocardiograms.
  12. Shadi Ghiasi, Mostafa Abdollahpur, nasimalsadat madani, kamran kiyani, ali ghaffari. Atrial Fibrillation Detection Using Feature Based Algorithm and Deep Conventional Neural Network.
  13. Vadim Gliner, Yael Yaniv. Identification of Features for Machine Learning Analysis for Automatic Arrhythmogenic Event Classification.
  14. Andrew Goodwin, Sebastian Goodfellow, Danny Eytan, Robert Greer, Mjaye Mazwi, Peter Laussen, Sebastian Goodfellow. Classification of Atrial Fibrillation Using Multidisciplinary Features and Gradient Boosting.
  15. Shenda Hong, Meng Wu, Yuxi Zhou, Qingyun Wang, Junyuan Shang, Hongyan Li, Junqing Xie. ENCASE: an ENsemble ClASsifiEr for ECG Classification Using Expert Features and Deep Neural Networks.
  16. Christoph Hoog Antink, Steffen Leonhardt, Marian Walter. Fusing QRS Detection, Waveform Features, and Robust Interval Estimation with a Random Forest to Classify Atrial Fibrillation.
  17. Irena Jekova, Todor Stoyanov, Ivan Dotsinsky. Arrhythmia Classification via Time and Frequency Domain Analyses of Ventricular and Atrial Contractions.
  18. Santiago Jiménez-Serrano, Jaime Yagüe-Mayans, Elena Simarro-Mondejar, Conrado J. Calvo, Francisco Castells, José Millet Roig. Atrial Fibrillation Detection Using Feedforward Neural Networks and Automatically Extracted Signal Features.
  19. Martin Kropf, Dieter Hayn, Günter Schreier. ECG Classification Based on Time and Frequency Domain Features Using Random Forrests.
  20. Mohamed Limam, Frederic Precioso. AF Detection and ECG Classification based on Convolutional Recurrent Neural Network.
  21. Chengyu Liu, Qiao Li, Pradyumna B Suresha, Gari Clifford. Combining Multi-source Features and Support Vector Machine for Heart Rhythm Classification.
  22. Yang Liu, Kuanquan Wang, Qince Li, Runnan He, Yong Xia, Zhen Li, Hao Liu, Henggui Zhang. Diagnosis of AF Based on Time and Frequency Features by using a Hierarchical Classifier.
  23. Octavian Lucian Hasna, Rodica Potolea. Robust Feature Extraction from Noisy ECG for Atrial Fibrillation Detection.
  24. Ruhi Mahajan, Rishikesan Kamaleswaran, John Andrew Howe, Oguz Akbilgic. Cardiac Rhythm Classification from a Short Single Lead ECG Recording via Random Forest.
  25. Vykintas Maknickas, Algirdas Maknickas. Atrial Fibrillation Classification Using QRS Complex Features and LSTM.
  26. Saman Parvaneh, Jonathan Rubin, Rahman Asif, Bryan Conroy, Saeed Babaeizadeh. Densely Connected Convolutional Networks and Signal Quality Analysis to Detect Atrial Fibrillation Using Short Single-Lead ECG Recordings.
  27. Shivnarayan Patidar, Ashish Sharma, Niranjan Garg. Automated Detection of Atrial Fbrillation using Fourier-Bessel expansion and Teager Energy Operator from Electrocardiogram Signals.
  28. Filip Plesinger, Petr Nejedly, Ivo Viscor, Josef Halamek, Pavel Jurak. Automatic Detection of Atrial Fibrillation and Other Arrhythmias in Holter ECG Recordings using PQRS Morphology and Rhythm Features.
  29. Patrick Schwab, Gaetano Claudio Scebba, Jia Zhang, Marco Delai, Walter Karlen. Beat by Beat: Classifying Cardiac Arrhythmias with Recurrent Neural Networks.
  30. Radovan Smíšek, Jakub Hejc, Marina Ronzhina, Andrea Nemcová, Lucie Maršánová, Jirí Chmelík, Jana Kolárová, Ivo Provazník, Lukáš Smital, Martin Vítek. SVM Based ECG Classification Using Rhythm and Morphology Features, Cluster Analysis and Multilevel Noise Estimation.
  31. Dawid Smolen. Atrial Fibrillation Detection Using Boosting and Stacking Ensemble.
  32. Dionisije Sopic, Elisabetta De Giovanni, Amir Aminifar, David Atienza. A Hierarchical Cardiac Rhythm Classification Methodology Based on Electrocardiogram Fiducial Points.
  33. Katarzyna Stepien, Iga Grzegorczyk. Classification of ECG Recordings with Neural Networks Based on Specific Morphological Features and Regularity of the Signal.
  34. Tomas Teijeiro, Constantino A. Garcia, Daniel Castro, Paulo Félix. Arrhythmia Classification from the Abductive Interpretation of Short Single-Lead ECG Records.
  35. Maria Tziakouri, Costas Pitris, Christina Orphanidou. Identification of AF and Other Cardiac Arrhythmias from a Single-lead ECG Using Dynamic Time Warping.
  36. Heikki Väänänen, Jarno Mäkelä. Electrocardiogram Classification -- a Human Expert Way.
  37. Marcus Vollmer, Philipp Sodmann, Leonard Caanitz, Neetika Nath, Lars Kaderali. Can Supervised Learning Be Used to Classify Cardiac Rhythms?.
  38. Philip Warrick, Masun Nabhan Homsi. Cardiac Arrhythmia Detection from ECG Combining Convolutional and Long Short-Term Memory Networks.
  39. Bradley Whitaker, Muhammed Rizwan, Burak Aydemir, James Rehg, David Anderson. AF Classification from ECG Recording Using Feature Ensemble and Sparse Coding.
  40. Zhaohan Xiong, Martin Stiles, Jichao Zhao. Robust ECG Signal Classification for the Detection of Atrial Fibrillation Using Novel Neural Networks.
  41. Sasan Yazdani, Priscille Laub, Adrian Luca, Jean-Marc Vesin. Heart Rhythm Classification using Short-term ECG Atrial and Ventricular Activity Analysis.
  42. Morteza Zabihi, Ali Bahrami Rad, Aggelos K. Katsaggelos, Serkan Kiranyaz, Susanna Narkilahti, Moncef Gabbouj. Detection of Atrial Fibrillation in ECG Hand-held Devices Using a Random Forest Classifier.
  43. Martin Zihlmann, Dmytro Perekrestenko, Michael Tschannen. Convolutional Recurrent Neural Networks for Electrocardiogram Classification.

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Updated Monday, 15 October 2018 at 21:27 CEST

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