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

5-1: Challenge I

  1. AF Classification from a Short Single Lead ECG Recording: the Physionet Computing in Cardiology Challenge 2017. Gari Clifford, Chengyu Liu, Benjamin Moody, Li-Wei Lehman, Ikaro Silva, Qiao Li, Alistair Johnson, Roger Mark.
  2. Robust ECG Signal Classification for the Detection of Atrial Fibrillation Using Novel Neural Networks. Zhaohan Xiong, Martin Stiles, Jichao Zhao.
  3. Heart Rhythm Classification using Short-term ECG Atrial and Ventricular Activity Analysis. Sasan Yazdani, Priscille Laub, Adrian Luca, Jean-Marc Vesin.
  4. Atrial Fibrillation Detection Using Boosting and Stacking Ensemble. Dawid Smolen.
  5. Detection of Atrial Fibrillation in ECG Hand-held Devices Using a Random Forest Classifier. Morteza Zabihi, Ali Bahrami Rad, Aggelos K. Katsaggelos, Serkan Kiranyaz, Susanna Narkilahti, Moncef Gabbouj.
  6. Convolutional Recurrent Neural Networks for Electrocardiogram Classification. Martin Zihlmann, Dmytro Perekrestenko, Michael Tschannen.

6-9: Challenge Posters I

  1. Atrial Fibrillation Detection Using Feature Based Algorithm and Deep Conventional Neural Network. Shadi Ghiasi, Mostafa Abdollahpur, nasimalsadat madani, kamran kiyani, ali ghaffari.
  2. Densely Connected Convolutional Networks and Signal Quality Analysis to Detect Atrial Fibrillation Using Short Single-Lead ECG Recordings. Saman Parvaneh, Jonathan Rubin, Rahman Asif, Bryan Conroy, Saeed Babaeizadeh.
  3. Cardiac Arrhythmia Detection from ECG Combining Convolutional and Long Short-Term Memory Networks. Philip Warrick, Masun Nabhan Homsi.
  4. Combining Multi-source Features and Support Vector Machine for Heart Rhythm Classification. Chengyu Liu, Qiao Li, Pradyumna B Suresha, Gari Clifford.
  5. Atrial Fibrillation Detection Using Convolutional Neural Networks. Sandeep Chandra Bollepalli, S Sastry Challa, Soumya Jana, Shivnarayan Patidar.
  6. Robust Feature Extraction from Noisy ECG for Atrial Fibrillation Detection. Octavian Lucian Hasna, Rodica Potolea.
  7. Rhythm and Quality Classification from Short ECGs Recorded using a Mobile Device. Joachim A. Behar, Aviv Rosenberg, Yael Yaniv, Julien Oster.
  8. Arrhythmia Classification from the Abductive Interpretation of Short Single-Lead ECG Records. Tomas Teijeiro, Constantino A. Garcia, Daniel Castro, Paulo Félix.
  9. Fusing QRS Detection, Waveform Features, and Robust Interval Estimation with a Random Forest to Classify Atrial Fibrillation. Christoph Hoog Antink, Steffen Leonhardt, Marian Walter.
  10. ECG Classification Based on Time and Frequency Domain Features Using Random Forrests. Martin Kropf, Dieter Hayn, Günter Schreier.
  11. Detection of Atrial Fibrillation Episodes from Short Single Lead Recordings by Means of Ensemble Learning. Pietro Bonizzi, Kurt Driessens, Joel Karel.
  12. Identification of Features for Machine Learning Analysis for Automatic Arrhythmogenic Event Classification. Vadim Gliner, Yael Yaniv.
  13. AF Detection and ECG Classification based on Convolutional Recurrent Neural Network. Mohamed Limam, Frederic Precioso.
  14. SVM Based ECG Classification Using Rhythm and Morphology Features, Cluster Analysis and Multilevel Noise Estimation. Radovan Smíšek, Jakub Hejc, Marina Ronzhina, Andrea Nemcová, Lucie Maršánová, Jirí Chmelík, Jana Kolárová, Ivo Provazník, Lukáš Smital, Martin Vítek.
  15. Identifying Normal, AF and other Abnormal ECG Rhythms using a Cascaded Binary Classifier. Shreyasi Datta, Chetanya Puri, Ayan Mukherjee, Rohan Banerjee, Anirban Dutta Choudhury, Rituraj Singh, Arijit Ukil, Soma Bandyopadhyay, Arpan Pal, Sundeep Khandelwal.
  16. AF Classification from ECG Recording Using Feature Ensemble and Sparse Coding. Bradley Whitaker, Muhammed Rizwan, Burak Aydemir, James Rehg, David Anderson.
  17. Multi-parametric Analysis for Atrial Fibrillation Classification in the ECG. Ivaylo Christov, Vessela Krasteva, Iana Simova, Tatyana Neycheva, Ramun Schmid.
  18. ENCASE: an ENsemble ClASsifiEr for ECG Classification Using Expert Features and Deep Neural Networks. Shenda Hong, Meng Wu, Yuxi Zhou, Qingyun Wang, Junyuan Shang, Hongyan Li, Junqing Xie.
  19. Cardiac Rhythm Classification from a Short Single Lead ECG Recording via Random Forest. Ruhi Mahajan, Rishikesan Kamaleswaran, John Andrew Howe, Oguz Akbilgic.
  20. Diagnosis of AF Based on Time and Frequency Features by using a Hierarchical Classifier. Yang Liu, Kuanquan Wang, Qince Li, Runnan He, Yong Xia, Zhen Li, Hao Liu, Henggui Zhang.
  21. Computer-based Assessment of the Effects of Amiodarone on Short QT Syndrome Variant 1 in Human Ventricles. Cunjin Luo, Kuanquan Wang, Henggui Zhang, Yang Liu, Yong Xia.

11-9: Challenge Posters II

  1. Atrial Fibrillation Screening through Combined Timing Features of Short Single-Lead Electrocardiograms. Manuel García, Juan Ródenas, Raul Alcaraz, José J Rieta.
  2. Atrial Fibrillation Detection Using Feedforward Neural Networks and Automatically Extracted Signal Features. Santiago Jiménez-Serrano, Jaime Yagüe-Mayans, Elena Simarro-Mondejar, Conrado J. Calvo, Francisco Castells, José Millet Roig.
  3. Detection of Atrial Fibrillation Using Decision Tree Ensemble. Guangyu Bin, Minggang Shao, Guanghong Bin, Jiao Huang, Dingchang Zheng, Shuicai Wu.
  4. A Hierarchical Cardiac Rhythm Classification Methodology Based on Electrocardiogram Fiducial Points. Dionisije Sopic, Elisabetta De Giovanni, Amir Aminifar, David Atienza.
  5. Detection of AF and Other Rhythms Using RR Variability and ECG Spectrum Measures. Lucia Billeci, Franco Chiarugi, Magda Costi, David Lombardi, Maurizio Varanini.
  6. Arrhythmia Classification via Time and Frequency Domain Analyses of Ventricular and Atrial Contractions. Irena Jekova, Todor Stoyanov, Ivan Dotsinsky.
  7. Combining Template-based and Feature-based Classification to Detect Atrial Fibrillation from a Short Single Lead ECG Recording. Matthieu Da Silva-Filarder, Faezeh Marzbanrad.
  8. Can Supervised Learning Be Used to Classify Cardiac Rhythms?. Marcus Vollmer, Philipp Sodmann, Leonard Caanitz, Neetika Nath, Lars Kaderali.
  9. Identification of AF and Other Cardiac Arrhythmias from a Single-lead ECG Using Dynamic Time Warping. Maria Tziakouri, Costas Pitris, Christina Orphanidou.
  10. Automated Detection of Atrial Fbrillation using Fourier-Bessel expansion and Teager Energy Operator from Electrocardiogram Signals. Shivnarayan Patidar, Ashish Sharma, Niranjan Garg.
  11. Atrial Fibrillation Classification Using QRS Complex Features and LSTM. Vykintas Maknickas, Algirdas Maknickas.
  12. Atrial Fibrillation Classification from a Short Single Lead ECG Recording Using Hierarchical Classifier. Erin Coppola, Prashnna Gyawali, Nihar Vanjara, Daniel Giaime, Linwei Wang.
  13. Electrocardiogram Classification -- a Human Expert Way. Heikki Väänänen, Jarno Mäkelä.
  14. Classification of ECG Recordings with Neural Networks Based on Specific Morphological Features and Regularity of the Signal. Katarzyna Stepien, Iga Grzegorczyk.

12-1: Challenge II

  1. Comparing Feature Based Classifiers and Convolutional Neural Networks to Detect Arrhythmia from Short Segments of ECG. Fernando Andreotti, Oliver Carr, Marco A F Pimentel, Adam Mahdi, Maarten De Vos.
  2. Classification of Atrial Fibrillation Using Multidisciplinary Features and Gradient Boosting. Andrew Goodwin, Sebastian Goodfellow, Danny Eytan, Robert Greer, Mjaye Mazwi, Peter Laussen, Sebastian Goodfellow.
  3. Beat by Beat: Classifying Cardiac Arrhythmias with Recurrent Neural Networks. Patrick Schwab, Gaetano Claudio Scebba, Jia Zhang, Marco Delai, Walter Karlen.
  4. Automatic Detection of Atrial Fibrillation and Other Arrhythmias in Holter ECG Recordings using PQRS Morphology and Rhythm Features. Filip Plesinger, Petr Nejedly, Ivo Viscor, Josef Halamek, Pavel Jurak.

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Updated Wednesday, 31 January 2018 at 13:53 EST

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