Publications citing AF Classification from a Short Single Lead ECG Recording: The PhysioNet/Computing in Cardiology Challenge 2017 v1.0.0

Atrial Fibrillation Detection Using Boosting and Stacking Ensemble
Dawid Smolen. Computing in cardiology, 2017. Cites: paper

Atrial Fibrillation Detection Using Feature Based Algorithm and Deep Conventional Neural Network
Shadi Ghiasi, Mostafa Abdollahpur, nasimalsadat madani et al. Computing in cardiology, 2017. Cites: paper

Atrial Fibrillation Detection Using Feedforward Neural Networks and Automatically Extracted Signal Features
Santiago Jiménez-Serrano, Jaime Yag�e-Mayans, Elena Simarro Mondéjar et al. Computing in cardiology, 2017. Cites: paper

Atrial Fibrillation Screening through Combined Timing Features of Short Single-Lead Electrocardiograms
Manuel García, Juan Ródenas, Raúl Alcaraz et al. Computing in cardiology, 2017. Cites: paper

Automated Detection of Atrial Fbrillation using Fourier-Bessel expansion and Teager Energy Operator from Electrocardiogram Signals
Shivnarayan Patidar, Ashish Sharma, Niranjan Garg. Computing in cardiology, 2017. Cites: paper

Automatic Detection of Atrial Fibrillation and Other Arrhythmias in Holter ECG Recordings using PQRS Morphology and Rhythm Features
Filip Plešinger, Petr Nejedlý, Ivo Viščor et al. Computing in cardiology, 2017. Cites: paper

Beat by Beat: Classifying Cardiac Arrhythmias with Recurrent Neural Networks
Patrick Schwab, Gaetano Scebba, Jia Zhang et al. Computing in cardiology, 2017. Cites: paper

Can Supervised Learning Be Used to Classify Cardiac Rhythms?
Marcus Vollmer, Philipp Sodmann, Leonard Caanitz et al. Computing in cardiology, 2017. Cites: paper

Cardiac Arrhythmia Detection from ECG Combining Convolutional and Long Short-Term Memory Networks
Philip A. Warrick, Masun Nabhan Homsi. Computing in cardiology, 2017. Cites: paper

Cardiac Rhythm Classification from a Short Single Lead ECG Recording via Random Forest
Ruhi Mahajan, Rishikesan Kamaleswaran, A Howe et al. Computing in cardiology, 2017. Cites: paper

Classification of ECG Recordings with Neural Networks Based on Specific Morphological Features and Regularity of the Signal
Katarzyna Stępień, Iga Grzegorczyk. Computing in cardiology, 2017. Cites: paper

Combining Multi-source Features and Support Vector Machine for Heart Rhythm Classification
Chengyu Liu, Qiao Li, Pradyumna Byappanahalli Suresha et al. Computing in cardiology, 2017. Cites: paper

Combining Template-based and Feature-based Classification to Detect Atrial Fibrillation from a Short Single Lead ECG Recording
Matthieu Da Silva-Filarder, Faezeh Marzbanrad. Computing in cardiology, 2017. Cites: paper

Comparing Feature Based Classifiers and Convolutional Neural Networks to Detect Arrhythmia from Short Segments of ECG
Fernando Andreotti, Oliver Carr, Marco A. F. Pimentel et al. Computing in cardiology, 2017. Cites: paper

Convolutional Recurrent Neural Networks for Electrocardiogram Classification
Martin Zihlmann, Dmytro Perekrestenko, Michael Tschannen. Computing in cardiology, 2017. Cites: paper

Densely Connected Convolutional Networks and Signal Quality Analysis to Detect Atrial Fibrillation Using Short Single-Lead ECG Recordings
Saman Parvaneh, Jonathan Rubin, Rahman Asif et al. Computing in cardiology, 2017. Cites: paper

Detection of AF and Other Rhythms Using RR Variability and ECG Spectrum Measures
Lucia Billeci, Franco Chiarugi, Magda Costi et al. Computing in cardiology, 2017. Cites: paper

Detection of Atrial Fibrillation Episodes from Short Single Lead Recordings by Means of Ensemble Learning
Pietro Bonizzi, Kurt Driessens, Joel M. H. Karel. Computing in cardiology, 2017. Cites: paper

Detection of Atrial Fibrillation in ECG Hand-held Devices Using a Random Forest Classifier
Morteza Zabihi, Ali Bahrami Rad, Aggelos K. Katsaggelos et al. Computing in cardiology, 2017. Cites: paper

Detection of Atrial Fibrillation Using Decision Tree Ensemble
Guangyu Bin, Minggang Shao, Guanghong Bin et al. Computing in cardiology, 2017. Cites: paper

Diagnosis of AF Based on Time and Frequency Features by using a Hierarchical Classifier
Yang Liu, Kuanquan Wang, Qince Li et al. Computing in cardiology, 2017. Cites: paper

ECG Classification Based on Time and Frequency Domain Features Using Random Forrests
Martin Kropf, Dieter Hayn, Günter Schreier. Computing in cardiology, 2017. Cites: paper

Electrocardiogram Classification -- a Human Expert Way
Heikki V��n�nen, Jarno M�kel�. Computing in cardiology, 2017. Cites: paper

ENCASE: an ENsemble ClASsifiEr for ECG Classification Using Expert Features and Deep Neural Networks
Shenda Hong, Meng Wu, Yuxi Zhou et al. Computing in cardiology, 2017. Cites: paper

Fusing QRS Detection, Waveform Features, and Robust Interval Estimation with a Random Forest to Classify Atrial Fibrillation
Christoph Hoog Antink, Steffen K. Leonhardt, Marian Walter. Computing in cardiology, 2017. Cites: paper

Heart Rhythm Classification using Short-term ECG Atrial and Ventricular Activity Analysis
Sasan Yazdani, Priscille Laub, Adrian Luca et al. Computing in cardiology, 2017. Cites: paper

Identification of AF and Other Cardiac Arrhythmias from a Single-lead ECG Using Dynamic Time Warping
Maria Tziakouri, Costas Pitris, Christina Orphanidou. Computing in cardiology, 2017. Cites: paper

Identifying Normal, AF and other Abnormal ECG Rhythms using a Cascaded Binary Classifier
Shreyasi Datta, Chetanya Puri, Ayan Mukherjee et al. Computing in cardiology, 2017. Cites: paper

Multi-parametric Analysis for Atrial Fibrillation Classification in the ECG
Ivaylo Ivanov Christov, Vessela Krasteva, Iana Ivaylova Simova et al. Computing in cardiology, 2017. Cites: paper

Rhythm and Quality Classification from Short ECGs Recorded using a Mobile Device
Joachim A. Behar, Aviv A. Rosenberg, Yael Yaniv et al. Computing in cardiology, 2017. Cites: paper

Robust ECG Signal Classification for the Detection of Atrial Fibrillation Using Novel Neural Networks
Zhaohan Xiong, Martin K. Stiles, Jichao Zhao. Computing in cardiology, 2017. Cites: paper

Robust Feature Extraction from Noisy ECG for Atrial Fibrillation Detection
Octavian Lucian Hasna, Rodica Potolea. Computing in cardiology, 2017. Cites: paper

SVM Based ECG Classification Using Rhythm and Morphology Features, Cluster Analysis and Multilevel Noise Estimation
Radovan Smíšek, Jakub Hejč, Marina Ronzhina et al. Computing in cardiology, 2017. Cites: paper

Automatic detection of atrial fibrillation and other arrhythmias in holter ECG recordings using rhythm features and neural networks
Filip Plešinger, Petr Nejedlý, Ivo Viščor et al. Computing in cardiology, 2017. Cites: paper

Classification of AF and other arrhythmias from a short segment of ECG using dynamic time warping
Maria Tziakouri, Costas Pitris, Christina Orphanidou. Computing in cardiology, 2017. Cites: paper

Electrocardiogram classification — A human expert way
Heikki Väänänen, Jarno Mäkelä. Computing in cardiology, 2017. Cites: paper

Hierarchical cardiac-rhythm classification based on electrocardiogram morphology
Dionisije Sopic, Elisabetta De Giovanni, Amir Aminifar et al. Infoscience (Ecole Polytechnique Fédérale de Lausanne), 2017. Cites: paper

Multi-source features and support vector machine for heart rhythm classification
Chengyu Liu, Qiao Li, Pradyumna B. Suresh et al. Computing in cardiology, 2017. Cites: paper

Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks
Pranav Rajpurkar, Awni Hannun, Masoumeh Haghpanahi et al. arXiv (Cornell University), 2017. Cites: paper

Techniques for visualizing LSTMs applied to electrocardiograms
Jos van der Westhuizen, Joan Lasenby. arXiv (Cornell University), 2017. Cites: paper

Automatic diagnosis of the 12-lead ECG using a deep neural network
Antônio H. Ribeiro, Manoel Horta Ribeiro, Gabriela Miana Paixao et al. RePEc: Research Papers in Economics. Cites: paper

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