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

Dual-Channel Neural Network for Atrial Fibrillation Detection From a Single Lead ECG Wave
Bo Fang, Junxin Chen, Yu Liu et al. IEEE Journal of Biomedical and Health Informatics, 2021. Cites: paper

A community effort to assess and improve computerized interpretation of 12-lead resting electrocardiogram
Zijian Ding, Guijin Wang, Huazhong Yang et al. Medical & Biological Engineering & Computing, 2021. Cites: paper

Application of Dense Neural Networks for Detection of Atrial Fibrillation and Ranking of Augmented ECG Feature Set
Vessela Krasteva, Ivaylo Ivanov Christov, Stefan Naydenov et al. Sensors, 2021. Cites: paper

Applications of Machine Learning in Ambulatory ECG
Joel Q. Xue, Long Hao Yu. Hearts, 2021. Cites: paper

NAS-Bench-360: Benchmarking Diverse Tasks for Neural Architecture Search
Renbo Tu, Mikhail Khodak, Nicholas P. Roberts et al. arXiv (Cornell University), 2021. Cites: paper

Framework for Privacy-Preserving Wearable Health Data Analysis: Proof-of-Concept Study for Atrial Fibrillation Detection
Anamaria Vizitiu, Cosmin-Ioan Niță, Radu Miron Toev et al. Applied Sciences, 2021. Cites: paper

Convolutional squeeze-and-excitation network for ECG arrhythmia detection
Rongjun Ge, Tengfei Shen, Ying Zhou et al. Artificial Intelligence in Medicine, 2021. Cites: paper

Electrocardiogram Quality Assessment with a Generalized Deep Learning Model Assisted by Conditional Generative Adversarial Networks
Xue Mei Zhou, Xin Zhu, Keijiro Nakamura et al. Life, 2021. Cites: paper

Efficient ECG Analysis with High F1 Score and Low Computation Complexity
Andrii Yavorskyi, Bohdan Tyshchenko, Т. В. Панченко. IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), 2021. Cites: paper

A Study on Selection of HRV-based Features for Different Classifiers in Atrial Fibrillation Detection
Szymon Buś, Konrad Jędrzejewski, Przemysław Guzik. Signal Processing Symposium (SPSympo), 2021. Cites: paper

ECG Quality Assessment via Deep Learning and Data Augmentation
Álvaro Huerta Herráiz, Arturo Martínez‐Rodrigo, José Joaquín Rieta et al. Computing in cardiology, 2021. Cites: paper

Improving Machine Learning Education During the COVID-Pandemic Using Past Computing in Cardiology Challenges
Maurice Rohr, Filip Plešinger, Veronika Bulková et al. Computing in cardiology, 2021. Cites: paper

Investigating the Robustness of Deep Learning to Electrocardiogram Noise
Jenny Venton, Philip J. Aston. Computing in cardiology, 2021. Cites: paper

Leveraging Period-Specific Variations in ECG Topology for Classification Tasks
Paul Samuel Ignacio. Computing in cardiology, 2021. Cites: paper

Will Two Do? Varying Dimensions in Electrocardiography: The PhysioNet/Computing in Cardiology Challenge 2021
Matthew A. Reyna, Nadi Sadr, Erick Andres Perez Alday et al. Computing in cardiology, 2021. Cites: paper

Electrocardiogram Quality Assessment Using Unsupervised Deep Learning
Nick Seeuws, Maarten De Vos, Alexander Bertrand. IEEE Transactions on Biomedical Engineering, 2021. Cites: paper

Atrial fibrillation detection by DFA and SDCST methods
Regis Nunes Vargas, Antônio Cláudio Paschoarelli Veiga, Raquel Romes Linhares. Model Assisted Statistics and Applications, 2021. Cites: paper

AFCNNet: Automated detection of AF using chirplet transform and deep convolutional bidirectional long short term memory network with ECG signals
Tejas Radhakrishnan, Jay Karhade, Samit Kumar Ghosh et al. Computers in Biology and Medicine, 2021. Cites: paper

Synthesis of Realistic ECG Waveforms Using a Composite Generative Adversarial Network for Classification of Atrial Fibrillation
Rohan Banerjee, Avik Ghose. European Signal Processing Conference (EUSIPCO), 2021. Cites: paper

Survey on atrial fibrillation detection from a single-lead ECG wave for Internet of Medical Things
Yu Liu, Junxin Chen, Nan Bao et al. Computer Communications, 2021. Cites: paper

Robust Heartbeat Classification for Wearable Single-Lead ECG via Extreme Gradient Boosting
Huaiyu Zhu, Yisheng Zhao, Yun Pan et al. Sensors, 2021. Cites: paper

Machine Learning Approach to Detection of Atrial Fibrillation Using High Quality Facial Videos
Cigdem Polat Dautov, Ruslan Dautov, Jean‐Philippe Couderc et al. IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI ...), 2021. Cites: paper

Sequence to Sequence ECG Cardiac Rhythm Classification Using Convolutional Recurrent Neural Networks
Teeranan Pokaprakarn, Rebecca Rutherford Kitzmiller, Joseph Randall Moorman et al. IEEE Journal of Biomedical and Health Informatics, 2021. Cites: paper

Machine learning with electrocardiograms: A call for guidelines and best practices for ‘stress testing’ algorithms
Raymond Bond, Dewar Finlay, Salah S. Al‐Zaiti et al. Journal of Electrocardiology, 2021. Cites: paper

A clinical deep learning framework for continually learning from cardiac signals across diseases, time, modalities, and institutions
Dani Kiyasseh, Tingting Zhu, David A. Clifton. Nature Communications, 2021. Cites: paper

HADLN: Hybrid Attention-Based Deep Learning Network for Automated Arrhythmia Classification
Mingfeng Jiang, Jiayan Gu, Yang Li et al. Frontiers in Physiology, 2021. Cites: paper

Real-time machine learning-based intensive care unit alarm classification without prior knowledge of the underlying rhythm
Wan-Tai Michael Au-Yeung, Rahul K. Sevakula, Ashish Kumar Sahani et al. European Heart Journal - Digital Health, 2021. Cites: paper

Learning Explainable Time-Morphology Patterns for Automatic Arrhythmia Classification from Short Single-Lead ECGs
Hyeonjeong Lee, Miyoung Shin. Sensors, 2021. Cites: paper

Multiclass Classification of Cardiac Rhythms on Short Single Lead ECG Recordings using Bidirectional Long Short-Term Memory Networks
Miguel Altuve, Fabio Hernández. IEEE Latin America Transactions, 2021. Cites: paper

Meeting the unmet needs of clinicians from AI systems showcased for cardiology with deep-learning–based ECG analysis
Yonatan Elul, Aviv A. Rosenberg, Assaf Schuster et al. Proceedings of the National Academy of Sciences, 2021. Cites: paper

Dual temporal convolutional network for single-lead fibrillation waveform extraction
Jun Lu, Jie Luo, Zhuoyan Xie et al. Neural Computing and Applications, 2021. Cites: paper

Identification of Transient Noise to Reduce False Detections in Screening for Atrial Fibrillation
Hesam Halvaei, Emma Svennberg, Leif Sörnmo et al. Frontiers in Physiology, 2021. Cites: paper

An Empiric Analysis of Wavelet-Based Feature Extraction on Deep Learning and Machine Learning Algorithms for Arrhythmia Classification.
Ritu Singh, Navin Rajpal, Rajesh Mehta. International Journal of Interactive Multimedia and Artificial Intelligence, 2021. Cites: paper

The application of deep learning in electrocardiogram: Where we came from and where we should go?
Jin-Yu Sun, Hui Shen, Qiang Qu et al. International Journal of Cardiology, 2021. Cites: paper

An Interpretable Hand-Crafted Feature-Based Model for Atrial Fibrillation Detection
Rahimeh Rouhi, Marianne Clausel, Julien Oster et al. Frontiers in Physiology, 2021. Cites: paper

How machine learning is impacting research in atrial fibrillation: implications for risk prediction and future management
Iván Olier, Sandra Ortega‐Martorell, Mark Pieroni et al. Cardiovascular Research, 2021. Cites: paper

SoCal: Selective Oracle Questioning for Consistency-based Active Learning of Cardiac Signals
Dani Kiyasseh, Tingting Zhu, David Andrew Clifton. arXiv (Cornell University), 2021. Cites: paper

Stacking segment-based CNN with SVM for recognition of atrial fibrillation from single-lead ECG recordings
Quang H. Nguyen, Binh Phu Nguyen, Trung B. Nguyen et al. Biomedical Signal Processing and Control, 2021. Cites: paper

The Analysis of biological signals using Machine Learning Techniques
Marina Ciocîrlan, Andreea Udrea, Ciprian Lupu. International Conference on Control Systems and Computer Science, 2021. Cites: paper

A Novel Transfer Learning-Based Approach for Screening Pre-Existing Heart Diseases Using Synchronized ECG Signals and Heart Sounds
Ramith Hettiarachchi, Udith Haputhanthri, Kithmini Herath et al. IEEE International Symposium on Circuits and Systems proceedings, 2021. Cites: paper

Recurrent Neural Network Circuit for Automated Detection of Atrial Fibrillation from Raw ECG
Sudarsan Sadasivuni, Rahul Chowdhury, Vinay Elkoori Ghantala Karnam et al. IEEE International Symposium on Circuits and Systems proceedings, 2021. Cites: paper

Certainty in QRS detection with artificial neural networks
Jonas Chromik, Lukas Pirl, Jossekin Beilharz et al. Biomedical Signal Processing and Control, 2021. Cites: paper

A Clinical Study on Atrial Fibrillation, Premature Ventricular Contraction, and Premature Atrial Contraction Screening Based on an ECG Deep Learning Model (Preprint)
Jianyuan Hong, Hua‐Jung Li, Chung‐Chi Yang et al. 2021. Cites: paper

Catheter ablation of persistent atrial fibrillation guided by electrograms spatiotemporal dispersion : automatic identification using machine learning approaches
Amina Ghrissi. 2021. Cites: paper

A SCALABLE HYBRID MODEL FOR ATRIAL FIBRILLATION DETECTION
Hao Wen, Wenjian Yu, Yuanqing Wu et al. Journal of Mechanics in Medicine and Biology, 2021. Cites: paper

All-You-Can-Fit 8-Bit Flexible Floating-Point Format for Accurate and Memory-Efficient Inference of Deep Neural Networks
Chengwei Huang, Tim-Wei Chen, Juinn-Dar Huang. arXiv (Cornell University), 2021. Cites: paper

Digital biomarkers and algorithms for detection of atrial fibrillation using surface electrocardiograms: A systematic review
Fons J. Wesselius, Mathijs S. van Schie, Natasja M. S. de Groot et al. Computers in Biology and Medicine, 2021. Cites: paper

InterCriteria Analysis: Application for ECG Data Analysis
Irena Ilieva Jekova, Peter Vassilev, Todor Stoyanov et al. Mathematics, 2021. Cites: paper

Interpretable deep learning for automatic diagnosis of 12-lead electrocardiogram
Dongdong Zhang, Samuel Yang, Xiaohui Yuan et al. iScience, 2021. Cites: paper

Classification of Arrhythmia in Time Series ECG Signals Using Image Encoding And Convolutional Neural Networks
K. Vandith Sreenivas, M. Ganesan, R. Vidhya Lavanya. International Conference on Bio Signals, Images, and Instrumentation (ICBSII), 2021. Cites: paper

Cited by