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

Piece-wise Matching Layer in Representation Learning for ECG Classification
Behzad Ghazanfari, Fatemeh Afghah, Sixian Zhang. arXiv (Cornell University), 2020. Cites: paper

Heartbeat monitoring with an mm-wave radar based on deep learning: a novel approach for training and classifying heterogeneous signals
Haoyu Zhang. Remote Sensing Letters, 2020. Cites: paper

Characterization of Gravitational Waves Signals Using Neural Networks
A. Caramete, Andrei Ieronim Constantinescu, L. Caramete et al. arXiv (Cornell University), 2020. Cites: paper

Accurate deep neural network model to detect cardiac arrhythmia on more than 10,000 individual subject ECG records
Özal Yıldırım, Muhammed Talo, Edward J. Ciaccio et al. Computer Methods and Programs in Biomedicine, 2020. Cites: paper

Data Augmentation for Electrocardiogram Classification with Deep Neural Network
Naoki Nonaka, Jun Seita. arXiv (Cornell University), 2020. Cites: paper

Using Deep Networks for Scientific Discovery in Physiological Signals
Tom Beer, Bar Eini-Porat, Sebastian D. Goodfellow et al. arXiv (Cornell University), 2020. Cites: paper

The Effect of Various Strengths of Noises and Data Augmentations on Classification of Short Single-Lead ECG Signals Using Deep Neural Networks
Faezeh Nejati Hatamian, Amirabbas Davari, Andreas Maier. arXiv (Cornell University), 2020. Cites: paper

Knowledge-shot learning: An interpretable deep model for classifying imbalanced electrocardiography data
Yen-Hsiu Chou, Shenda Hong, Yuxi Zhou et al. Neurocomputing, 2020. Cites: paper

The Future AI in Healthcare: A Tsunami of False Alarms or a Product of Experts?
Gari D. Clifford. arXiv (Cornell University), 2020. Cites: paper

1-D Convolutional Neural Network for ECG Arrhythmia Classification
Jacopo Ferretti, Vincenzo Randazzo, Giansalvo Cirrincione et al. Smart innovation, systems and technologies, 2020. Cites: paper

A Deep Learning Approach for Featureless Robust Quality Assessment of Intermittent Atrial Fibrillation Recordings from Portable and Wearable Devices
Álvaro Huerta Herráiz, Arturo Martínez‐Rodrigo, Vicente Bertomeu‐González et al. Entropy, 2020. Cites: paper

Comparative analysis between convolutional neural network learned and engineered features: A case study on cardiac arrhythmia detection
Ruhi Mahajan, Rishikesan Kamaleswaran, Oğuz Akbilgiç. Cardiovascular Digital Health Journal, 2020. Cites: paper

Low-Cost DNN Hardware Accelerator for Wearable, High-Quality Cardiac Arrythmia Detection
Johnson Loh, Jianan Wen, Tobias Gemmeke. IEEE International Conference on Application-specific Systems, Architectures and Processors (ASAP), 2020. Cites: paper

Interpretable Factorization for Neural Network ECG Models
Christopher Snyder, Sriram Vishwanath. arXiv (Cornell University), 2020. Cites: paper

Artificial Neural Network for Atrial Fibrillation Identification in Portable Devices
Daniele Marinucci, Agnese Sbrollini, Ilaria Marcantoni et al. Sensors, 2020. Cites: paper

Multicenter intracranial EEG dataset for classification of graphoelements and artifactual signals
Petr Nejedlý, Václav Křemen, Vladimir Sladky et al. Scientific Data, 2020. Cites: paper

A novel Domain Adaptive Residual Network for automatic Atrial Fibrillation Detection
Yanrui Jin, Chengjin Qin, Jinlei Liu et al. Knowledge-Based Systems, 2020. Cites: paper

Opportunities and challenges of deep learning methods for electrocardiogram data: A systematic review
Shenda Hong, Yuxi Zhou, Junyuan Shang et al. Computers in Biology and Medicine, 2020. Cites: paper

Automatic multilabel electrocardiogram diagnosis of heart rhythm or conduction abnormalities with deep learning: a cohort study
Hongling Zhu, Cheng Cheng, Hang Yin et al. The Lancet Digital Health, 2020. Cites: paper

CLOCS: Contrastive Learning of Cardiac Signals.
Dani Kiyasseh, Tingting Zhu, David A. Clifton. arXiv (Cornell University), 2020. Cites: paper

CLOCS: Contrastive Learning of Cardiac Signals Across Space, Time, and Patients
Dani Kiyasseh, Tingting Zhu, David A. Clifton. arXiv (Cornell University), 2020. Cites: paper

PTB-XL, a large publicly available electrocardiography dataset
Patrick Wagner, Nils Strodthoff, Ralf-Dieter Bousseljot et al. Scientific Data, 2020. Cites: paper

Detection of Atrial Fibrillation from Single Lead ECG Signal Using Multirate Cosine Filter Bank and Deep Neural Network
Samit Kumar Ghosh, Rajesh Kumar Tripathy, Mario R. Arrieta Paternina et al. Journal of Medical Systems, 2020. Cites: paper

ECG signal classification with binarized convolutional neural network
Qing Wu, Yangfan Sun, Hui Min Yan et al. Computers in Biology and Medicine, 2020. Cites: paper

Effects of Lead Position, Cardiac Rhythm Variation and Drug-induced QT Prolongation on Performance of Machine Learning Methods for ECG Processing
Marat Bogdanov, Salim Baigildin, Aigul Fabarisova et al. Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT), 2020. Cites: paper

Path to precision: prevention of post-operative atrial fibrillation
Rinku S. Skaria, Saman Parvaneh, Sophia Zhou et al. Journal of Thoracic Disease, 2020. Cites: paper

Kalman-based Spectro-Temporal ECG Analysis using Deep Convolutional Networks for Atrial Fibrillation Detection
Zheng Ming Zhao, Simo Särkkä, Ali Bahrami Rad. Journal of Signal Processing Systems, 2020. Cites: paper

Convolutional-Recurrent Neural Networks on Low-Power Wearable Platforms for Cardiac Arrhythmia Detection
Antonino Faraone, Ricard Delgado-Gonzalo. IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), 2020. Cites: paper

SoQal: Selective Oracle Questioning in Active Learning
Dani Kiyasseh, Tingting Zhu, David Andrew Clifton. arXiv (Cornell University), 2020. Cites: paper

ALPS: Active Learning via Perturbations
Dani Kiyasseh, Tingting Zhu, David Andrew Clifton. arXiv (Cornell University), 2020. Cites: paper

A morphology based deep learning model for atrial fibrillation detection using single cycle electrocardiographic samples
Sarah W.E. Baalman, Florian E. Schroevers, Abel J. Oakley et al. International Journal of Cardiology, 2020. Cites: paper

Deep Multi-Scale Fusion Neural Network for Multi-Class Arrhythmia Detection
Ruxin Wang, Jianping Fan, Ye Li. IEEE Journal of Biomedical and Health Informatics, 2020. Cites: paper

Automated pre-screening of arrhythmia using hybrid combination of Fourier–Bessel expansion and LSTM
Ashish Sharma, Niranjan Garg, Shivnarayan Patidar et al. Computers in Biology and Medicine, 2020. Cites: paper

Detection of Atrial Fibrillation Using 1D Convolutional Neural Network
Chaur‐Heh Hsieh, Yan-Shuo Li, Bor‐Jiunn Hwang et al. Sensors, 2020. Cites: paper

Automatic diagnosis of the 12-lead ECG using a deep neural network
Antônio H. Ribeiro, Manoel Horta Ribeiro, Gabriela M. M. Paixão et al. Nature Communications, 2020. Cites: paper

Reversal No Longer Matters: Attention-Based Arrhythmia Detection with Lead-Reversal ECG Data
Zheng Cao, Jialin Shi, Ji Yi Wu. IEEE International Conference on Acoustics Speech and Signal Processing, 2020. Cites: paper

The Effect of Data Augmentation on Classification of Atrial Fibrillation in Short Single-Lead ECG Signals Using Deep Neural Networks
Faezeh Nejati Hatamian, Nishant Ravikumar, Sulaiman Vesal et al. IEEE International Conference on Acoustics Speech and Signal Processing, 2020. Cites: paper

ECGadv: Generating Adversarial Electrocardiogram to Misguide Arrhythmia Classification System
Huangxun Chen, Chenyu Huang, Qianyi Huang et al. Proceedings of the AAAI Conference on Artificial Intelligence, 2020. Cites: paper

The impact of Negative to Positive Training Dataset Ratio on Atrial Fibrillation Classification Machine Learning Algorithms Performance
Firdaus Firdaus, Andre Herviant Juliano, Muhammad Naufal Rachmatullah et al. Journal of Physics Conference Series, 2020. Cites: paper

A Lightweight Deep Learning Model for Fast Electrocardiographic Beats Classification With a Wearable Cardiac Monitor: Development and Validation Study
Eunjoo Jeon, Kyusam Oh, Soonhwan Kwon et al. JMIR Medical Informatics, 2020. Cites: paper

Deep learning models for electrocardiograms are susceptible to adversarial attack
Xintian Han, Yuxuan Hu, Luca Foschini et al. Nature Medicine, 2020. Cites: paper

Development of Rhythm-based and Morphology-based Algorithm for Atrial Fibrillation Detection From Single Lead ECG Signal
Jantappapa Chanthercrob, Suchet Mahattanatawee, Arporn Teeramongkonrasmee et al. International Electrical Engineering Congress (iEECON), 2020. Cites: paper

A Review on the State of the Art in Atrial Fibrillation Detection Enabled by Machine Learning
Ali Rizwan, Ahmed Zoha, Ismail Ben Mabrouk et al. IEEE Reviews in Biomedical Engineering, 2020. Cites: paper

A high-precision arrhythmia classification method based on dual fully connected neural network
Haoren Wang, Haotian Shi, Ke Lin et al. Biomedical Signal Processing and Control, 2020. Cites: paper

A Prototype Framework Design for Assisting the Detection of Atrial Fibrillation Using a Generic Low-Cost Biomedical Sensor
Jesús Pérez-Valero, Antonio-Javier García-Sánchez, Manuel Ruiz Marín et al. Sensors, 2020. Cites: paper

ICU management based on big data
Stefano Falini, Giovanni Angelotti, Maurizio Cecconi. Current Opinion in Anaesthesiology, 2020. Cites: paper

Identification of patients with atrial fibrillation: a big data exploratory analysis of the UK Biobank
Julien Oster, Jemma C. Hopewell, Klemen Žiberna et al. Physiological Measurement, 2020. Cites: paper

A Comprehensive Study of Complexity and Performance of Automatic Detection of Atrial Fibrillation: Classification of Long ECG Recordings Based on the PhysioNet Computing in Cardiology Challenge 2017
Denis V. Kleyko, Evgeny Osipov, Urban Wiklund. Biomedical Physics & Engineering Express, 2020. Cites: paper

A Wearable Electrocardiogram Telemonitoring System for Atrial Fibrillation Detection
Minggang Shao, Zhuhuang Zhou, Guangyu Bin et al. Sensors, 2020. Cites: paper

Anomaly Detection and Safe Transmission of ECG Signals in Point-of-Care Systems
N. S. Akhila, Sabeena Beevi K. Lecture notes in electrical engineering, 2020. Cites: paper

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