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

Atrial Fibrillation Detection in Dynamic Signals
Caiyun Ma, Shoushui Wei, Chengyu Liu. Springer eBooks, 2020. Cites: paper

Binary Classifiers for Data Integrity Detection in Wearable IoT Edge Devices
Arlene John, Rajesh Chandrasekhara Panicker, Barry Cardiff et al. IEEE Open Journal of Circuits and Systems, 2020. Cites: paper

Cardiac Arrhythmia Detection from ECG with Convolutional Recurrent Neural Networks
Jérôme Van Zaen, Ricard Delgado-Gonzalo, Damien Ferrario et al. Communications in computer and information science, 2020. Cites: paper

DAAT: A New Method to Train Convolutional Neural Network on Atrial Fibrillation Detection
Jian Zhang, Juan Liu, Pei‐Fang Li et al. Lecture notes in computer science, 2020. Cites: paper

Data Augmentation for Deep Learning-Based ECG Analysis
Qing Pan, Xinyi Li, Luping Fang. Springer eBooks, 2020. Cites: paper

ECG Beat Classifiers: A Journey from ANN To DNN
Manisha Manisha, Sanjeev Kumar Dhull, Krishna Kant Singh. Procedia Computer Science, 2020. Cites: paper

ECG Classification Using Deep CNN Improved by Wavelet Transform
Yunxiang Zhao, Jinyong Cheng, Ping Zhan et al. Computers, materials & continua/Computers, materials & continua (Print), 2020. Cites: paper

ECG Interpretation with Deep Learning
Wenjie Cai, Danqin Hu. Springer eBooks, 2020. Cites: paper

Machine Learning for Physiological Time Series: Representing and Controlling Blood Glucose for Diabetes Management
Ian Fox. Deep Blue (University of Michigan), 2020. Cites: paper

MultiFusionNet: Atrial Fibrillation Detection With Deep Neural Networks.
Luan Tran, Yanfang Li, Luciano P. Nocera et al. PubMed Central, 2020. Cites: paper

Online Augmentation for Quality Improvement of Neural Networks for Classification of Single-Channel Electrocardiograms
Valeriia Guryanova. Communications in computer and information science, 2020. Cites: paper

Representative Databases for Feature Engineering and Computational Intelligence in ECG Processing
Hongxiang Gao, Chengyu Liu, Qin Shen et al. Springer eBooks, 2020. Cites: paper

Stages-Based ECG Signal Analysis From Traditional Signal Processing to Machine Learning Approaches: A Survey
Muhammad Wasimuddin, Khaled M. Elleithy, Abdelshakour Abuzneid et al. IEEE Access, 2020. Cites: paper

Study on Automatic Classification of Arrhythmias
Runnan He, Yang Liu, Henggui Zhang. Springer eBooks, 2020. Cites: paper

Topological Data Analysis for Arrhythmia Detection Through Modular Neural Networks
Meryll Dindin, Yuhei Umeda, Frédéric Chazal. Lecture notes in computer science, 2020. Cites: paper

Transferable approach for cardiac disease classification using deep learning
P. Gopika, V. Sowmya, E. A. Gopalakrishnan et al. Elsevier eBooks, 2020. Cites: paper

Anti-interference Ability Analysis for Common Atrial Fibrillation Features
Ying Li, Fan Zhou, Lina Zhao et al. Computing in cardiology, 2019. Cites: paper

Anti-Interference Ability Analysis for Common Atrial Fibrillation Features
Ying Li, Fan Zhou, Lina Zhao et al. 2019. Cites: paper

Clustered Standard Deviation and Its Benefit to Identify Atrial Fibrillation
Filip Plešinger, Ivo Viščor, Petr Nejedlý et al. Computing in cardiology, 2019. Cites: paper

Factors Influencing Automated Detection of Atrial Fibrillation
Peter W. Macfarlane, Shahid Latif, Brian Devine. Computing in cardiology, 2019. Cites: paper

Pay Attention and Watch Temporal Correlation: A Novel 1-D Convolutional Neural Network for ECG Record Classification
Yongchao Wang, Bin Xiao, Xiuli Bi et al. 2019. Cites: paper

Pay Attention and Watch Temporal Correlation: A Novel 1-D Convolutional Neural Network for ECG Record Classification
Yongchao Wang, Bin Xiao, Xiuli Bi et al. Computing in cardiology, 2019. Cites: paper

Opportunities and Challenges of Deep Learning Methods for Electrocardiogram Data: A Systematic Review
Shenda Hong, Yuxi Zhou, Junyuan Shang et al. arXiv (Cornell University), 2019. Cites: paper

Deep time–frequency representation and progressive decision fusion for ECG classification
Jing Zhang, Jing Tian, Yang Hua Cao et al. Knowledge-Based Systems, 2019. Cites: paper

An Improved Convolutional Neural Network Based Approach for Automated Heartbeat Classification
Haoren Wang, Haotian Shi, Xiaojun Chen et al. Journal of Medical Systems, 2019. Cites: paper

ATRIAL FIBRILLATION DETECTION ON ELECTROCARDIOGRAMS WITH CONVOLUTIONAL NEURAL NETWORKS
Viktor Kifer, Nataliya Zagorodna, Olena Hevko. Informatyka Automatyka Pomiary w Gospodarce i Ochronie Środowiska, 2019. Cites: paper

Automated arrhythmia classification based on a combination network of CNN and LSTM
Chen Chen, Zhengchun Hua, Ruiqi Zhang et al. Biomedical Signal Processing and Control, 2019. Cites: paper

Effects of lead position, cardiac rhythm variation and drug-induced QT prolongation on performance of machine learning methods for ECG processing
Bogdanov, Marat, Salim Baigildin, Aigul Fabarisova et al. arXiv (Cornell University), 2019. Cites: paper

MTCNN: A Deep Neural Network for Recognizing Stochastic Phase Shifted Time Series Data
Junpeng Bao, Xinyi Li, Yu Han Gao. 2019. Cites: paper

Atrial Fibrillation Prediction With Residual Network Using Sensitivity and Orthogonality Constraints
Amin Jalali, Minho Lee. IEEE Journal of Biomedical and Health Informatics, 2019. Cites: paper

CNNs, LSTMs, and Attention Networks for Pathology Detection in Medical Data
Nora Vogt. arXiv (Cornell University), 2019. Cites: paper

Atrial Fibrillation Detection in Spectrogram Based on Convolution Neural Networks
Jing-Ming Guo, Chiao-Chun Yang, Zong‐Hui Wang et al. International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS), 2019. Cites: paper

Automatic Identification of Arrhythmia from ECG Using AlexNet Convolutional Neural Network
Fazla Rabbi Mashrur, Amit Dutta Roy, Dabasish Kumar Saha. International Conference on Electrical Information and Communication Technology (EICT), 2019. Cites: paper

Classification of Single-Lead Electrocardiograms: TDA Informed Machine Learning
Paul Samuel Ignacio, Christopher Dunstan, Esteban Escobar et al. International Conference on Machine Learning and Applications, 2019. Cites: paper

Work-in-Progress: On the Feasibility of Lightweight Scheme of Real-Time Atrial Fibrillation Detection Using Deep Learning
Yunkai Yu, Zhihong Yang, Peiyao Li et al. IEEE Real-Time Systems Symposium (RTSS), 2019. Cites: paper

Dynamic Monitoring of Health Using Smart Health Band
Viraj Pradip Puntambekar, Shreyas Agarwal, P. Mahalakshmi. Advances in intelligent systems and computing, 2019. Cites: paper

Domain Knowledge Guided Deep Atrial Fibrillation Classification and Its Visual Interpretation
Xiaoyu Li, Buyue Qian, Jishang Wei et al. ACM International Conference on Information and Knowledge Management (CIKM), 2019. Cites: paper

Atrial Fibrillation Identification through ECG Signals
Ng Joe Yee, Vikneswaran Vijean, A. Saidatul et al. Journal of Physics Conference Series, 2019. Cites: paper

Health intelligence
Arash Shaban‐Nejad, Rishikesan Kamaleswaran, Eun Kyong Shin et al. Elsevier eBooks, 2019. Cites: paper

AfibPred
Khadidja Benchaira, Salim Bitam, Abdelhamid Mellouk et al. International Conference on Big Data and Internet of Things (BDIOT), 2019. Cites: paper

On the Effectiveness of Deep Representation Learning: The Atrial Fibrillation Case
Matteo Gadaleta, Michele Rossi, Eric J. Topol et al. Computer, 2019. Cites: paper

Deep learning to automatically interpret images of the electrocardiogram: Do we need the raw samples?
Rob Brisk, Raymond Bond, Elizabeth C. Banks et al. Journal of Electrocardiology, 2019. Cites: paper

A Short-term ECG Signal Classification Method Based on Residual Network and Bi-directional LSTM
Xiaochun Wu, Xin’an Wang, Jieru Ma et al. International Communication Engineering and Cloud Computing Conference (CECCC), 2019. Cites: paper

Strong deep learning baseline for single lead ECG processing
Botos Csaba, Tamás Hakkel, Márton Áron Goda et al. 2019. Cites: paper

StuCoSReC. Proceedings of the 2019 6th Student Computer Science Research Conference
Máté Pintér, Balázs Dávid. 2019. Cites: paper

A novel data augmentation method to enhance deep neural networks for detection of atrial fibrillation
Ping Cao, Xinyi Li, Kedong Mao et al. Biomedical Signal Processing and Control, 2019. Cites: paper

A Hardware Inference Accelerator for Temporal Convolutional Networks
Rashid Ali, Maen Mallah, Martin Leyh et al. IEEE Nordic Circuits and Systems Conference (NorCAS), 2019. Cites: paper

An End-to-End framework for automatic detection of Atrial Fibrillation using Deep Residual Learning
Deepankar Nankani, Rashmi Dutta Baruah. IEEE Region 10 Conference (TENCON), 2019. Cites: paper

Classification of Short-time Single-lead ECG Recordings Using Deep Residual CNN
Areej Kharshid, Haikel Salem Alhichri, Ridha Ouni et al. International Conference on New Trends in Computing Sciences (ICTCS), 2019. Cites: paper

Enabling Heart Self-Monitoring for All and for AAL—Portable Device within a Complete Telemedicine System
Andrés L. Bleda, Francisco-Manuel Melgarejo-Meseguer, Francisco-Javier Gimeno-Blanes et al. Sensors, 2019. Cites: paper

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