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

Time–frequency time–space LSTM for robust classification of physiological signals
Tuan D. Pham. Scientific Reports, 2021. Cites: paper

Machine Learning for Real-Time Heart Disease Prediction
Dimitris Bertsimas, Luca Mingardi, Bartolomeo Stellato. IEEE Journal of Biomedical and Health Informatics, 2021. Cites: paper

Transfer learning for ECG classification
Kuba Weimann, Tim Conrad. Scientific Reports, 2021. Cites: paper

A Comprehensive Explanation Framework for Biomedical Time Series Classification
Praharsh Ivaturi, Matteo Gadaleta, Amitabh C. Pandey et al. IEEE Journal of Biomedical and Health Informatics, 2021. Cites: paper

ECG Language processing (ELP): A new technique to analyze ECG signals
Sajad Mousavi, Fatemeh Afghah, Fatemeh Khadem et al. Computer Methods and Programs in Biomedicine, 2021. Cites: paper

Electrocardiogram Classification and Visual Diagnosis of Atrial Fibrillation with DenseECG
Dacheng Chen, Dan Li, Xiuqin Xu et al. arXiv (Cornell University), 2021. Cites: paper

Supervised SVM Transfer Learning for Modality-Specific Artefact Detection in ECG
Jonathan Moeyersons, John Morales, Amalia Villa et al. Sensors, 2021. Cites: paper

Platform for Healthcare Promotion and Cardiovascular Disease Prevention
Carlos A. Gómez-García, Miguel Askar-Rodriguez, Jaime Velasco-Medina. IEEE Journal of Biomedical and Health Informatics, 2021. Cites: paper

A Comparative Analysis of Biomedical Data Mining Models for Cardiac Signal Classification
Saumendra Kumar Mohapatra, Mihir Narayan Mohanty. Lecture notes in electrical engineering, 2021. Cites: paper

An Arbitrarily Reconfigurable Extreme Learning Machine Inference Engine for Robust ECG Anomaly Detection
Yu-Chuan Chuang, Yi-Ta Chen, Huai-Ting Li et al. IEEE Open Journal of Circuits and Systems, 2021. Cites: paper

Attention Models
Cao Xiao, Jimeng Sun. Springer eBooks, 2021. Cites: paper

CLECG: A Novel Contrastive Learning Framework for Electrocardiogram Arrhythmia Classification
Hui Chen, Guijin Wang, Guodong Zhang et al. IEEE Signal Processing Letters, 2021. Cites: paper

Electrocardiogram Classification Based on Deep Convolutional Neural Networks: A Review
admin admin, Adnan Mohsin Abdulazeez. Fusion Practice and Applications, 2021. Cites: paper

Electrocardiogram Quality Assessment with Autoencoder
Jan Garus, Mateusz Pabian, Marcin Wiśniewski et al. Lecture notes in computer science, 2021. Cites: paper

Innovative Way of Detecting Atrial Fibrillation Based on HRV Features Using AI-Techniques
Yongho Lee, Vinh Pham, Tai‐Myoung Chung. Communications in computer and information science, 2021. Cites: paper

Natural Brain-Inspired Intelligence for Screening in Healthcare Applications
Mahdi Naghshvarianjahromi, Sumit Majumder, Shiva Kumar et al. IEEE Access, 2021. Cites: paper

Over-fitting suppression training strategies for deep learning-based atrial fibrillation detection
Xiangyu Zhang, Jianqing Li, Zhipeng Cai et al. Medical & Biological Engineering & Computing, 2021. Cites: paper

Real-Time Analysis and Processing of Cardiogram Signals
A. Yavorskyi. Bulletin of Taras Shevchenko National University of Kyiv Series Physics and Mathematics, 2021. Cites: paper

Structural-Modal Analysis of Biomedical Signals
Alexey Yu. Spasenov, Kirill V. Kucherov, Сергей Досько et al. Advances in intelligent systems and computing, 2021. Cites: paper

Application Domains, Evaluation Data Sets, and Research Challenges of IoT: A Systematic Review
Ritika Lohiya, Ankit Thakkar. IEEE Internet of Things Journal, 2020. Cites: paper

A Real-time ECG Classification Scheme Using Anti-aliased Blocks with Low Sampling Rate
Yunkai Yu, Zhihong Yang, Zhicheng Yang et al. Computing in cardiology, 2020. Cites: paper

Comparative Study of Convolutional Neural Networks for ECG Quality Assessment
Álvaro Huerta Herráiz, Arturo Martínez‐Rodrigo, Alberto Puchol et al. Computing in cardiology, 2020. Cites: paper

False Alarm Reduction in Atrial Fibrillation Screening
Hesam Halvaei, Emma Svennberg, Leif Sörnmo et al. Computing in cardiology, 2020. Cites: paper

Knowledge, Machine Learning and Atrial Fibrillation: More Ingredients for a Tastier Cocktail
Tomás Teijeiro. Computing in cardiology, 2020. Cites: paper

Multi-Class Classification of Pathologies Found on Short ECG Signals
Georgi I. Nalbantov, Svetoslav Ivanov, Jeffrey van Prehn. Computing in cardiology, 2020. Cites: paper

Shapelet Discovery for Atrial Fibrillation Detection
Saman Parvaneh, Yale Chang. Computing in cardiology, 2020. Cites: paper

Integration of Results From Convolutional Neural Network in a Support Vector Machine for the Detection of Atrial Fibrillation
Caiyun Ma, Shoushui Wei, Tongshuai Chen et al. IEEE Transactions on Instrumentation and Measurement, 2020. Cites: paper

Voting of predictive models for clinical outcomes: consensus of algorithms for the early prediction of sepsis from clinical data and an analysis of the PhysioNet/Computing in Cardiology Challenge 2019
Matthew A. Reyna, Gari D. Clifford. arXiv (Cornell University), 2020. Cites: paper

Addressing Noise and Skewness in Interpretable Health-Condition Assessment by Learning Model Confidence
Yuxi Zhou, Shenda Hong, Junyuan Shang et al. Sensors, 2020. Cites: paper

An Accurate CNN Architecture For Atrial Fibrillation Detection Using Neural Architecture Search
Najmeh Fayyazifar. European Signal Processing Conference (EUSIPCO), 2020. Cites: paper

Atrial fibrillation detection using heart rate variability and atrial activity: A hybrid approach
Gerald Hirsch, Søren H. Jensen, Erik S. Poulsen et al. Expert Systems with Applications, 2020. Cites: paper

Weakly Supervised Arrhythmia Detection Based on Deep Convolutional Neural Network
Yang Liu, Kuanquan Wang, Qince Li et al. arXiv (Cornell University), 2020. Cites: paper

Anomaly Detection in Time Series with Triadic Motif Fields and Application in Atrial Fibrillation ECG Classification
Yadong Zhang, Xin Chen. arXiv (Cornell University), 2020. Cites: paper

Remote Atrial Fibrillation Burden Estimation Using Deep Recurrent Neural Network
Armand Chocron, Julien Oster, Shany Brimer Biton et al. IEEE Transactions on Biomedical Engineering, 2020. Cites: paper

Explainable artificial intelligence to detect atrial fibrillation using electrocardiogram
Yong‐Yeon Jo, Younghoon Cho, Soo Youn Lee et al. International Journal of Cardiology, 2020. Cites: paper

A review of Hidden Markov models and Recurrent Neural Networks for event detection and localization in biomedical signals
Yassin Khalifa, Danilo P. Mandic, Ervin Sejdić. Information Fusion, 2020. Cites: paper

Hard-Label Black-Box Adversarial Attack on Deep Electrocardiogram Classifier
Jonathan Lam, Pengrui Quan, Jiamin Xu et al. 2020. Cites: paper

Optimal DNN architecture search using Bayesian Optimization Hyperband for arrhythmia detection
Seungwoo Han, Heesang Eom, Juhyeong Kim et al. IEEE Wireless Power Transfer Conference, 2020. Cites: paper

Wearable Sensors for Monitoring and Preventing Noncommunicable Diseases: A Systematic Review
Annica Kristoffersson, María Lindén. Information, 2020. Cites: paper

An Efficient ECG Classification System Using Resource-Saving Architecture and Random Forest
Bo-Han Kung, Po-Yuan Hu, Chiu-Chang Huang et al. IEEE Journal of Biomedical and Health Informatics, 2020. Cites: paper

POSTER: Atrial Fibrillation Detection Using a Double-Layer Bi-Directional LSTM Neural Networks
Mona N. Alsaleem, Md Saiful Islam. International Conference of Smart Systems and Emerging Technologies (SMARTTECH), 2020. Cites: paper

ViVA: Semi-Supervised Visualization via Variational Autoencoders
Sungtae An, Shenda Hong, Jimeng Sun. Proceedings, 2020. Cites: paper

Machine Learning Based Decision Support System for Atrial Fibrillation Detection using Electrocardiogram
Shrikanth Rao S.K, Roshan Joy Martis. IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER), 2020. Cites: paper

Comparison of Pre-Trained Deep Learning Algorithms for Quality Assessment of Electrocardiographic Recordings
Álvaro Huerta Herráiz, Arturo Martínez‐Rodrigo, Alberto Puchol et al. International Conference on e-Health and Bioengineering (EHB), 2020. Cites: paper

Automated Smart Home Assessment to Support Pain Management: Multiple Methods Analysis
Roschelle Fritz, Marian Wilson, Gordana Dermody et al. Journal of Medical Internet Research, 2020. Cites: paper

A Novel Approach to Classify Electrocardiogram Signals Using Deep Neural Networks
Tasnim Ahmed, Ariq Rahman, Tareque Mohmud Chowdhury et al. 2020. Cites: paper

Multiscale convolutional neural network for detecting paroxysmal atrial fibrillation from single lead ECG signals
Eedara Prabhakararao, Samarendra Dandapat. IEEE Applied Signal Processing Conference (ASPCON), 2020. Cites: paper

DDxNet: a deep learning model for automatic interpretation of electronic health records, electrocardiograms and electroencephalograms
Jayaraman J. Thiagarajan, Deepta Rajan, Sameeksha Katoch et al. Scientific Reports, 2020. Cites: paper

Deep Learning Algorithm Classifies Heartbeat Events Based on Electrocardiogram Signals
Yongbo Liang, Shimin Yin, Qunfeng Tang et al. Frontiers in Physiology, 2020. Cites: paper

Federated Learning on Clinical Benchmark Data: Performance Assessment
Geun Hyeong Lee, Soo-Yong Shin. Journal of Medical Internet Research, 2020. Cites: paper

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