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Paroxysmal Atrial Fibrillation Events Detection from Dynamic ECG Recordings: The 4th China Physiological Signal Challenge 2021 (v1.0.0)

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33 publications cite any version of this project, newest first.

• Gml-PAF: A Generalizable Machine Learning Algorithm for Paroxysmal Atrial Fibrillation Detection based on Short-Term Inter-Beat Intervals
  Yongjun Song, Jihui Fan, Zikun Yang et al. Computer Methods in Biomechanics & Biomedical Engineering, 2026.
• Federated Learning Strategies for Atrial Fibrillation Detection
  Wesley Chorney, Sing Hui Ling. Journal of Experimental and Theoretical Analyses, 2025.
• Detection of Bundle Branch Block From 12-Lead ECG Using Fifth-Order Tensor-Domain Machine Learning
  Chhaviraj Chauhan, Rajesh Kumar Tripathy, Monika Agrawal. IEEE Transactions on Human-Machine Systems, 2025.
• Multimodal Time-Frequency Pseudo Anomalies for Atrial Fibrillation Anomaly Detection
  Haoyi Fan, Han Han, Huihui Chang et al. IEEE Journal of Biomedical and Health Informatics, 2025.
• A Multi-Level Multiple Contrastive Learning Method for Single-Lead Electrocardiogram Atrial Fibrillation Detection
  Yonggang Zou, Peng Wang, Lidong Du et al. Bioengineering, 2025.
• Self-Supervised ECG Anomaly Detection Based on Time-Frequency Specific Waveform Mask Feature Fusion
  Chongrui Tian, Fengbin Zhang. IEEE Access, 2025.
• A deep learning method for beat-level risk analysis and interpretation of atrial fibrillation patients during sinus rhythm
  Jun Lei, Yuxi Zhou, Xue Tian et al. Biomedical Signal Processing and Control, 2024.
• Transfer Learning Based Paroxysmal Atrial Fibrillation Classification Using Continuous Wavelet Transform Scalogram
  Safkat Shahrier Swapnil, Md. Nuhi-Alamin, Ajay Krishno Sarkar et al. IEEE International Conference on Power, Electrical, Electronics and Industrial Applications (PEEIACON), 2024.
• SeqAFNet: A Beat-Wise Sequential Neural Network for Atrial Fibrillation Classification in Adhesive Patch-Type Electrocardiographs
  Sangkyu Kim, Jiwoo Lim, Jaeseong Jang. IEEE Journal of Biomedical and Health Informatics, 2024.
• Difference Poincaré Image Feature-Based Persistent Atrial Fibrillation Classification Using Short-Term Electrocardiograms
  Md Mayenul Islam, Sumaiya Kabir, Mohammod Abdul Motin. IEEE Sensors Letters, 2024.
• Periodic Distribution Entropy: Unveiling the complexity of physiological time series through multidimensional dynamics
  Yu Xiao, Weimin Li, Bing Jun Yang et al. Information Fusion, 2024.
• A Q-transform-based deep learning model for the classification of atrial fibrillation types
  B. Dhananjay, R. Pradeep Kumar, Bala Chakravarthy Neelapu et al. Physical and Engineering Sciences in Medicine, 2024.
• Directional statistics-inspired end-to-end atrial fibrillation detection model based on ECG rhythm
  Chengsi Luo, Kaixuan Zhang, Yeting Hu et al. Expert Systems with Applications, 2024.
• An Automated Approach for Atrial Fibrillation Detection Using Electrocardiogram
  Md Mayenul Islam, Mohammod Abdul Motin. International Conference on Computer and Information Technology (ICCIT), 2023.
• A generalizable and robust deep learning method for atrial fibrillation detection from long-term electrocardiogram
  Yonggang Zou, Xianya Yu, Siying Li et al. Biomedical Signal Processing and Control, 2023.
• An Atrial Fibrillation Detection Strategy in Dynamic ECGs With Significant Individual Differences
  Caiyun Ma, Xi Long, Weijie Sheng et al. IEEE Transactions on Instrumentation and Measurement, 2023.
• Automatic segmentation of atrial fibrillation and flutter in single-lead electrocardiograms by self-supervised learning and Transformer architecture
  Donghwan Yun, Hyun-Lim Yang, Soonil Kwon et al. Journal of the American Medical Informatics Association, 2023.
• SEResUTer: a deep learning approach for accurate ECG signal delineation and atrial fibrillation detection
  Xinyue Li, Wenjie Cai, Bolin Xu et al. Physiological Measurement, 2023.
• A Review on Atrial Fibrillation Detection From Ambulatory ECG
  Caiyun Ma, Zhijun Xiao, Lina Zhao et al. IEEE Transactions on Biomedical Engineering, 2023.
• Detection of Paroxysmal Atrial Fibrillation from Dynamic ECG Recordings Based on a Deep Learning Model
  Yating Hu, Tengfei Feng, Miao Wang et al. Journal of Personalized Medicine, 2023.
• Novel HRV Metrics using Analytic Signal Representation of Interbeat Intervals for New Insights on Cardiac Dynamics
  Sawon Pratiher, Apoorva Srivastava, Mrinal Acharya et al. 2023.
• Automatic Identification and Location of Paroxysmal Atrial Fibrillation Based on Single Heartbeat from Dynamic Electrocardiogram
  Bailing Zhang, Shaochang Wang, Yi Xin et al. International Conference on Computing and Artificial Intelligence (ICCAI), 2023.
• Generalizable and robust deep learning algorithm for atrial fibrillation diagnosis across geography, ages and sexes
  Shany Brimer Biton, Mohsin Aldhafeeri, Erez Marcusohn et al. npj Digital Medicine, 2023.
• Detection of Fibrillatory Episodes in Atrial Fibrillation Rhythms via Topology-informed Machine Learning
  Paul Samuel Ignacio. 2023.
• A Deep Learning Architecture Using 3D Vectorcardiogram to Detect R-Peaks in ECG with Enhanced Precision
  Maroua Mehri, Guillaume Calmon, Freddy Odille et al. Sensors, 2023.
• Deviation distance entropy: A method for quantifying the dynamic features of biomedical time series
  Yu Xiao, Weimin Li, Bing Jun Yang et al. Chaos Solitons & Fractals, 2023.
• Atrial fibrillation classification and detection from ECG recordings
  Ali Fatih Gündüz, Muhammed Fatih Talu. Biomedical Signal Processing and Control, 2022.
• A comparative study on neural networks for paroxysmal atrial fibrillation events detection from electrocardiography
  Hao Wen, Jingsu Kang. Journal of Electrocardiology, 2022.
• Percept U-Net: Percept Attention-based Convolutional Neural Network for Atrial Fibrillation Episode Localization
  Ying An, Lebing Pan, Lin Guo et al. IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2022.
• A novel deep learning package for electrocardiography research
  Hao Wen, Jingsu Kang. Physiological Measurement, 2022.
• A Multistep Paroxysmal Atrial Fibrillation Scanning Strategy in Long-Term ECGs
  Caiyun Ma, Chengyu Liu, Xingyao Wang et al. IEEE Transactions on Instrumentation and Measurement, 2022.
• A two-step method for paroxysmal atrial fibrillation event detection based on machine learning
  Yanan Wang, Sen Liu, Haijun Jia et al. Mathematical Biosciences & Engineering, 2022.
• Paroxysmal Atrial Fibrillation Events Detection from Dynamic ECG Recordings - The 4th China Physiological Signal Challenge 2021
  Francisco Bischoff. Zenodo (CERN European Organization for Nuclear Research), 2021.

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