Publications citing Classification of 12-lead ECGs: The PhysioNet/Computing in Cardiology Challenge 2020 v1.0.2

Generation of Synthetic ECG Signal Using Generative Adversarial Network With Transformers
Hanamanth S Kaleli, Vasudev Dehalwar. International Conference on Computing, Communication and Networking Technologies (ICCCNT), 2023. Cites: paper

Desarrollo de nuevos marcadores y clasificadores de bajo coste computacional para identificar afecciones cardiacas en registros ECG
Santiago Jiménez Serrano. 2023. Cites: paper

An Efficient Deep Learning Approach for Arrhythmia Classification using 3D Temporal SVCG
Lorenzo Simone, Mauro Giuseppe Camporeale, Nunzia Lomonte et al. IEEE International Conference on Digital Health (ICDH), 2023. Cites: paper

Diagnosing Left Bundle Branch Block in 12-lead Electrocardiogram using Self-Attention Convolutional Neural Networks
Alireza Sadeghi, Alireza Rezaee, Farshid Hajati. medRxiv, 2023. Cites: paper

Practical intelligent diagnostic algorithm for wearable 12-lead ECG via self-supervised learning on large-scale dataset
Jiewei Lai, Huixin Tan, Jinliang Wang et al. Nature Communications, 2023. Cites: paper

Heart Rhythm Abnormal Signal Diagnosis Based on Neural Network Deep Learning
Wang Jiao, Wei Wei. IEEE Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS), 2023. Cites: paper

PTB-XL+, a comprehensive electrocardiographic feature dataset
Nils Strodthoff, Temesgen Mehari, Claudia Nagel et al. Scientific Data, 2023. Cites: paper

Analysis of a Deep Learning Model for 12-Lead ECG Classification Reveals Learned Features Similar to Diagnostic Criteria
Theresa Bender, Jacqueline Michelle Metsch, Dagmar Krefting et al. IEEE Journal of Biomedical and Health Informatics, 2023. Cites: paper

Premature Ventricular Contraction Detection Based on Chebyshev Polynomials and K Nearest Neighbours Classifier
Fatima Guendouzi, Mokhtar Attari. Traitement du signal, 2023. Cites: paper

Automatic Classification Method of Arrhythmias Based on 12-Lead Electrocardiogram
Xiao Yu Yang, Zhong Ji. Sensors, 2023. Cites: paper

Self-Supervised Contrastive Learning for Medical Time Series: A Systematic Review
Ziyu Liu, Azadeh Alavi, Minyi Li et al. Sensors, 2023. Cites: paper

Interpretation of EKG with Image Recognition and Convolutional Neural Networks
Subrat Das, M. B. Epland, Yu Jiang et al. Current Problems in Cardiology, 2023. Cites: paper

Current and Future Use of Artificial Intelligence in Electrocardiography
Manuel Martínez‐Sellés, Manuel Marina Breysse. Journal of Cardiovascular Development and Disease, 2023. Cites: paper

Applications of Self-Supervised Learning to Biomedical Signals: where are we now
Federico Del Pup, Manfredo Atzori. 2023. Cites: paper

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. Cites: paper

Classification of 27 heart abnormalities using 12-lead ECG signals with combined deep learning techniques
Atiaf Ayal Rawi, Murtada Khalafallah Elbashir, Awadallah M. Ahmed. Bulletin of Electrical Engineering and Informatics, 2023. Cites: paper

Evaluation of post-hoc interpretability methods in time-series classification
Hugues Turbé, Mina Bjelogrlic, Christian Lovis et al. Nature Machine Intelligence, 2023. Cites: paper

Detection of Fibrillatory Episodes in Atrial Fibrillation Rhythms via Topology-informed Machine Learning
Paul Samuel Ignacio. 2023. Cites: paper

Spatiotemporal self-supervised representation learning from multi-lead ECG signals
Rui Min Hu, Jie Chen, L. P. Zhou. Biomedical Signal Processing and Control, 2023. Cites: paper

Effective Delineation of ECG Signals for Operative Neural Disease Recognition and Classification
Aashim Dhawan. IEEE International Conference on Innovations in High Speed Communication and Signal Processing (IHCSP), 2023. Cites: paper

sCL-ST: Supervised Contrastive Learning With Semantic Transformations for Multiple Lead ECG Arrhythmia Classification
Duc Le, Sang Truong, Brijesh D. Patel et al. IEEE Journal of Biomedical and Health Informatics, 2023. Cites: paper

Atrial fibrillation classification based on the 2D representation of minimal subset ECG and a non-deep neural network
Hua Zhang, Chengyu Liu, Fangfang Tang et al. Frontiers in Physiology, 2023. Cites: paper

Algorithms for automated diagnosis of cardiovascular diseases based on ECG data: A comprehensive systematic review
Hanna Vitaliyivna Denysyuk, Rui João Pinto, Pedro Miguel Silva et al. Heliyon, 2023. Cites: paper

Deep learning approach to transformer-based arrhythmia classification using scalogram of single-lead ECG
Ji Seung Ryu, Solam Lee, Young Jun Park et al. Diagnostic and Therapeutic Applications of Light in Cardiology, 2023. Cites: paper

Development of Electrocardiogram Signal Generator for Fibrillation Detector
Ponco Siwindarto, Bill Jason, Adharul Muttaqin et al. Journal of Information Technology and Computer Science, 2023. Cites: paper

Using deep convolutional neural networks to predict patients age based on ECGs from an independent test cohort
Bjørn-Jostein Singstad, Belal Tavashi. Proceedings of the Northern Lights Deep Learning Workshop, 2023. Cites: paper

3D ECG display with deep learning approach for identification of cardiac abnormalities from a variable number of leads
Giovanni Bortolan. Physiological Measurement, 2023. Cites: paper

A novel temporal generative adversarial network for electrocardiography anomaly detection
Jing Qin, Fujie Gao, Zumin Wang et al. Artificial Intelligence in Medicine, 2023. Cites: paper

Improving generalization performance of electrocardiogram classification models
Hyeongrok Han, Seongjae Park, Seonwoo Min et al. Physiological Measurement, 2023. Cites: paper

AI and Machine Learning: The Basics
Nicolás Duchateau, Esther Puyol Anton, Bram Ruijsink et al. Springer eBooks, 2023. Cites: paper

Analysis of Non-imaging Data
Nicolás Duchateau, Óscar Cámara, Rafael Sebastián et al. Springer eBooks, 2023. Cites: paper

A Personal Health Agent for Decision Support in Arrhythmia Diagnosis
Tezira Wanyana, Mbithe Nzomo, Catherine Susan Price et al. Communications in computer and information science, 2023. Cites: paper

Applications of Self-Supervised Learning to Biomedical Signals: A Survey
Federico Del Pup, Manfredo Atzori. IEEE Access, 2023. Cites: paper

Diagnosis
Daniel Rueckert, Moritz A. Knolle, Nicolás Duchateau et al. Springer eBooks, 2023. Cites: paper

Entropy-based reliable non-invasive detection of coronary microvascular dysfunction using machine learning algorithm
Xiaoye Zhao, Yinlan Gong, Lihua Xu et al. Mathematical Biosciences & Engineering, 2023. Cites: paper

Joint Optimization of a $$\beta $$-VAE for ECG Task-Specific Feature Extraction
Viktor van der Valk, Douwe E. Atsma, Roderick W.C. Scherptong et al. Lecture notes in computer science, 2023. Cites: paper

Performance improvement of deep learning based multi-class ECG classification model using limited medical dataset
Sanghoon Choi, Hyo-Chang Seo, Min Soo Cho et al. IEEE Access, 2023. Cites: paper

ShapeWordNet: An Interpretable Shapelet Neural Network for Physiological Signal Classification
Wenqiang He, Mingyue Cheng, Qi Liu et al. Lecture notes in computer science, 2023. Cites: paper

Impact of Pre-Processing Decisions on Automated ECG Classification Accuracy
Adrian K. Cornely, Grace Mirsky. Computing in cardiology, 2022. Cites: paper

Interpretable Machine Learning Techniques in ECG-Based Heart Disease Classification: A Systematic Review
Yehualashet Megersa Ayano, Friedhelm Schwenker, Bisrat Derebssa Dufera et al. Diagnostics, 2022. Cites: paper

A joint cross-dimensional contrastive learning framework for 12-lead ECGs and its heterogeneous deployment on SoC
Wenhan Liu, Huaicheng Zhang, Sheng Chang et al. Computers in Biology and Medicine, 2022. Cites: paper

Application of Fourier-Bessel expansion and LSTM on multi-lead ECG for cardiac abnormalities identification
Nidhi Kalidas Sawant, Shivnarayan Patidar. Physiological Measurement, 2022. Cites: paper

A unique cardiac electrocardiographic 3D model. Toward interpretable AI diagnosis
Cristina Rueda, Alejandro Rodríguez-Collado, Itziar Fernández et al. iScience, 2022. Cites: paper

A novel multi-scale 2D CNN with weighted focal loss for arrhythmias detection on varying-dimensional ECGs
Pan Xia, Zhengling He, Zhongrui Bai et al. Physiological Measurement, 2022. Cites: paper

Ultra-low Power Analog Recurrent Neural Network Design Approximation for Wireless Health Monitoring
Yung-Ting Hsieh, Khizar Anjum, Dario Pompili. IEEE International Conference on Mobile Ad-Hoc and Sensor Systems (MASS), 2022. Cites: paper

Multimodality Multi-Lead ECG Arrhythmia Classification using Self-Supervised Learning
Thinh Phan, Duc Le, Brijesh D. Patel et al. IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI ...), 2022. Cites: paper

A novel deep learning package for electrocardiography research
Hao Wen, Jingsu Kang. Physiological Measurement, 2022. Cites: paper

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