Cited by

St Petersburg INCART 12-lead Arrhythmia Database (v1.0.0)

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

• Physiology-augmented deep representations improve cross-database electrocardiogram beat classification under unseen-domain shift
  Mujahid Shahid¹, Oliver Konyo. Research Square, 2026.
• An OMOP Common Data Model pipeline for electrocardiography dataset integration by standardizing signal metadata and heart rate variability features
  Pierluigi Reali, Valentina Tibollo, Gaia Riboni et al. Frontiers in Bioinformatics, 2026.
• Comparing subset selection methods in multi-lead electrocardiogram data
  Kassidy Crockett, Autumn Langer, Tyler Cook et al. Network Modeling Analysis in Health Informatics and Bioinformatics, 2026.
• Assessing the Impact of Downsampled ECGs and Alternative Loss Functions in Multi-Label Classification of 12-Lead ECGs
  Bjørn-Jostein Singstad, Eraraya Morenzo Muten. Cardiovascular Engineering and Technology, 2025.
• ECG Arrhythmia Detector with Custom Designed Wavelet-Based Convolutional Autoencoder for Unbalanced Data
  Öykü Eravcı, Çağla Sarvan, Nalan Özkurt. Innovations in Intelligent Systems and Applications Conference (ASYU), 2025.
• A Comparative Analysis of Advanced Deep Learning Techniques for Accurate Cardiac Arrhythmia Classification
  Anniah Pratima, K. Gopalakrishna, Sarappadi Narasimha Prasad. Engineering Technology & Applied Science Research, 2025.
• A survey of transformers and large language models for ECG diagnosis: advances, challenges, and future directions
  Mohammed Yusuf Ansari, Mohammed Yaqoob, Mohammed Ishaq et al. Artificial Intelligence Review, 2025.
• Multi-Resolution Autonomous Linear State Space Filters for N-Dimensional Signals
  Christof Baeriswyl, Frédéric Waldmann, Alexander Bertrand et al. IEEE Transactions on Signal Processing, 2025.
• Artificial Intelligence Techniques for Cardiovascular Disease Classification using 12-Lead ECG
  Younes Hadzine, Atman Jbari, Mohamed Najoui et al. International Conference on Embedded Systems and Artificial Intelligence (ESAI), 2024.
• Coronary Artery Disease Classification Using One-dimensional Convolutional Neural Network
  Atitaya Phoemsuk, Vahid Abolghasemi. IEEE Conference on Artificial Intelligence (CAI), 2024.
• A Multi-View learning approach to enhance automatic 12-Lead ECG diagnosis performance
  Jae-Won Choi, Dae-Yong Hong, Chan Jung et al. Biomedical Signal Processing and Control, 2024.
• Reduced lead ECG multi-label classification with higher generalization using 2D SEResnets with self attention
  Danish Sheikh, Himanshu Verma, Naveen Chauhan. Multimedia Tools and Applications, 2024.
• Arrhythmia Detection with Custom Designed Wavelet-based Convolutional Autoencoder
  Öykü Eravcı, Nalan Özkurt. International Conference on Innovations in Intelligent Systems and Applications (INISTA), 2023.
• A framework for comparative study of databases and computational methods for arrhythmia detection from single-lead ECG
  Elena Merdjanovska, Aleksandra Rashkovska. Scientific Reports, 2023.
• Desarrollo de nuevos marcadores y clasificadores de bajo coste computacional para identificar afecciones cardiacas en registros ECG
  Santiago Jiménez Serrano. 2023.
• 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.
• 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.
• 3D ECG display with deep learning approach for identification of cardiac abnormalities from a variable number of leads
  Giovanni Bortolan. Physiological Measurement, 2023.
• 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.
• A Domain Generalization Approach for Out-Of-Distribution 12-lead ECG Classification with Convolutional Neural Networks
  Aristotelis Ballas, Christos Diou. IEEE International Conference on Big Data Computing Service and Applications (BigDataService), 2022.
• A clinical study on Atrial Fibrillation, Premature Ventricular Contraction, and Premature Atrial Contraction screening based on an ECG deep learning model
  Jianyuan Hong, Hua‐Jung Li, Chung‐Chi Yang et al. Applied Soft Computing, 2022.
• A large-scale multi-label 12-lead electrocardiogram database with standardized diagnostic statements
  Hui Liu, Dan Chen, Da Chen et al. Scientific Data, 2022.
• Multi-label classification of reduced-lead ECGs using an interpretable deep convolutional neural network
  Nima L. Wickramasinghe, Mohamed Athif. Physiological Measurement, 2022.
• From 12 to 1 ECG lead: multiple cardiac condition detection mixing a hybrid machine learning approach with a one-versus-rest classification strategy
  Santiago Jiménez-Serrano, Miguel Rodrigo, Conrado Javier Calvo et al. Physiological Measurement, 2022.
• Classification of cardiac arrhythmia using a convolutional neural network and bi-directional long short-term memory
  Shahab Ul Hassan, Mohd Soperi Mohd Zahid, Talal A. A. Abdullah et al. Digital Health, 2022.
• Norwegian Endurance Athlete ECG Database
  Bjørn-Jostein Singstad. IEEE Open Journal of Engineering in Medicine and Biology, 2022.
• Machine learning methods as a tool for diagnostic and prognostic research in cardiovascular disease
  Charos Khidirova, Shakhnoza Sadikova, Shamil Mukhsinov et al. International Conference on Information Science and Communications Technologies (ICISCT), 2021.
• Cross-Database Generalization of Deep Learning Models for Arrhythmia Classification
  Elena Merdjanovska, Aleksandra Rashkovska. International Convention on Information and Communication Technology, Electronics and Microelectronics, 2021.
• 3-D ECG images with Deep Learning Approach for Identification of Cardiac Abnormalities from a Variable Number of Leads
  Giovanni Bortolan. Computing in cardiology, 2021.
• An Ensemble Learning Approach to Detect Cardiac Abnormalities in ECG Data Irrespective of Lead Availability
  Tim Uhlemann, Joshua Prim, Nils Gumpfer et al. Computing in cardiology, 2021.
• A Novel Multi-Scale Convolutional Neural Network for Arrhythmia Classification on Reduced-Lead ECGs
  Pan Xia, Zhengling He, Yusi Zhu et al. Computing in cardiology, 2021.
• Automated Diagnosis of Reduced-Lead Electrocardiograms Using a Shared Classifier
  HT Jessen, Rutger R van de Leur, Pieter A. Doevendans et al. Computing in cardiology, 2021.
• Cardiac Abnormalities Recognition in ECG Using a Convolutional Network with Attention and Input with an Adaptable Number of Leads
  Tomáš Vičar, Petra Novotná, Jakub Hejč et al. Computing in cardiology, 2021.
• Combining ResNet Model with Handcrafted Temporal Features for ECG Classification with Varying Number of Leads
  Stefano Magni, Andrea Sansonetti, Chiara Salvi et al. Computing in cardiology, 2021.
• Convolution-Free Waveform Transformers for Multi-Lead ECG Classification
  Annamalai Natarajan, Gregory Boverman, Yale Chang et al. Computing in cardiology, 2021.
• Hybrid Arrhythmia Detection on Varying-Dimensional Electrocardiography: Combining Deep Neural Networks and Clinical Rules
  Hao Wen, Jingsu Kang. Computing in cardiology, 2021.
• Leveraging Period-Specific Variations in ECG Topology for Classification Tasks
  Paul Samuel Ignacio. Computing in cardiology, 2021.
• Multi-Label Cardiac Abnormality Classification from Electrocardiogram Using Deep Convolutional Neural Networks
  Nima L. Wickramasinghe, Mohamed Athif. Computing in cardiology, 2021.
• Reduced-Lead ECG Classifier Model Trained with DivideMix and Model Ensemble
  Hiroshi Seki, T. Nakano, Koshiro Ikeda et al. Computing in cardiology, 2021.
• Semi-Supervised Learning for ECG Classification
  Rui André Rodrigues, Paula Couto. Computing in cardiology, 2021.
• Towards High Generalization Performance on Electrocardiogram Classification
  Hyeongrok Han, Seongjae Park, Seonwoo Min et al. Computing in cardiology, 2021.
• Using Mel-Frequency Cepstrum and Amplitude-Time Heart Variability as XGBoost Handcrafted Features for Heart Disease Detection
  SS Krivenko, Anatolii A. Pulavskyi, Sergii Kryvenko et al. Computing in cardiology, 2021.
• Will Two Do? Varying Dimensions in Electrocardiography: The PhysioNet/Computing in Cardiology Challenge 2021
  Matthew A. Reyna, Nadi Sadr, Erick Andres Perez Alday et al. Computing in cardiology, 2021.
• Classification of 12-lead ECGs: the PhysioNet/Computing in Cardiology Challenge 2020
  Matthew A. Reyna, Erick Andres Perez Alday, Annie Gu et al. Computing in cardiology, 2020.
• Machine learning as a tool for diagnostic and prognostic research in coronary artery disease
  B. I. Geltser, Циванюк Михаил Михайлович, Karina I. Shakhgeldyan et al. Russian Journal of Cardiology, 2020.
• Classification of 12-lead ECGs: the PhysioNet/Computing in Cardiology Challenge 2020
  Erick Andres Perez Alday, Annie Gu, Amit Jasvant Shah et al. Physiological Measurement, 2020.
• A Neural-Network Model for Deriving Analogous Three-vector ECGs of S-ICD from Standard Nine-lead ECGs
  Xiaobiao Fan, Yuxiang Bu, Dakun Lai. International Conference on Image Processing and Machine Vision (IPMV), 2020.
• Recognition of ECG signals using wavelet based on atomic functions
  Andres Hernandez-Matamoros, Hamido Fujita, Enrique Escamilla-Hernández et al. Journal of Applied Biomedicine, 2020.

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