Publications citing PTB-XL, a large publicly available electrocardiography dataset v1.0.1

Stochastic co-teaching for training neural networks with unknown levels of label noise
Bob D. de Vos, Gino E. Jansen, Ivana Išgum. Scientific Reports, 2023. Cites: paper

Artificial neural network–based modelling of pacemaker and its pace tracking using discrete wavelet transform–based finite dimension repetitive controller
Rijhi Dey, Rudra Sankar Dhar, Ujjwal Mondal. Transactions of the Institute of Measurement and Control, 2023. Cites: paper

Multi-center atrial fibrillation electrocardiogram (ECG) classification using Fourier space convolutional neural networks (FD-CNN) and transfer learning
Luiz Vasconcelos, Bryan O. Pérez Martínez, Madeline Kent et al. Journal of Electrocardiology, 2023. Cites: paper

ElectroCardioGuard: Preventing patient misidentification in electrocardiogram databases through neural networks
Michal Seják, Jakub Sido, David Žahour. Knowledge-Based Systems, 2023. Cites: paper

MVKT-ECG: Efficient single-lead ECG classification for multi-label arrhythmia by multi-view knowledge transferring
Yuzhen Qin, Li Sun, Hui Chen et al. Computers in Biology and Medicine, 2023. Cites: paper

A token selection-based multi-scale dual-branch CNN-transformer network for 12-lead ECG signal classification
Siyuan Zhang, Cheng Lian, Bingrong Xu et al. Knowledge-Based Systems, 2023. Cites: paper

Classification of cardiac disorders using weighted visibility graph features from ECG signals
Gokhan Kutluana, İlker Türker. Biomedical Signal Processing and Control, 2023. Cites: paper

ECG-CL: A Comprehensive Electrocardiogram Interpretation Method Based on Continual Learning
Hongxiang Gao, Xingyao Wang, Zhenghua Chen et al. IEEE Journal of Biomedical and Health Informatics, 2023. Cites: paper

SwinDAE: Electrocardiogram Quality Assessment Using 1D Swin Transformer and Denoising AutoEncoder
Guanyu Chen, Tianyi Shi, Baoxing Xie et al. IEEE Journal of Biomedical and Health Informatics, 2023. Cites: paper

IM-ECG: An interpretable framework for arrhythmia detection using multi-lead ECG
Rui Tao, Lin Wang, Yingnan Xiong et al. Expert Systems with Applications, 2023. Cites: paper

Completing the Cabrera Circle: deriving adaptable leads from ECG limb leads by combining constraints with a correction factor
Henning Dathe, Dagmar Krefting, Nicolai Spicher. Physiological Measurement, 2023. Cites: paper

Electrocardiographic temporo-spatial assessment of depolarization and repolarization changes after epicardial arrhythmogenic substrate ablation in Brugada syndrome
Emanuela H. Locati, Peter Michael van Dam, Giuseppe Ciconte et al. European Heart Journal - Digital Health, 2023. Cites: paper

Augmentation Strategies for Self-Supervised Representation Learning from Electrocardiograms
Matilda Andersson, Mattias Nilsson, Gabrielle Flood et al. European Signal Processing Conference (EUSIPCO), 2023. Cites: paper

A resource-efficient ECG diagnosis model for mobile health devices
Rui Tao, Lin Wang, Binrong Wu. Information Sciences, 2023. Cites: paper

Comparative Study of Machine Learning Algorithms in Classifying HRV for the Driver’s Physiological Condition
Abdul Razak, Sharifah Noor Masidayu Sayed Ismail, Sumendra Yogarayan et al. Civil Engineering Journal, 2023. Cites: paper

Towards Quantitative Precision for ECG Analysis: Leveraging State Space Models, Self-Supervision and Patient Metadata
Temesgen Mehari, Nils Strodthoff. IEEE Journal of Biomedical and Health Informatics, 2023. Cites: paper

Studying the Impact of Varying Sample Length of ECG Signal on Classification Accuracy
Hassan Raza Bukhari, Yongqiang Cheng, Qingde Li. International Conference on Automation and Computing (ICAC), 2023. Cites: paper

Towards Domain Generalization for ECG and EEG Classification: Algorithms and Benchmarks
Aristotelis Ballas, Christos Diou. IEEE Transactions on Emerging Topics in Computational Intelligence, 2023. Cites: paper

Using domain adaptation for classification of healthy and disease conditions from mobile-captured images of standard 12-lead electrocardiograms
Вадим Глинер, Vladimir V. Makarov, Arutyun Ishkhanovich Avetisyan et al. Scientific Reports, 2023. Cites: paper

Evaluation of uncertainty quantification methods in multi-label classification: A case study with automatic diagnosis of electrocardiogram
Marília Barandas, Lorenzo Famiglini, Andrea Campagner et al. Information Fusion, 2023. Cites: paper

Love or Hate? Share or Split? Privacy-Preserving Training Using Split Learning and Homomorphic Encryption
Tanveer Khan, Khoa Nguyen, Antonis Michalas et al. International Conference on Privacy, Security and Trust (PST), 2023. Cites: paper

A structure for extracting features of electrocardiogram signals
Qingyu Yao, Xuesong Su, Siyuan Li et al. International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE), 2023. Cites: dataset, paper

Automatic classification and detection of 12-lead electrocardiogram signal classification with Fourier convolutions
Siyuan Li, Xuesong Su, Qingyu Yao et al. International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE), 2023. Cites: dataset

Artificial Intelligence-Driven Screening System for Rapid Classification of 12-Lead ECG Exams: A Promising Solution for Emergency Room Prioritization
Felipe Meneguitti Dias, Estela Ribeiro, ramon moreno et al. 2023. Cites: paper

Generative adversarial networks in electrocardiogram synthesis: Recent developments and challenges
Laurenz Berger, Max Haberbusch, Francesco Moscato. Artificial Intelligence in Medicine, 2023. Cites: paper

MedalCare-XL: 16,900 healthy and pathological synthetic 12 lead ECGs from electrophysiological simulations
Karli K. Gillette, Matthias A. F. Gsell, Claudia Nagel et al. Scientific Data, 2023. Cites: paper

DConv-LSTM-Net: A Novel Architecture for Single- and 12-Lead ECG Anomaly Detection
Theekshana Dissanayake, Tharindu Fernando, Simon Denman et al. IEEE Sensors Journal, 2023. Cites: paper

Self-supervised Classification of Clinical Multivariate Time Series using Time Series Dynamics
Yakir Yehuda, Daniel Freedman, Kira Radinsky. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2023. Cites: paper

A Dual-Scale Lead-Separated Transformer for ECG Classification
Yang Li, Guijin Wang, Zhourui Xia et al. Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2023. Cites: paper

AF automatic classification based on different time-delay values of the recurrence plot
Hua Zhang, Chengyu Liu, Fangfang Tang et al. Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2023. Cites: paper

Automated Myocardial Infarction Screening Using Morphology-Based Electrocardiogram Biomarkers
Dhaladhuli Jahnavi, Ashutosh Kumar Dash, Nirmalya Ghosh et al. Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2023. Cites: paper

SF-ECG: Source-Free Intersubject Domain Adaptation for Electrocardiography-Based Arrhythmia Classification
Taki Hasan Rafi, Young Woong Ko. Applied Sciences, 2023. Cites: paper

Signal quality indices evaluation for robust ECG signal quality assessment systems
Fotsing Kuetche, Noura Alexendre, Ntsama Eloundou Pascal et al. Biomedical Physics & Engineering Express, 2023. Cites: paper

Self-Supervised Time Series Representation Learning via Cross Reconstruction Transformer
Wenrui Zhang, Ling Qiang Yang, Shijia Geng et al. IEEE Transactions on Neural Networks and Learning Systems, 2023. Cites: paper

A framework for comparative study of databases and computational methods for arrhythmia detection from single-lead ECG
Elena Merdjanovska, Aleksandra Rashkovska. Scientific Reports, 2023. Cites: paper

Convolutional block attention autoencoder for denoising electrocardiograms
Wesley Chorney, Haifeng Wang, Lu He et al. Biomedical Signal Processing and Control, 2023. Cites: paper

Adversarial Spatiotemporal Contrastive Learning for Electrocardiogram Signals
Ning Wang, Panpan Feng, Zhaoyang Ge et al. IEEE Transactions on Neural Networks and Learning Systems, 2023. Cites: paper

Contribution to the prediction, detection and classification of atrial fibrillation : processing of ECG signals using temporal and frequency methods and contribution of artificial intelligence : Early prediction of atrial fibrillation
Hassan Serhal. 2023. Cites: paper

Usefulness of Heat Map Explanations for Deep-Learning-Based Electrocardiogram Analysis
Andrea Marheim Storås, Ole Emil Andersen, Sam M. Lockhart et al. Diagnostics, 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

Electrocardiogram monitoring as a predictor of neurological and survival outcomes in patients with out-of-hospital cardiac arrest: a single-center retrospective observational study
Masaki Takahashi, Kentaro Ogura, Tadahiro Goto et al. Frontiers in Neurology, 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

Empirical Study of Mix-based Data Augmentation Methods in Physiological Time Series Data
Peikun Guo, Huiyuan Yang, Akane Sano. IEEE International Conference on Healthcare Informatics (ICHI), 2023. Cites: paper

Twelve Lead Double Stacked Generalization for ECG Classification
Sagnik Dakshit, Balakrishnan Prabhakaran. IEEE International Conference on Healthcare Informatics (ICHI), 2023. Cites: paper

A unified framework for multi-lead ECG characterization using Laplacian Eigenmaps
Amalia Villa, Sebastian Ingelaere, Ben Jacobs et al. Physiological Measurement, 2023. Cites: paper

Single-Lead to Multi-Lead Electrocardiogram Reconstruction Using a Modified Attention U-Net Framework
Akshit Garg, Vijay Vignesh Venkataramani, U. Deva Priyakumar. International Joint Conference on Neural Networks (IJCNN), 2023. Cites: paper

Analysis of ECG Noise Cancellation and Abnormality Classification using Various Adaptive Algorithms
S M Ramesh, S. Nithya, V. V. Teresa et al. International Conference on Applied Intelligence and Sustainable Computing (ICAISC), 2023. Cites: paper

Detection of Myocardial Infarction Using Hybrid Models of Convolutional Neural Network and Recurrent Neural Network
Sumayyah Hasbullah, Mohd Soperi Mohd Zahid, Satria Mandala. BioMedInformatics, 2023. Cites: paper

Remote Heart Diagnostic System
Emma Miller, Edward Armitage, Farzan Aminian et al. International Journal on Engineering Science and Technology, 2023. Cites: dataset, paper

Cited by