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

Hyperparameter Tuning of Dense Neural Network for ECG Signal Classification
S. Clement Virgeniya, E. Ramaraj. Algorithms for intelligent systems, 2022. Cites: paper

Multilabel 12-Lead ECG Classification Based on Leadwise Grouping Multibranch Network
Xiaoyun Xie, Hui Liu, Da Chen et al. IEEE Transactions on Instrumentation and Measurement, 2022. Cites: paper

Norwegian Endurance Athlete ECG Database
Bjørn-Jostein Singstad. IEEE Open Journal of Engineering in Medicine and Biology, 2022. Cites: paper

Personalized decision support for cardiology based on deep learning: an overview
Ling Chen, Vincent S. M. Tseng, HSUAN‐MING TSAO et al. Elsevier eBooks, 2022. Cites: paper

Simulation of ECG for Cardiac Diseases Using Generative Adversarial Networks
Kohki Nakane, Tatsuki Kawai, Rintaro Sugie et al. Lecture notes in computer science, 2022. Cites: dataset

Toward Automated Feature Extraction for Deep Learning Classification of Electrocardiogram Signals
Fatima Sajid Butt, Matthias Wagner, Jörg Schäfer et al. IEEE Access, 2022. Cites: paper

Transformer-based Neural Network for Electrocardiogram Classification
Mohammed A. Atiea, Mark Adel. International Journal of Advanced Computer Science and Applications, 2022. Cites: paper

EvoMBN: Evolving Multi-Branch Networks on Myocardial Infarction Diagnosis Using 12-Lead Electrocardiograms
Wenhan Liu, Jiewei Ji, Sheng Chang et al. Biosensors, 2021. Cites: paper

Multi-Scale Convolutional Neural Network Ensemble for Multi-Class Arrhythmia Classification
Eedara Prabhakararao, Samarendra Dandapat. IEEE Journal of Biomedical and Health Informatics, 2021. Cites: paper

Self-supervised representation learning from 12-lead ECG data
Temesgen Mehari, Nils Strodthoff. Computers in Biology and Medicine, 2021. Cites: paper

IoT and Big Data for ECG Signal Classification - A Quick Decision System
S. Clement Virgeniya, E. Ramaraj. International Conference on Communication, Computing and Industry 4.0 (C2I4), 2021. Cites: paper

X-ECGNet: An Interpretable DL model for Stress Detection using ECG in COVID-19 Healthcare Workers
Anubha Gupta, Deepankar Kansal, Vandana Gupta et al. International Conference on Bio-engineering for Smart Technologies (BioSMART), 2021. Cites: paper

Deep Learning Techniques in the Classification of ECG Signals Using R-Peak Detection Based on the PTB-XL Dataset
Sandra Śmigiel, Krzysztof Pałczyński, Damian Ledziński. Sensors, 2021. Cites: paper

Automated Deep Learning Based Cardiovascular Disease Diagnosis Using ECG Signals
Sajja Karthik, M. Santhosh, M. S. Kavitha et al. Computer Systems Science and Engineering, 2021. Cites: paper

BaT: Beat-aligned Transformer for Electrocardiogram Classification
Xiaoyu Li, Chen Li, Yuhua Wei et al. Proceedings, 2021. Cites: paper

Enhanced Transfer Learning Through Medical Imaging and Patient Demographic Data Fusion
Spencer A. Thomas. arXiv (Cornell University), 2021. Cites: paper

ECGsound for human identification
Carmen Cámara, Pedro Peris Lopez, Masoumeh Safkhani et al. Biomedical Signal Processing and Control, 2021. Cites: paper

Automatic Multi-Label ECG Classification with Category Imbalance and Cost-Sensitive Thresholding
Yang Liu, Qince Li, Kuanquan Wang et al. Biosensors, 2021. Cites: paper

Intelligent Biomedical Electrocardiogram Signal Processing for Cardiovascular Disease Diagnosis
Jin Wang, Chenchen Han, Xiaofeng Yu et al. Computers, materials & continua/Computers, materials & continua (Print), 2021. Cites: paper

Machine Learning for Health: Algorithm Auditing & Quality Control
Luis Oala, Andrew G. Murchison, Pradeep Balachandran et al. Journal of Medical Systems, 2021. Cites: paper

Meta-Learning to Improve Pre-Training
Aniruddh Raghu, Jonathan Lorraine, Simon Kornblith et al. arXiv (Cornell University), 2021. Cites: paper

Edge computing in 5G cellular networks for real-time analysis of electrocardiography recorded with wearable textile sensors
Nicolai Spicher, Arne Klingenberg, Valentin Purrucker et al. Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2021. Cites: paper

Improving Automatic Detection of ECG Abnormality with Less Manual Annotations using Siamese Network
Fan Yang, Guijin Wang, Chuankai Luo et al. Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2021. Cites: paper

Self-Supervised Learning with Electrocardiogram Delineation for Arrhythmia Detection
Byeong Tak Lee, Seo Taek Kong, Youngjae Song et al. Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2021. Cites: paper

Spatial Feature Extraction of Vectorcardiography via Minimum Volume Ellipsoid Enclosure in Classifying Left Ventricular Hypertrophy
Yasuyuki Kataoka, Hitonobu Tomoike. Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2021. Cites: paper

What will we ask to artificial intelligence for cardiovascular medicine in the next decade?
Guglielmo Gallone, Francesco BRUNO, Fabrizio D’ASCENZO et al. Minerva Cardiology and Angiology, 2021. Cites: paper

SIM-ECG: A Signal Importance Mask-driven ECGClassification System
Dharma Kc, Chicheng Zhang, Chris Gniady et al. arXiv (Cornell University), 2021. Cites: paper

Automated Learning of ECG Streaming Data Through Machine Learning Internet of Things
Mwaffaq Abu-Alhaija, Nidal M. Turab. Intelligent Automation & Soft Computing, 2021. Cites: paper

IMLE-Net: An Interpretable Multi-level Multi-channel Model for ECG Classification
Likith Reddy, Vivek Talwar, Shanmukh Alle et al. Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics, 2021. Cites: paper

Classification of Bundle Branch Blocks with QRS Templates Extracted from 12-lead ECGs
Hannes Welle, Claudia Nagel, Axel Loewe et al. Current Directions in Biomedical Engineering, 2021. Cites: dataset

Real-time frequency-independent single-Lead and single-beat myocardial infarction detection
Harold Martin, Ulyana Morar, Walter Izquierdo et al. Artificial Intelligence in Medicine, 2021. Cites: paper

Symmetric Projection Attractor Reconstruction: Sex Differences in the ECG
Jane V. Lyle, Manasi Nandi, Philip J. Aston. Frontiers in Cardiovascular Medicine, 2021. Cites: paper

Potential of Rule-Based Methods and Deep Learning Architectures for ECG Diagnostics
Giovanni Bortolan, Ivaylo Ivanov Christov, Iana Ivaylova Simova. Diagnostics, 2021. Cites: paper

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. Cites: paper

A Mixed-Domain Self-Attention Network for Multilabel Cardiac Irregularity Classification Using Reduced-Lead Electrocardiogram
Hao-Chun Yang, Wan‐Ting Hsieh, Trista Pei-Chun Chen. Computing in cardiology, 2021. Cites: paper

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. Cites: paper

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. Cites: paper

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. Cites: paper

Automatic Classification of Full- and Reduced-Lead Electrocardiograms Using Morphological Feature Extraction
Alexander Hammer, Matthieu Scherpf, Hannes Ernst et al. Computing in cardiology, 2021. Cites: paper

Automatic Diagnosis of Cardiac Disease from Twelve-Lead and Reduced-Lead ECGs Using Multilabel Classification
Prathic Sundararajan, Kevin Moses, Cristhian Potes et al. Computing in cardiology, 2021. Cites: paper

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. Cites: paper

Cardiac Abnormality Detection Based on an Ensemble Voting of Single-Lead Classifier Predictions
Pierre Gabriel Aublin, Mouïn Ben Ammar, Nina Achache et al. Computing in cardiology, 2021. Cites: paper

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. Cites: paper

Convolution-Free Waveform Transformers for Multi-Lead ECG Classification
Annamalai Natarajan, Gregory Boverman, Yale Chang et al. Computing in cardiology, 2021. Cites: paper

Deep Discriminative Domain Generalization with Adversarial Feature Learning for Classifying ECG Signals
Zuogang Shang, Zhibin Zhao, Hui Fang et al. Computing in cardiology, 2021. Cites: paper

First Steps Towards Self-Supervised Pretraining of the 12-Lead ECG
Daniel Gedon, Antônio H. Ribeiro, Niklas Wahlström et al. Computing in cardiology, 2021. Cites: paper

Leveraging Period-Specific Variations in ECG Topology for Classification Tasks
Paul Samuel Ignacio. Computing in cardiology, 2021. Cites: paper

Multi-Label Cardiac Abnormality Classification from Electrocardiogram Using Deep Convolutional Neural Networks
Nima L. Wickramasinghe, Mohamed Athif. Computing in cardiology, 2021. Cites: paper

Multi-label Classification of Cardiac Abnormalities for Multi-lead ECG Recordings Based on Auto-encoder Features and a Neural Network Classifier
Onno Linschmann, Maurice Rohr, Steffen K. Leonhardt et al. Computing in cardiology, 2021. Cites: paper

Multi-Label Classification of Multi-lead ECG Based on Deep 1D Convolutional Neural Networks With Residual and Attention Mechanism
Yamin Liu, Hanshuang Xie, Qineng Cao et al. Computing in cardiology, 2021. Cites: paper

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