Publications citing PTB-XL, a large publicly available electrocardiography dataset v1.0.3

ResNet-50 for 12-Lead Electrocardiogram Automated Diagnosis
Nizar Sakli, Haifa Ghabri, Ben Othman Soufiene et al. Computational Intelligence and Neuroscience, 2022. Cites: paper

A study on several critical problems on arrhythmia detection using varying-dimensional electrocardiography
Jingsu Kang, Hao Wen. Physiological Measurement, 2022. Cites: paper

Comprehensive survey of computational ECG analysis: Databases, methods and applications
Elena Merdjanovska, Aleksandra Rashkovska. Expert Systems with Applications, 2022. Cites: paper

Automatic ECG classification and label quality in training data
Ľubomír Antoni, Erik Bruoth, Peter Bugata et al. Physiological Measurement, 2022. Cites: paper

Compelling new electrocardiographic markers for automatic diagnosis
Cristina Rueda, Itziar Fernández, Yolanda Larriba et al. Computer Methods and Programs in Biomedicine, 2022. Cites: paper

Future Techniques and Perspectives on Implanted and Wearable Heart Failure Detection Devices
Muhammad E. H. Chowdhury, Amith Khandaker, Yazan Qiblawey et al. Wiley eBooks, 2022. Cites: paper

Preserving Privacy of High-Dimensional Data by l-Diverse Constrained Slicing
Zenab Amin, Adeel Anjum, Abid Khan et al. Electronics, 2022. Cites: paper

The Optimization of ECG-Leads Based Cardiovascular Abnormal Detection Scheme
Warda R. Mohammed, Ahmed Faeq Hussein. IEEE International Conference on Computing and Machine Intelligence (ICMI), 2022. Cites: paper

Predict alone, decide together: cardiac abnormality detection based on single lead classifier voting
Pierre Gabriel Aublin, Mouïn Ben Ammar, Jérémy Fix et al. Physiological Measurement, 2022. Cites: paper

XBeats: A Real-Time Electrocardiogram Monitoring and Analysis System
Ahmed Badr, Abeer A. Badawi, Abdulmonem M. Rashwan et al. Signals, 2022. Cites: paper

Federated Learning Stability Under Byzantine Attacks
Ala Gouissem, Khalid Abualsaud, Elias E. Yaacoub et al. IEEE Wireless Communications and Networking Conference, 2022. Cites: paper

CNN-FWS: A Model for the Diagnosis of Normal and Abnormal ECG with Feature Adaptive
Junjiang Zhu, Jintao Lv, Dongdong Kong. Entropy, 2022. Cites: paper

Deep Learning for Detecting and Locating Myocardial Infarction by Electrocardiogram: A Literature Review
Ping Xiong, Simon Ming‐Yuen Lee, Ging Chan. Frontiers in Cardiovascular Medicine, 2022. Cites: paper

Automated multilabel diagnosis on electrocardiographic images and signals
Veer Sangha, Bobak J. Mortazavi, Adrian D. Haimovich et al. Nature Communications, 2022. Cites: paper

A visually interpretable detection method combines 3-D ECG with a multi-VGG neural network for myocardial infarction identification
Rui Fang, Chih‐Cheng Lu, Cheng-Ta Chuang et al. Computer Methods and Programs in Biomedicine, 2022. Cites: dataset, paper

Generalizable Beat-by-Beat Arrhythmia Detection by Using Weakly Supervised Deep Learning
Yang Liu, Qince Li, Runnan He et al. Frontiers in Physiology, 2022. Cites: paper

WaSP-ECG: A Wave Segmentation Pretraining Toolkit for Electrocardiogram Analysis
Rob Brisk, Raymond R. Bond, Dewar Finlay et al. Frontiers in Physiology, 2022. Cites: paper

Lead-agnostic Self-supervised Learning for Local and Global Representations of Electrocardiogram
Oh, Jungwoo, Chung, Hyunseung, Joon‐myoung Kwon et al. arXiv (Cornell University), 2022. Cites: dataset

Unsupervised ECG Analysis: A Review
Kasra Nezamabadi, Neda Sardaripour, Benyamin Haghi et al. IEEE Reviews in Biomedical Engineering, 2022. Cites: paper

A transformer-based deep neural network for arrhythmia detection using continuous ECG signals
Rui Min Hu, Jie Chen, Li Zhou. Computers in Biology and Medicine, 2022. Cites: paper

Explainable AI decision model for ECG data of cardiac disorders
Atul Anand, Tushar Kadian, Manu Kumar Shetty et al. Biomedical Signal Processing and Control, 2022. Cites: paper

Real-Time Deep Compressed Sensing Reconstruction for Electrocardiogram Signals
Weibin Cao, Jun Zhang. International Conference on Machine Learning and Computing (ICMLC), 2022. Cites: paper

Transfer learning for non-image data in clinical research: A scoping review
Andreas Ladefoged Ebbehøj, Mette Østergaard Thunbo, Ole Emil Andersen et al. PLOS Digital Health, 2022. Cites: paper

CineECG provides a novel anatomical view on the normal atrial P-wave
Emanuela H. Locati, Carlo Pappone, Francesca Heilbron et al. European Heart Journal - Digital Health, 2022. Cites: paper

Study of the Few-Shot Learning for ECG Classification Based on the PTB-XL Dataset
Krzysztof Pałczyński, Sandra Śmigiel, Damian Ledziński et al. Sensors, 2022. Cites: paper

Practical Lessons on 12-Lead ECG Classification: Meta-Analysis of Methods From PhysioNet/Computing in Cardiology Challenge 2020
Shenda Hong, Wenrui Zhang, Chenxi Sun et al. Frontiers in Physiology, 2022. Cites: paper

A Bi-atrial Statistical Shape Model as a Basis to Classify Left Atrial Enlargement from Simulated and Clinical 12-Lead ECGs
Claudia Nagel, Matthias Schaufelberger, Olaf Dössel et al. Lecture notes in computer science, 2022. Cites: paper

An EMD-Based Approach for Atrial Fibrillation Classification Using Wavelets and Convolutional Neural Network
Hassan Serhal, Nassib Abdallah, Jean-Marie Marion et al. Biomedical Signal Processing and Control, 2022. Cites: paper

Artificial Intelligence for Detection of Cardiovascular-Related Diseases from Wearable Devices: A Systematic Review and Meta-Analysis
Solam Lee, Yuseong Chu, Jiseung Ryu et al. Yonsei Medical Journal, 2022. Cites: paper

Assessing the Reidentification Risks Posed by Deep Learning Algorithms Applied to ECG Data
Arin Ghazarian, Jianwei Zheng, Daniele Carlo Struppa et al. IEEE Access, 2022. Cites: paper

BiDLNet: An Integrated Deep Learning Model for ECG-based Heart Disease Diagnosis
S. Kusuma, K R Jothi. International Journal of Advanced Computer Science and Applications, 2022. Cites: paper

Classificação do infarto de miocárdio a partir da extração de parâmetros de espaços e fase com o uso dos Descritores de Haralick
Vinícius Jefferson Dias Vieira, Emanuel thiago, Dimas Merêncio et al. Anais de XXXV Simpósio Brasileiro de Telecomunicações e Processamento de Sinais, 2022. Cites: paper

Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research
Zhibo Zhang, Hussam Al Hamadi, Ernesto Damiani et al. IEEE Access, 2022. Cites: paper

Explainable Electrocardiogram Analysis with Wave Decomposition: Application to Myocardial Infarction Detection
Yingyu Yang, Marie Rocher, Pamela Moceri et al. Lecture notes in computer science, 2022. Cites: paper

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

Left Atrial Overload Detection in Ecg Using Frequency Domain Features with Machine Learning and Deep Learning Algorithms
Serkan Uslu, Nihal Öztürk, Selçuk Küçükseymen et al. Biomedical Signal Processing and Control, 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

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