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

ECG Signal Classification Using Deep Learning Techniques Based on the PTB-XL Dataset
Sandra Śmigiel, Krzysztof Pałczyński, Damian Ledziński. Entropy, 2021. Cites: paper

MFB-LANN: A lightweight and updatable myocardial infarction diagnosis system based on convolutional neural networks and active learning
Ziyang He, Zhiyong Yuan, Panfeng An et al. Computer Methods and Programs in Biomedicine, 2021. Cites: paper

k-Labelsets Method for Multi-Label ECG Signal Classification Based on SE-ResNet
Jihye Yoo, Yeongbong Jin, Bonggyun Ko et al. Applied Sciences, 2021. Cites: paper

Detection of Myocardial Infarction from 12 Lead ECG Images
Ravi Kumar Sanjay Sane, Pharvesh Salman Choudhary, Laxmi Narayan Sharma et al. National Conference on Communications, 2021. Cites: paper

The relation of 12 lead ECG to the cardiac anatomy: The normal CineECG
Peter Michael van Dam, Machteld Boonstra, Emanuela H. Locati et al. Journal of Electrocardiology, 2021. Cites: paper

Practical fine-grained learning based anomaly classification for ECG image
Qing Cao, Nan Du, Yu Li et al. Artificial Intelligence in Medicine, 2021. Cites: paper

Acute Myocardial Infarction Detection Using Deep Learning-Enabled Electrocardiograms
Xiehui Chen, Wenqin Guo, Lingyue Zhao et al. Frontiers in Cardiovascular Medicine, 2021. Cites: paper

Detection and classification of arrhythmia using an explainable deep learning model
Yong‐Yeon Jo, Joon‐myoung Kwon, Ki‐Hyun Jeon et al. Journal of Electrocardiology, 2021. Cites: paper

A Novel Deep Learning based Gated Recurrent Unit with Extreme Learning Machine for Electrocardiogram (ECG) Signal Recognition
Clement Virgeniya S., E. Ramaraj. Biomedical Signal Processing and Control, 2021. Cites: paper

Automated detection of shockable ECG signals: A review
Mohamed Adel Hammad, Kandala N. V. P. S. Rajesh, Amira Abdelatey et al. Information Sciences, 2021. Cites: paper

Recurrence Plot-Based Approach for Cardiac Arrhythmia Classification Using Inception-ResNet-v2
Hua Zhang, Chengyu Liu, Zhimin Zhang et al. Frontiers in Physiology, 2021. Cites: paper

WITHDRAWN: Review of ECG arrhythmia classification using deep neural network
Deepak Gupta, Bhavna Bajpai, Gaurav Dhiman et al. Materials Today Proceedings, 2021. Cites: paper

The year in cardiovascular medicine 2020: digital health and innovation
Charalambos A. Antoniades, Folkert Wouter Asselbergs, Panos E. Vardas. Russian Journal of Cardiology, 2021. Cites: paper

Semi-supervised time series classification method for quantum computing
Sheir Yarkoni, Andrii Kleshchonok, Yury Dzerin et al. Quantum Machine Intelligence, 2021. Cites: paper

Interpretable deep learning for automatic diagnosis of 12-lead electrocardiogram
Dongdong Zhang, Samuel Yang, Xiaohui Yuan et al. iScience, 2021. Cites: paper

Development of a Real-Time Single-Lead Single-Beat Frequency-Independent Myocardial Infarction Detector
Harold Martin. FIU - Digital Commons (Florida International University), 2021. Cites: paper

Automatic Detection for Multi-Labeled Cardiac Arrhythmia Based on Frame Blocking Preprocessing and Residual Networks
Zicong Li, Henggui Zhang. Frontiers in Cardiovascular Medicine, 2021. Cites: paper

Let Your Heart Speak in its Mother Tongue: Multilingual Captioning of Cardiac Signals
Dani Kiyasseh, Tingting Zhu, David Andrew Clifton. arXiv (Cornell University), 2021. Cites: paper

ECGT2T: Electrocardiogram synthesis from Two asynchronous leads to Ten leads.
Yong‐Yeon Jo, Joon‐myoung Kwon. 2021. Cites: paper

Electrocardiogram synthesis
Yong‐Yeon Jo, Joon‐myoung Kwon. arXiv (Cornell University), 2021. Cites: paper

Towards Synthesizing Twelve-Lead Electrocardiograms from Two Asynchronous Leads
Yong‐Yeon Jo, Young Sang Choi, Jong-Hwan Jang et al. arXiv (Cornell University), 2021. Cites: paper

Automated ECG classification using a non-local convolutional block attention module
Jikuo Wang, Qiao Xu, Changchun Liu et al. Computer Methods and Programs in Biomedicine, 2021. Cites: paper

Big data reveals insights for lead importance in ECG interpretation
Ting Yang, Richard E. Gregg, Saeed Babaeizadeh. Journal of Electrocardiology, 2021. Cites: paper

Advances of ECG Sensors from Hardware, Software and Format Interoperability Perspectives
Khaleel Husain, Mohd Soperi Mohd Zahid, Shahab Ul Hassan et al. Electronics, 2021. Cites: paper

Analysis of the optimal time delay for Reconstruction of Phase Space of VCG signals to identify anterior wall infarction
Rafael Duarte de Sousa, José Barbosa, Ana Nogueira Rodrigues De Oliveira et al. Anais de XXXV Simpósio Brasileiro de Telecomunicações e Processamento de Sinais, 2021. Cites: paper

An Experiment Environment for Definition, Training and Evaluation of Electrocardiogram-Based AI Models
Nils Gumpfer, Joshua Prim, Dimitri Grün et al. Lecture notes in computer science, 2021. Cites: paper

CLECG: A Novel Contrastive Learning Framework for Electrocardiogram Arrhythmia Classification
Hui Chen, Guijin Wang, Guodong Zhang et al. IEEE Signal Processing Letters, 2021. Cites: paper

MLBF-Net: A Multi-Lead-Branch Fusion Network for Multi-Class Arrhythmia Classification Using 12-Lead ECG
Jing Zhang, Liang Deng, Aiping Liu et al. IEEE Journal of Translational Engineering in Health and Medicine, 2021. Cites: paper

Stratified Sampling for Extreme Multi-label Data
Maximillian Merrillees, Lan Du. Lecture notes in computer science, 2021. Cites: paper

12-lead ECG Arrythmia Classification Using Convolutional Neural Network for Mutually Non-Exclusive Classes
Mateusz Soliński, Michał Łepek, Antonina Pater et al. Computing in cardiology, 2020. Cites: paper

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. Cites: paper

On the Application of Convolutional Neural Networks for 12-lead ECG Multi-label Classification using Datasets from Multiple Centers
Davide Borra, Alice Andalò, Stefano Severi et al. Computing in cardiology, 2020. Cites: paper

The electro-anatomical pathway for normal and abnormal ECGs in COVID patients
Peter Michael van Dam, Machteld Boonstra, Rob W. Roudijk et al. Computing in cardiology, 2020. Cites: paper

Development of an embedded device for real-time detection of atrial fibrillation and atrial flutter in single-channel ECG, using optimised classification based on a large training corpus
Auer, Eric. Zenodo (CERN European Organization for Nuclear Research), 2020. Cites: paper

The year in cardiovascular medicine 2020: digital health and innovation
Charalambos A. Antoniades, Folkert Wouter Asselbergs, Panos E. Vardas. European Heart Journal, 2020. Cites: paper

Explainable artificial intelligence to detect atrial fibrillation using electrocardiogram
Yong‐Yeon Jo, Younghoon Cho, Soo Youn Lee et al. International Journal of Cardiology, 2020. Cites: paper

CROCS: Clustering and Retrieval of Cardiac Signals Based on Patient Disease Class, Sex, and Age
Dani Kiyasseh, Tingting Zhu, David Andrew Clifton. arXiv (Cornell University), 2020. Cites: paper

PCPs: Patient Cardiac Prototypes
Dani Kiyasseh, Tingting Zhu, David Andrew Clifton. arXiv (Cornell University), 2020. Cites: paper

The use of machine translation algorithm based on residual and LSTM neural network in translation teaching
Beibei Ren. PLoS ONE, 2020. Cites: paper

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. Cites: paper

An Approach for Multichannel ECG Compression Using HOSVD
Yuliyan Slaveykov Velchev. 2020. Cites: paper

Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL
Nils Strodthoff, Patrick Wagner, Tobias Schaeffter et al. IEEE Journal of Biomedical and Health Informatics, 2020. Cites: dataset, paper

A Generative Adversarial Approach To ECG Synthesis And Denoising
Karol Antczak. arXiv (Cornell University), 2020. Cites: paper

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