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

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

Multi-Label ECG Classification Using Convolutional Neural Networks in a Classifier Chain
Bjørn-Jostein Singstad, Eraraya Morenzo Muten, Pal Haugar Brekke. Computing in cardiology, 2021. Cites: paper

Pathologies Prediction on Short ECG Signals with Focus on Feature Extraction Based on Beat Morphology and Image Deformation
Jeffrey van Prehn, Svetoslav Ivanov, Georgi I. Nalbantov. Computing in cardiology, 2021. Cites: paper

Reduced-Lead ECG Classifier Model Trained with DivideMix and Model Ensemble
Hiroshi Seki, T. Nakano, Koshiro Ikeda et al. Computing in cardiology, 2021. Cites: paper

Robust and Task-Aware Training of Deep Residual Networks for Varying-Lead ECG Classification
Hansheng Ren, Miao Xiong, Bryan Hooi. Computing in cardiology, 2021. Cites: paper

Segment, Perceive and Classify - Multitask Learning of the Electrocardiogram in a Single Neural Network
Philipp Sodmann, Marcus Vollmer, Lars Kaderali. Computing in cardiology, 2021. Cites: paper

Semi-Supervised Learning for ECG Classification
Rui André Rodrigues, Paula Couto. Computing in cardiology, 2021. Cites: paper

Spatio-Temporal ECG Network for Detecting Cardiac Disorders from Multi-Lead ECGs
Long Chen, Zheheng Jiang, Tiago Paggi de Almeida et al. Computing in cardiology, 2021. Cites: paper

Towards Generalization of Cardiac Abnormality Classification Using ECG Signal
Xiaoyu Li, Chen Li, Xian Xu et al. Computing in cardiology, 2021. Cites: paper

Towards High Generalization Performance on Electrocardiogram Classification
Hyeongrok Han, Seongjae Park, Seonwoo Min et al. Computing in cardiology, 2021. Cites: paper

Two Might Do: A Beat-by-Beat Classification of Cardiac Abnormalities Using Deep Learning with Domain-Specific Features
Berken Utku Demirel, Adnan Harun Dogan, Mohammad Abdullah Al Faruque. Computing in cardiology, 2021. Cites: paper

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. Cites: paper

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. Cites: paper

ECG reading differences demonstrated on two databases
Richard E. Gregg, Ting Yang, Stephen W. Smith et al. Journal of Electrocardiology, 2021. Cites: paper

VIbCReg: Variance-Invariance-better-Covariance Regularization for Self-Supervised Learning on Time Series
Dae-Soo Lee, Erlend Aune. arXiv (Cornell University), 2021. Cites: paper

Synthesis of standard 12‑lead electrocardiograms using two-dimensional generative adversarial networks
Yuhe Zhang, Saeed Babaeizadeh. Journal of Electrocardiology, 2021. Cites: paper

Cited by