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

Self-Supervised Contrastive Learning for Medical Time Series: A Systematic Review
Ziyu Liu, Azadeh Alavi, Minyi Li et al. Sensors, 2023. Cites: paper

Fusion of edge detection and graph neural networks to classifying electrocardiogram signals
Linh T. Duong, Doan Thi Hoai Thu, Cong Q. Chu et al. Expert Systems with Applications, 2023. Cites: paper

Deep Learning-Based ECG Arrhythmia Classification: A Systematic Review
Qiao Xiao, Khuan Lee, Siti Aisah Mokhtar et al. Applied Sciences, 2023. Cites: paper

Applications of Self-Supervised Learning to Biomedical Signals: where are we now
Federico Del Pup, Manfredo Atzori. 2023. Cites: paper

Fourier space approach for convolutional neural network (CNN) electrocardiogram (ECG) classification: A proof-of-concept study
Madeline Kent, Luiz Vasconcelos, Sardar Ansari et al. Journal of Electrocardiology, 2023. Cites: paper

Applications of Self-Supervised Learning to Biomedical Signals: where are we now
Federico Del Pup, Manfredo Atzori. 2023. Cites: paper

An ECG Classification Method Based on Multi-Task Learning and CoT Attention Mechanism
Quancheng Geng, Hui Liu, Tianlei Gao et al. Healthcare, 2023. Cites: dataset, paper

Artificial intelligence in clinical physiology: How to improve learning agility
D. V. Shutov, Daria E. Sharova, L. R. Abuladze et al. Digital Diagnostics, 2023. Cites: paper

Integrating multimodal information in machine learning for classifying acute myocardial infarction
Ran Xiao, Cheng Ding, Xiao Hu et al. Physiological Measurement, 2023. Cites: paper

An Attentive Spatio-Temporal Learning-Based Network for Cardiovascular Disease Diagnosis
Debasish Jyotishi, Samarendra Dandapat. IEEE Transactions on Systems Man and Cybernetics Systems, 2023. Cites: paper

Dense lead contrast for self-supervised representation learning of multilead electrocardiograms
Wenhan Liu, Zhoutong Li, Huaicheng Zhang et al. Information Sciences, 2023. Cites: paper

Heart disease classification based on ECG using machine learning models
Seyed Matin Malakouti. Biomedical Signal Processing and Control, 2023. Cites: paper

Effectively Modeling Time Series with Simple Discrete State Spaces
Michael Zhang, Khaled Kamal Saab, Michael Poli et al. arXiv (Cornell University), 2023. Cites: paper

Classification of 27 heart abnormalities using 12-lead ECG signals with combined deep learning techniques
Atiaf Ayal Rawi, Murtada Khalafallah Elbashir, Awadallah M. Ahmed. Bulletin of Electrical Engineering and Informatics, 2023. Cites: paper

Hypergraph and cross-attention-based unsupervised domain adaptation framework for cross-domain myocardial infarction localization
Shuaiying Yuan, Ziyang He, Jianhui Zhao et al. Information Sciences, 2023. Cites: paper

Spatiotemporal self-supervised representation learning from multi-lead ECG signals
Rui Min Hu, Jie Chen, L. P. Zhou. Biomedical Signal Processing and Control, 2023. Cites: paper

Intelligent Electrocardiogram Analysis in Medicine: Data, Methods, and Applications
Yu-Xia Guan, Ying An, Feng-Yi Guo et al. Chinese Medical Sciences Journal, 2023. Cites: paper

Don't be fooled: label leakage in explanation methods and the importance of their quantitative evaluation
Neil Jethani, Adriel Saporta, Rajesh Ranganath. PubMed, 2023. Cites: paper

MVMTnet: A Multi-variate Multi-modal Transformer for Multi-class Classification of Cardiac Irregularities Using ECG Waveforms and Clinical Notes
Ankur Samanta, Mark Karlov, Meghna Ravikumar et al. arXiv (Cornell University), 2023. Cites: dataset

Pre-training in Medical Data: A Survey
Yixuan Qiu, Feng Lin, Weitong Chen et al. Machine Intelligence Research, 2023. Cites: paper

Bimodal CNN for cardiovascular disease classification by co-training ECG grayscale images and scalograms
Taeyoung Yoon, Daesung Kang. Scientific Reports, 2023. Cites: paper

Multi-Modal Stacking Ensemble for the Diagnosis of Cardiovascular Diseases
Taeyoung Yoon, Daesung Kang. Journal of Personalized Medicine, 2023. Cites: paper

A Deep Learning Architecture Using 3D Vectorcardiogram to Detect R-Peaks in ECG with Enhanced Precision
Maroua Mehri, Guillaume Calmon, Freddy Odille et al. Sensors, 2023. Cites: paper

Atrial fibrillation classification based on the 2D representation of minimal subset ECG and a non-deep neural network
Hua Zhang, Chengyu Liu, Fangfang Tang et al. Frontiers in Physiology, 2023. Cites: paper

Algorithms for automated diagnosis of cardiovascular diseases based on ECG data: A comprehensive systematic review
Hanna Vitaliyivna Denysyuk, Rui João Pinto, Pedro Miguel Silva et al. Heliyon, 2023. Cites: paper

Efficient Clustering-Based electrocardiographic biometric identification
David Meltzer, David Luengo. Expert Systems with Applications, 2023. Cites: paper

On Merging Feature Engineering and Deep Learning for Diagnosis, Risk Prediction and Age Estimation Based on the 12-Lead ECG
Eran Zvuloni, Jesse Read, Antônio H. Ribeiro et al. IEEE Transactions on Biomedical Engineering, 2023. Cites: paper

Using deep convolutional neural networks to predict patients age based on ECGs from an independent test cohort
Bjørn-Jostein Singstad, Belal Tavashi. Proceedings of the Northern Lights Deep Learning Workshop, 2023. Cites: paper

3D ECG display with deep learning approach for identification of cardiac abnormalities from a variable number of leads
Giovanni Bortolan. Physiological Measurement, 2023. Cites: paper

A Multi-View Multi-Scale Neural Network for Multi-Label ECG Classification
Shunxiang Yang, Cheng Lian, Zhigang Zeng et al. IEEE Transactions on Emerging Topics in Computational Intelligence, 2023. Cites: paper

ECG Identification Based on the Gramian Angular Field and Tested with Individuals in Resting and Activity States
Carmen Cámara, Pedro Peris Lopez, Masoumeh Safkhani et al. Sensors, 2023. Cites: paper

Improving generalization performance of electrocardiogram classification models
Hyeongrok Han, Seongjae Park, Seonwoo Min et al. Physiological Measurement, 2023. Cites: paper

Functional Characterization of a Spectrum of Novel Romano-Ward Syndrome KCNQ1 Variants
Susanne Rinné, Annemarie Oertli, Claudia Nagel et al. International Journal of Molecular Sciences, 2023. Cites: paper

Image based deep learning in 12-lead ECG diagnosis
Raymond Ao, George He. Frontiers in Artificial Intelligence, 2023. Cites: paper

A metodologia Scrum com as ferramentas de desenvolvimento de uma Startup como estudo para um modelo de negócio de publicações científicas em uma instituição de ensino
Mariane Soares dos Santos, Edilson Carlos Silva Lima. Editora Pascal LTDA eBooks, 2023. Cites: paper

Análise Comparativa das Arquiteturas de Rajpurkar et al. e Ribeiro et al. para Classificação de Patologias Cardíacas em Eletrocardiogramas
Sarah Morgana Meurer, Daniel Gomes de Pinho Zanco, Eduardo Vinícius Kuhn et al. Anais de XXXV Simpósio Brasileiro de Telecomunicações e Processamento de Sinais, 2023. Cites: paper

Análise da utilização do framework Spring em sistema web para o aplicativo de gerenciamento de condomínio
Carlos Eduardo Júnior Ferreira, Edilson Carlos Silva Lima, Will Ribamar Mendes Almeida. Editora Pascal LTDA eBooks, 2023. Cites: paper

Análise de uma aplicação desenvolvida com PowerApps para automação de relatórios do SAP utilizando Framework Selenium com Python no escritório de uma Organização
Juliana Serra Portela, Edilson Carlos Silva Lima, Jonathan de Araújo Queiroz. Editora Pascal LTDA eBooks, 2023. Cites: paper

Aplicando Padrões de Projeto no Desenvolvimento de uma API Rest com Springboot para ser consumida por uma Aplicação de Clínicas de Saúde
Lucas Yan Costa Matos, Edilson Carlos Silva Lima. Editora Pascal LTDA eBooks, 2023. Cites: paper

A Policy for Early Sequence Classification
Alexander Cao, Jean Utke, Diego Klabjan. Lecture notes in computer science, 2023. Cites: paper

Applications of Self-Supervised Learning to Biomedical Signals: A Survey
Federico Del Pup, Manfredo Atzori. IEEE Access, 2023. Cites: paper

Automated Detection and Localization of Myocardial Infarction With Interpretability Analysis Based on Deep Learning
Chuang Han, Jiajia Sun, Yingnan Bian et al. IEEE Transactions on Instrumentation and Measurement, 2023. Cites: paper

Cardiac Disease Detection Using IoT-Enabled ECG Sensors and Deep Learning Approach
Pranali P. Lokhande, Kotadi Chinnaiah. Communications in computer and information science, 2023. Cites: dataset

Cleaning ECG with Deep Learning: A Denoiser Based on Gated Recurrent Units
Mariana Dias, Phillip Probst, Luís Silva et al. IFIP advances in information and communication technology, 2023. Cites: paper

Convolutional Autoencoder for Filtering of Power-Line Interference with Variable Amplitude and Frequency: Study of 12-Lead PTB-XL ECG Database
Kamen Ivanov, Irena Ilieva Jekova, Vessela Krasteva. Lecture notes in networks and systems, 2023. Cites: paper

DASMcC: Data Augmented SMOTE Multi-Class Classifier for Prediction of Cardiovascular Diseases Using Time Series Features
Nidhi Sinha, M. A. Ganesh Kumar, Amit M. Joshi et al. IEEE Access, 2023. Cites: paper

Decentralized Learning in Healthcare: A Review of Emerging Techniques
Chamani Shiranthika, Parvaneh Saeedi, Ivan V. Bajić. IEEE Access, 2023. Cites: paper

Deep learning models for multilabel ECG abnormalities classification: A comparative study using TPE optimization
Atiaf Ayal Rawi, Murtada Khalafallah Elbashir, Awadallah M. Ahmed. Journal of Intelligent Systems, 2023. Cites: paper

Development of Wearable and Portable Cardiac Arrest Prediction System
Saadman Kabir, Ananta Banik, Moshfiq-Us-Saleheen Chowdhury et al. Lecture notes in networks and systems, 2023. Cites: paper

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