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

High precision ECG digitization using artificial intelligence
Anthony Demolder, Viera Krešňáková, Michal Hojcka et al. Journal of Electrocardiology, 2025. Cites: dataset, paper

Temporal and spatial self supervised learning methods for electrocardiograms
Wenping Chen, Huibin Wang, Lili Zhang et al. Scientific Reports, 2025. Cites: paper

Ensemble Post-hoc Explainable AI for Multilead ECG: Identifying Disease-Relevant Features in Single-Lead Interpretations
Jacqueline Michelle Metsch, Philip Hempel, Miriam Cindy Maurer et al. bioRxiv (Cold Spring Harbor Laboratory), 2025. Cites: paper

Self-supervised learning for Electrocardiogram classification using Lead Correlation and Decorrelation
Wenhan Liu, Shurong Pan, Sheng Chang et al. Applied Soft Computing, 2025. Cites: paper

Automated ECG Arrhythmia Classification Using Feature Images with Common Matrix Approach-Based Classifier
Ali Kırkbaş, Aydın Kızılkaya. Sensors, 2025. Cites: paper

Transforming Sleep Monitoring: Review of Wearable and Remote Devices Advancing Home Polysomnography and Their Role in Predicting Neurological Disorders
Diana Vitazkova, Helena Kosnacova, Daniela Turonova et al. Biosensors, 2025. Cites: paper

A cluster-based opposition differential evolution algorithm boosted by a local search for ECG signal classification
Mehran Pourvahab, Seyed Jalaleddin Mousavirad, Virginie Felizardo et al. Journal of Computational Science, 2025. Cites: paper

A multi-lead group network for myocardial infarction detection and localization based on clinical knowledge-driven and dynamic-static feature fusion
Qinghua Sun, Jiali Li, Chunmiao Liang et al. Expert Systems with Applications, 2025. Cites: paper

Transformer-based heart language model with electrocardiogram annotations
Stojancho Tudjarski, Marjan Gušev, Evangelos Kanoulas. Scientific Reports, 2025. Cites: paper

A dual-path interactive denoising autoencoder for removing mixed noise in multi-lead electrocardiogram signals
Xiuxin Zhang, Meng Chen, Yongjian Li et al. Computers & Electrical Engineering, 2025. Cites: paper

Multimodal fusion of spatial–temporal and frequency representations for enhanced ECG classification
Chien‐Liang Liu, Bin Xiao, Chiu-Hsun Hsieh. Information Fusion, 2025. Cites: paper

Assessing the performance of zero-shot visual question answering in multimodal large language models for 12-lead ECG image interpretation
Tomohisa Seki, Yoshimasa Kawazoe, Hiromasa Ito et al. Frontiers in Cardiovascular Medicine, 2025. Cites: paper

Adaptive wavelet base selection for deep learning-based ECG diagnosis: A reinforcement learning approach
Qiao Xiao, Chaofeng Wang. PLoS ONE, 2025. Cites: paper

Myocardial Infarction Identification in Electrocardiogram Signals Using Long Short Term Memory Transfer Learning (LSTM-TL)
Nugroho Rahmanto, Satria Mandala. International Conference on Advancement in Data Science, E-learning and Information System (ICADEIS), 2025. Cites: paper

Large Language Model-Informed ECG Dual Attention Network for Heart Failure Risk Prediction
Chen Chen, Lei Li, Marcel Beetz et al. IEEE Transactions on Big Data, 2025. Cites: paper

[Re] Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL
Antony Gitau, Ayoade Adetunji Adeyemi, Belal Tavashi et al. medRxiv, 2025. Cites: paper

Deep learning-based real-time diagnosis of cardiac diseases through behavioral changes in ECG signals
Muktesh Gupta, Rajesh Wadhvani, Akhtar Rasool. Biomedical Signal Processing and Control, 2025. Cites: paper

Lead-grouped multi-stage learning for myocardial infarction localization
Lin Guo, Qianyun Zhan, Jichao Yang et al. Methods, 2025. Cites: paper

ECG signal generation using feature disentanglement auto-encoder
Hanbin Xiao, Yong Xia. Physiological Measurement, 2025. Cites: dataset

Joint Detection of Rhythmic and Morphological Abnormalities in Electrocardiographic Images: A Multitask Learning Approach
Pharvesh Salman Choudhary, Laxmi Narayan Sharma, Samarendra Dandapat. IEEE Transactions on Artificial Intelligence, 2025. Cites: paper

DCentNet: Decentralized multistage biomedical signal classification using early exits
Xiaolin Li, Binhua Huang, Barry Cardiff et al. Biomedical Signal Processing and Control, 2025. Cites: paper

A Systematic Method Combining Rotated Convolution and State Space Augmented Transformer for Digitizing and Classifying Paper ECGs
Xiang Wang, Jie Yang. Symmetry, 2025. Cites: paper

Artificial Intelligence–Powered Electrocardiogram Detecting Culprit Vessel Blood Flow Abnormality: AI-ECG TIMI Study Design and Rationale
Robert Herman, Timea Kisova, Marta Belmonte et al. Journal of the Society for Cardiovascular Angiography & Interventions, 2025. Cites: paper

Enhanced detection of atrial fibrillation in single-lead electrocardiograms using a Cloud-based artificial intelligence platform
François De Guio, Michiel Rienstra, José María Lillo-Castellano et al. Heart Rhythm, 2025. Cites: paper

Hierarchical Hybrid Model Based on Deep Learning for Myocardial Infarction Detection
Mingxi Han, Chi Ma, Siyuan Li et al. International Conference on Big Data, Information and Computer Network (BDICN), 2025. Cites: dataset, paper

Metrology for artificial intelligence in medicine
Hans Rabus, Nikhil Agarwal, Ahcène Boubekki et al. Measurement Sensors, 2025. Cites: paper

Specialized ECG data augmentation method: leveraging precordial lead positional variability
Jeonghwa Lim, Yeha Lee, Won Seuk Jang et al. Biomedical Engineering Letters, 2025. Cites: paper

Advancements in AI for cardiac arrhythmia detection: A comprehensive overview
Jagdeep Rahul, Lakhan Dev Sharma. Computer Science Review, 2025. Cites: paper

3-D VCG Loop-Based Myocardial Infarction Identification With Multiscale Spatial Residual Attention
Cong He, Jieshuo Zhang, Peng Xiong et al. IEEE Transactions on Instrumentation and Measurement, 2025. Cites: paper

A convolutional autoencoder framework for ECG signal analysis
Ugo Lomoio, Patrizia Vizza, Raffaele Giancotti et al. Heliyon, 2025. Cites: paper

Advanced ECG Signal Analysis for Cardiovascular Disease Diagnosis Using AVOA Optimized Ensembled Deep Transfer Learning Approaches
Amrutanshu Panigrahi, Abhilash Pati, Bibhuprasad Sahu et al. Computers, materials & continua/Computers, materials & continua (Print), 2025. Cites: paper

Alternative Labeling of ECG Datasets on Deep Learning Model for ECG Fiducial Point Detection
Yu-Chen Lin, Cheng Cheng, Shi-Yi Wu et al. World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany, 2025. Cites: paper

A Method for Detecting ST Segment Anomalies Using an Inverted Transformer with Incept Fusion Attention
Feiyan Zhou, Yawen Wang, Yuhao Sun et al. Signals and communication technology, 2025. Cites: paper

An Enhanced Task Migration Technique Based on Convolutional Neural Network in Machine Learning Framework
Hamayun Khan, Muhammad Atif Imtiaz, Hira Siddique et al. Computer Systems Science and Engineering, 2025. Cites: paper

A Review of Deep Learning for Biomedical Signals: Current Applications, Advancements, Future Prospects, Interpretation, and Challenges
Ali Mohammad Alqudah, Zahra Moussavi. Computers, materials & continua/Computers, materials & continua (Print), 2025. Cites: paper

Augmentation-Free Contrastive Learning for EKG Classification
Junheng Wang, Miloš Hauskrecht. Lecture notes in computer science, 2025. Cites: paper

Bean: A Multibranch Network for 12 Leads Electrocardiogram Multilabel Classification Based on Ensemble Learning and Attention
Xuhang Wang, Ning Wang, Dange Liu et al. IEEE Transactions on Instrumentation and Measurement, 2025. Cites: paper

Binary Classification of Cardiac Pathologies Using Deep Learning: A PTB-XL Dataset Approach
Mohamed Khalil Chaabani, Nathan Antonio Guerreiro, Luiz Alberto Martineli Ribeiro et al. Springer proceedings in business and economics, 2025. Cites: paper

Contrasting by Augmented Patient Electrocardiograms to Learn Representations for a Foundation Model
Gul Rukh Khattak, Konstantinos Patlatzoglou, Yixiu Liang et al. Lecture notes in computer science, 2025. Cites: paper

Cross-Dataset Electrocardiogram Classification Using Transfer Learning with One-Dimensional Convolutional Neural Networks
Yang Liu, Yonglong Zhu, Wanhui Wen. SSRN Electronic Journal, 2025. Cites: paper

Detection and Localization of Acute Myocardial Infarction Using Branch-Reduced Convolutional Neural Network
Hanfei Shen, John Q. Liu. IEEE Transactions on Instrumentation and Measurement, 2025. Cites: paper

Diagnostic-Wise Hybrid Loss for ECG Signals Reconstruction Based on Wavelet Decomposition
Meng Chen, Yongjian Li, Mingsen Du et al. IEEE Transactions on Instrumentation and Measurement, 2025. Cites: paper

Ecgtizer: A Fully Automated Digitizing and Signal Recovery Pipeline for Electrocardiograms
Alex Lence, Ahmad Fall, S. Cohen et al. SSRN Electronic Journal, 2025. Cites: paper

ECGXtract: Deep Learning-based ECG Feature Extraction for Automated CVD Diagnosis
Youssif Abuzied, Hassan Abd-Eltawab, Abdelrhman Gaber et al. SSRN Electronic Journal, 2025. Cites: paper

FMM-Head: Enhancing Autoencoder-Based ECG Anomaly Detection with Prior Knowledge
Giacomo Verardo, Magnus Boman, Samuel Bruchfeld et al. Lecture notes in computer science, 2025. Cites: paper

Identification of Cardiac Risk Factors from ECG Signals Using Residual Neural Networks
Divya Arivalagan, O. Vignesh, Rubankumar Dhanasekaran. Congenital Heart Disease, 2025. Cites: paper

Identification of Myocardial Infarction Using an MLP Network and the Phase Space of ECG and VCG Signals
Emanuel T. de A. da Silva, Gabriel D. Gomes, Rafael Duarte de Sousa et al. World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany, 2025. Cites: paper

Integrating deep learning with ECG, heart rate variability and demographic data for improved detection of atrial fibrillation
Araz Rawshani, Aidin Rawshani, Gustav Jan Smith et al. Open Heart, 2025. Cites: paper

PearsonUnet: ECG Classification Model Integrating Pearson Related Beats
Hongxiao Yang, Yufei Zhao, Xinwu Yang. IEEE Transactions on Instrumentation and Measurement, 2025. Cites: paper

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