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

Enhancing cross-session electrocardiogram biometric authentication with Siamese Autoencoder and phase space features
Wen-Yen Hsu, Shih‐Chin Fang, Hsiao‐Lung Chan. Pattern Recognition Letters, 2026. Cites: paper

TRACE: Tractable Routing Autoencoder for Clinical ECG
Shunbo Jia, Runze Ma, Haonan Lyu et al. arXiv (Cornell University), 2026. Cites: dataset, paper

Adaptive ultra-lightweight 12-lead ECG reconstruction from limited leads via a vectorcardiogram-mediated cascade framework
Diao Limin, Hongyu Zhong, Yi Zheng et al. Biomedical Signal Processing and Control, 2026. Cites: paper

I-GraphECG: Observability-Based Lead Selection and Interpretable Disease Prediction from the 12-Lead ECG Using a Gray-Box Graph Electrophysiology Surrogate
Limin Zhao, Hongtao Xu, Pengjian Wang et al. Sensors, 2026. Cites: paper

CARDIA-X: Global Semantic Transition and Rough-Set Rules for Auditable Post Hoc Electrocardiographic Explainability
Pavlo Radiuk, Olexander Barmak, Liliana V. Klymenko et al. AI, 2026. Cites: paper

Hybrid Multi-Domain ECG Feature Learning with mRMR and CNN–Transformer for Cardiac Disease Classification
Mohammed Alnusayri, Sara Mumtaz, Bader Aldughayfiq et al. Bioengineering, 2026. Cites: paper

Analysis of Interpretable Machine Learning Classifiers for Myocardial Infarction Detection Using 12-Lead ECG Features
Chutian Chen, Valentina L. Kouznetsova, Igor F. Tsigelny. Medinformatics, 2026. Cites: dataset, paper

Why blinding sex is not enough: equitable discrimination, unequal operation in machine-learning detection of myocardial infarction from the ECG
Guilherme Cesar da Costa, Jose Gregorio Valero Rodriguez, Giulia fernandes de Oliveira da Costa et al. Journal of Electrocardiology, 2026. Cites: paper

Calibration and processing sensitivity of time-embedded three-dimensional electrocardiographic descriptors: a two-dataset benchmark
Alejandro Jesús Bermejo Valdés. medRxiv, 2026. Cites: dataset, paper

SOTER: A Generative Time-Series Foundation Model for Wearable Human Physiological Signals
Fangke Chen, Sirry Chen, Wei Chen et al. arXiv (Cornell University), 2026. Cites: dataset

Beyond full fine-tuning: towards enhanced generalizability in downstream ECG foundation model adaptation
Giuliana Monachino, Beatrice Zanchi, Georgiy Farina et al. Scientific Reports, 2026. Cites: dataset, paper

Uncertainty-quantified time–frequency neural operator (UQ-TF-NO) for discretization-invariant heart failure risk prediction from ECG signals
Victor Adeyi Odeh, Zhang XiaoLing, Clement Agaba et al. Applied Soft Computing, 2026. Cites: paper

Multi-Label 12-Lead ECG Classification on the PTB-XL Dataset: A Comparative Evaluation of Deep Learning Architectures and Heterogeneous Ensemble Approaches
Yunus Emre Mert, Ece Akdoğan, Hüseyin Üvet. arXiv (Cornell University), 2026. Cites: dataset

A Novel Hybrid Transformer-Based Framework for Reconstruction-Based ECG Anomaly Detection
Aashish Sharma, Shweta Jain, Rishit Aggarwal et al. Lecture notes in networks and systems, 2026. Cites: paper

Label-efficient cross-population transfer learning for electrocardiographic risk stratification: evaluation across German, Chinese and US cohorts
Yan Wang, Yan Lu. Frontiers in Cardiovascular Medicine, 2026. Cites: paper

A deep learning framework for arrhythmia classification: mitigating data leakage and class imbalance in ECG analysis
Sudeshna Sani, Dipra Mitra, Pallab Banerjee et al. Journal of Medical Engineering & Technology, 2026. Cites: paper

AI-Enabled Atrial Fibrillation Detection, Prediction, and Classification Based on ECG Data: A Review
Kimia Tahvildari, Fatih Kahraman. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 2026. Cites: paper

Masked autoencoder with adaptive Time–Frequency Cross-Attention for ECG arrhythmia classification
Hailun Wang, Yuanhang Li, Jiaqi Deng et al. Biomedical Signal Processing and Control, 2026. Cites: paper

Learning from VAE Errors to support ECG-based Differential Diagnosis of Myocardial Scar
Shayan Sharifi, Riccardo Treu, Ilaria Gandin et al. arXiv (Cornell University), 2026. Cites: paper

Comprehensive ECG Waveform Analysis for Machine Learning-Based Classification
Vidhi Sutaria, Arpit Jain. Lecture notes in networks and systems, 2026. Cites: paper

Data distribution impacts the performance and generalisability of contrastive learning-based foundation models of electrocardiograms
Gul Rukh Khattak, Konstantinos Patlatzoglou, Joseph Barker et al. PLOS Digital Health, 2026. Cites: paper

From Prediction to Confidence: A Survey of AI-Based ECG Uncertainty Quantification Methods
Ibrahima Sy, Ndeye Fatou Ngom, Cheikh H. T. C. Ndiaye. Lecture notes in networks and systems, 2026. Cites: paper

Accelerating Training of Neural Network ECG Arrhythmia Classification Using Segmentation Information
Lev L. Kalashnikov, Dmitry A. Rostov, Lev A. Smirnov. Scientific School Dynamics of Complex Networks and their Applications (DCNA), 2026. Cites: paper

AI-Based Cardiac Early Disease Detection Using Multimodal Data
Upasana Tiwari, Ruchika Pachori, Rohan Swarnkar et al. IEEE International Conference on Advances in Computing Research on Science Engineering and Technology (ACROSET), 2026. Cites: paper

Comparative Evaluation of Sliding Euclidean Pattern Quantisation Versus Center-Independent Local Binary Patterns for ECG-Based Detection of Left Ventricular Hypertrophy
Muatasim. M Abulmuttaleb Albakaa, Mohd Najeb Jamaludin. IEEE International Conference on Signal and Image Processing Applications (ICSIPA), 2026. Cites: paper

ESTI: Evolutionary Signal and Time-Series Inspection for Auditing ECG Classifiers
Yan Aquino, Pedro Bento, Arthur Buzelin et al. Lecture notes in computer science, 2026. Cites: dataset

Forecasting 1-year atrial fibrillation risk from sinus rhythm electrocardiograms in arrhythmia-naïve patients
Nathan T. Riek, Tanmay Gokhale, Rui Qi Ji et al. Heart Rhythm O2, 2026. Cites: paper

Information Content of Plantar Force Signals: A Quantitative Foundation for Low-Complexity Acquisition in Smart Footwear
Kamen Ivanov, T. Malakova, Dimitar Dimov et al. IEEE Smart World Congress (SWC), 2026. Cites: paper

Large Language Model Benchmarks in Medical Tasks
Lawrence K.Q. Yan, Qian Niu, Ming Li et al. Medicine Advances, 2026. Cites: paper

Multimodal Fusion of Electrocardiogram Signals and Demographic Characteristics with Cross-Attention Networks in the Classification of Cardiovascular Diseases
Talip Çay, Emre ÖLMEZ. Gazi Üniversitesi Fen Bilimleri Dergisi Part C Tasarım ve Teknoloji, 2026. Cites: paper

Multimodal Large Language Models in Healthcare: Architectures, Datasets, Applications, Evaluation Challenges, and Future Directions
Salman Ashraf, Rafia TEHSEEN, Hira ASHRAF. Applied Medical Informatics (University of Medicine and Pharmacy Cluj-Napoca), 2026. Cites: paper

Quantifying heartbeat micro-fragmentations post-myocardial infarction using wavelet entropy and complexity
Gisela V. Clemente, Leandro R. Andrini, Mariano Llamedo. Medical Engineering & Physics, 2026. Cites: paper

Rhythm-Adaptive Recognition of ECG Pathologies Using Model-Informed Preprocessing and Classical Machine Learning Classifiers
Roman Butsiy, Serhii A. Lupenko, А. М. Зозуля. International Conference on Advanced Computer Information Technologies (ACIT), 2026. Cites: paper

Robust Multi-Task Semi-Supervised Network for Fine-Grained Wearable ECG Segmentation
Shang Ma, Junteng Lei, Yueming Wang et al. IEEE Smart World Congress (SWC), 2026. Cites: paper

Towards Interpretable 12-Lead ECG Classification with Mamba and Learned Inter-Lead Graphs
Rhivu Dutta, Suma N G. 2026. Cites: paper

When Is Personalized Federated Learning Worth Its Deployment Cost?
Beyazit Bestami Yuksel, Ayse Yilmazer Metin, Emrah Dikbıyık. International Conference on Computer Science and Engineering (UBMK), 2026. Cites: paper

A Multimodal, Electrocardiography-Centric Clinical Data Platform With Artificial Intelligence-Assisted Querying for Cardiovascular Research
CARLOS RODRIGO RIVERO, Vicente Gallego Sánchez, Alejandro Cortés Beringola et al. Cureus, 2026. Cites: paper

A Novel Attention Mechanism (GAU) for TCNs in 3-to-12-Lead ECG Reconstruction
Mine ONAT, Yılmaz Kaya. Applied Sciences, 2026. Cites: paper

TSPFN: A Temporal Tabular Foundation Model for Physiological Time Series Classification
Jérémie Stym-Popper, Clément Rambour, Federica Granese et al. arXiv (Cornell University), 2026. Cites: paper

Federated acquisition-robust representation learning for cross-protocol ECG biometric authentication
Jiantao Xu, Dexing Zhou, Jianbo Xu et al. Pattern Recognition Letters, 2026. Cites: paper

AI-Assisted Multilabel Diagnosis of 12-Lead Electrocardiograms Using an Interpretable Stacked Deep Learning Model with External Validation
Asifa Tassaddiq, Aiman J.Albarakati, Rabab Alharbi et al. Diagnostics, 2026. Cites: paper

Next-Generation AI Architectures for ECG Arrhythmia Detection
Rajesh. S. Pashikanti. IGI Global eBooks, 2026. Cites: paper

Lead-Specific Pathological Sensitivity-Aware Multi-Lead ECG Abnormality Recognition
Dachuang Qin, Ziquan Feng, Jianfeng Liang et al. Information, 2026. Cites: paper

Leveraging ECG foundation models in critical care for sinus rhythm and atrial fibrillation classification
Maria Galanty, Michiel Hulleman, Merijn C. Reuland et al. Intensive Care Medicine Experimental, 2026. Cites: paper

Robotic-Inspired Tri-Metaheuristic Framework with Neuromorphic Edge Intelligence for Real-Time Multi-Modal Biomedical Signal Processing and Clinical Decision Support
Shaymaa E. Sorour, Mostafa Elbaz. Research Square, 2026. Cites: paper

A deformable lead-attention fusion network for multi-label ECG classification integrating clinical metadata
Enjun Zhou, Xiangyong Kong, Hao Wang et al. Biomedical Physics & Engineering Express, 2026. Cites: paper

Ultra-low communication overhead federated learning strategy for distributed wearable ECG networks
Mohamed Naeem. Journal of King Saud University - Computer and Information Sciences, 2026. Cites: paper

Disentangled conditional generation of ECG signals with interpretable latent factors
Rong Xiao, Zhijun Xiao, Jianqing Li et al. Computer Methods and Programs in Biomedicine, 2026. Cites: dataset

Multi-label arrhythmia classification from Holter scatter plots using deep learning and clinically oriented thresholds
X W Chen, Jia Xu, Hui Li et al. Scientific Reports, 2026. Cites: paper

Objective Assessment of Pathological ECG Waveforms Using Gender-specific Normal Waveform Distributions
Krzysztof Piotr Malinowski, Klaudia K. Proniewska, Peter Michael van Dam. Arrhythmia & Electrophysiology Review, 2026. Cites: dataset, paper

Cited by