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

Adaptive Cauchy inference from invariant information
Yiming Liu, Shaochen Wang. arXiv (Cornell University), 2026. Cites: paper

Diagnostic Preservation After ECG Digitization: From Risk Stratification to Reliability Assurance
Thai Nho Nguyen, Đoàn Văn Thắng. Journal of Trends in Computer Science and Smart Technology, 2026. Cites: paper

FedCardioGuard: Quality- and Uncertainty-Aware Personalized Federated Learning with Rare-Class Generalization Audits for ECG Classification
Sareh Saberi, Abbas Karimi, Faraneh Zarafshan et al. Research Square, 2026. Cites: paper

Inter-Lead RRI Agreement Between Lead I and Lead II Across Diagnostic ECG Categories: Pilot Evidence for Wearable HRV Monitoring
Naoya Morikawa, Emi Yuda. Applied Sciences, 2026. Cites: paper

A penalized, synergistic deep learning architecture leveraging DBN and GAN for classifying highly asymmetric medical data: A multi-domain validation
Hamidreza Ghasemi, Pedram Salehpour, Fariborz Rahimi. Array, 2026. Cites: paper

ScienceBuddy-PV: A Verifier-Governed Recursive Improvement Protocol for Panvascular Intervention Research
Lingsen You, Yujun Guo, Zhong Xinyu et al. arXiv (Cornell University), 2026. Cites: paper

A hybrid deep feature extraction and ensemble learning framework for robust multi-class ECG image classification
Sonam Nagar, Harish Sharma, Karan Verma et al. Scientific Reports, 2026. Cites: paper

Evolution of Automatic Paper Electrocardiogram Digitization: Methods, Applications, and Future Perspectives
Huang Biaosheng, Jincheng Liu, Xueting Tian et al. Electronics, 2026. Cites: paper

Multi-scale temporal feature fusion and lead-graph attention for multi-label classification of 12-lead ECGs
Feiyan Zhou, Mingyang Li. Physiological Measurement, 2026. Cites: dataset, paper

Three-Dimensional Geometric Deformation of the 12-Lead Electrocardiogram in Bundle Branch Block
Patrick Dumitrescu, Igor Antonio Narciso, Johnny Cobas Hernandez et al. Cureus, 2026. Cites: dataset, paper

FedGNN: Decentralized federated GNN with gradient masking and dynamic feature pruning for heterogeneous ECG classification
N. M. Istiak Chowdhury, Tanvir Mahtab Tapu, Md. Shahriar Shakil et al. Expert Systems with Applications, 2026. Cites: paper

From IoT Sensors to Predictive Analytics: Blockchain and Biometric-Based Data Science Approaches for Personalized Healthcare
Farnaz Farid, Farhad Ahamed, Sami Abduljalil Abdulhak Naji et al. Studies in big data, 2026. Cites: paper

Quantitative Monitoring of Semantic Regular Expressions with Oracle-Defined Trace Predicates
Matin Amini, Mukund Raghothaman. Lecture notes in computer science, 2026. Cites: dataset

A reproducible benchmark for ECG-to-text report generation: Leakage-safe evaluation, performance floors, and stress testing
Amnon Bleich, Antje Linnemann, B. Diem et al. Biomedical Signal Processing and Control, 2026. Cites: paper

TimeNet: An Extensible Unified Data Infrastructure for Next-Generation Temporal Foundation Models
Martin Maritsch, Timo Stoffregen, Thomas Kaar et al. arXiv (Cornell University), 2026. Cites: paper

An Adaptive Wavelet–Attention Framework with Dependency-Aware Feature Modeling for Robust ECG Arrhythmia Classification
H. Sumitha, M. Devanathan. Engineering Technology & Applied Science Research, 2026. Cites: paper

Automated Detection of Ventricular Flutter and Ventricular Hypertrophy From ECG Using Deep Learning
Anuradha Pathak, Raghavendra Sharma, Sunil Datt Sharma. Advances in computational intelligence and robotics book series, 2026. Cites: paper

Automated ECG Arrhythmia Detection and Classification Using Deep Convolutional and Residual Neural Networks
Anuradha Pathak, Radhavendra Sharma, Sunil Datt Sharma. Advances in computational intelligence and robotics book series, 2026. Cites: paper

Metaheuristic-Optimized CNN-HRV Fusion for ECG-Based Cardiac Abnormality Risk Detection under Limited Cross-Patient Data
Giovanni Dimas Prenata, Ahmad Ridho’i. Engineering Technology & Applied Science Research, 2026. Cites: paper

A Study on Methods for Evaluating the Quality of Datasets for Intelligent Algorithm Evaluation
Lu Bai, 谷雨晴, Wei Zhang et al. Electronics, 2026. Cites: paper

CardioAudit-CSD: Compact Prompt Encoding and Calibrated Sparse Fusion for Auditable Prediction
Abdelrhman Gaber, Hassan Abd Eltawab, Tamer ElBatt. International Conference on Machine Learning and Applications, 2026. Cites: paper

Interpretable Hybrid CNN–Transformer Networks for Multi-Label ECG Classification
Alina Lazar. International Conference on Machine Learning and Applications, 2026. Cites: paper

Physio-PPT: Physiology-Anchored Patch-Order Self-supervised Learning for ECG Classification Under Low-Label Regimes
Guilherme A. L. Silva, Pedro H. L. Silva, Arthur Negrão et al. Lecture notes in computer science, 2026. Cites: paper

RELIABILITY-AWARE ARTIFICIAL INTELLIGENCE FOR MULTILABEL ELECTROCARDIOGRAM DIAGNOSIS
Dias Konyspayev, Batyrkhan Kuzenbayev, Nurlybay T. Abatov et al. Scientific Journal of Astana IT University, 2026. Cites: paper

When Not to Diagnose: Evaluating Evidence-Sufficiency Recognition in ECG Multimodal Language Models with ECGTrustBench
Qijia Huang, Jingxia Jiang, Faisal F. Syed et al. International Workshop on Human-Centered Sensing, Modeling, and Intelligent Systems, 2026. Cites: paper

CTF-ANET: Clinical Time Frequency Aware-Attention Network for cardiovascular disease classification in wearable IoT network
C. Jerlin Ajith Davidson, T. R. Chenthil. Scientific Reports, 2026. Cites: paper

EIA-CKD: An End-to-End Internal Augmentation and Curriculum-based Knowledge Distillation framework for lightweight ECG classification
Nasim Beigzadeh, Mahyar Shahsavari, Abdolhossein Fathi. Biomedical Signal Processing and Control, 2026. Cites: paper

Key Issues in the Processing and Analysis of Large Electrocardiographic Datasets Using Machine Learning Methods
Danylo Zakrzhevsky. Cybernetics and Computer Technologies, 2026. Cites: dataset, paper

Cyclostationary Phase Conditioning for Medical Time Series Diffusion
Samuel Ruipérez-Campillo, Michele Copetti, Jorge da Silva Goncalves et al. arXiv (Cornell University), 2026. Cites: paper

Detection of Chagas disease from the ECG: the George B. Moody PhysioNet Challenge 2025
Matthew A. Reyna, Soheil Saghafi, Zuzana Koscova et al. Physiological Measurement, 2026. Cites: paper

PhysioTRACE: Provenance-Aware Stress Tests for Physiological Foundation Models
Ayana Mussabayeva, Anuar Aimoldin, Olivier Oullier et al. arXiv (Cornell University), 2026. Cites: dataset, paper

MSCA-Mamba: A Compact Multi-Scale Mamba Network for Multi-Label ECG Classification
Dilber Çetintaş. Diagnostics, 2026. Cites: paper

Recent advances in deep learning based arrhythmia classification using ECG and PPG signals: a systematic review
Nitya N. Kulkarni, Nagaraja G. S., S. Nagaraja G. et al. Discover Computing, 2026. Cites: paper

Wolff–Parkinson–White syndrome screening from ECG via TinyFLFormer: A tensor-decomposed dual-stream transformer with diffusion-guided knowledge distillation
Majid Sepahvand, Maytham N. Meqdad. Intelligence-Based Medicine, 2026. Cites: paper

A systematic survey of artificial intelligence methods for ECG-based cardiovascular disease prediction
Thanuja M, Soumyashree M. Panchal, Prasanna Lakshmi G S. Discover Artificial Intelligence, 2026. Cites: paper

BeatGraph: Self-Supervised Heartbeat Graphs for Infant ECG Representations from the Home Environment
Mohammad Nur Hossain Khan, Matthew S. Krafczyk, Beverly G. Bolster et al. arXiv (Cornell University), 2026. Cites: paper

Cross-Modal Concept Transfer: From ECG Signals to Images for Explainable Disease Prediction
Chan Lee, Dasom Noh, Minjy Suh et al. Lecture notes in computer science, 2026. Cites: paper

Intelligent Multimodal Biomedical Signal Fusion Using IoT and Deep Learning for Early Disease Prediction
Nadeem Ahmad. International Journal of Scientific Research in Computer Science Engineering and Information Technology, 2026. Cites: paper

MedTS-TTT: Test-Time Training for Medical Time Series Classification
Mingzhi Chen, Yiyu Gui, Guibo Luo. Lecture notes in computer science, 2026. Cites: paper

Accurate Reconstruction of the Standard 12-Lead ECG from a Single Lead Based on Inherent Principles of ECG
Dong-hyuk Seo, Ui Jong Kim, Yong-Yeon Jo et al. Lecture notes in computer science, 2026. Cites: paper

DFuse: A Dual-Branch Adaptive Fusion Network for Multi-Channel MCG Denoising
Shuai Huang, Hao Li, You Li et al. Lecture notes in computer science, 2026. Cites: paper

ECG-CoCa: Physiology-Informed Contrastive and Generative Framework for ECG Interpretation
Xinyue Liu, Yiming Lei, Shiwei Li et al. Lecture notes in computer science, 2026. Cites: dataset

EmERGE: A Multimodal LLM-Based Evidence-Mediated ECG Report Generation and Evaluation Approach
Keyu Yao, Yuchen Liu, Xiaohan Wang et al. Lecture notes in computer science, 2026. Cites: paper

Inversion from ECG Foundation Model
Junseok Lee, Seungwoo Jung, Gyeongsik Yang et al. Lecture notes in computer science, 2026. Cites: paper

MetaFormer: Efficient Metadata-Guided Transformer for Patient-Aware 12-Lead Electrocardiogram Reconstruction
Yi Xie, Jiepeng Chen, Zefeng Li et al. Lecture notes in computer science, 2026. Cites: paper

MSS-Net: Learning Hierarchical Multi-scale and Multimodal Representations for Complex Cardiac Disease Classification
Yuling Chen, Jiahao Xia, Xingwang Yao et al. Lecture notes in computer science, 2026. Cites: paper

SpikeMamba: Spike-Driven State Space Models for Energy-Efficient Biomedical Sequence Modeling
Si Yong Lee, R V Lee, Hawon Park et al. Lecture notes in computer science, 2026. Cites: paper

A comprehensive survey of machine and deep learning as well as explainable artificial intelligence approaches for heart disease detection
Md. Siam Ansary. Discover Informatics, 2026. Cites: paper

Tri-branch spatio-temporal learning network with dendritic aggregation based dynamic GCN for multi-lead ECG classification
Xulong Kong, Chunyan Ma, Jun Wang et al. Biomedical Signal Processing and Control, 2026. Cites: paper

From twelve leads to single: Cross-lead knowledge distillation for domain-generalized atrial fibrillation detection
Xiaojian Lin, Yang Li, Yaomin Shen et al. Biomedical Signal Processing and Control, 2026. Cites: paper

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