Cited by

PTB-XL+, a comprehensive electrocardiographic feature dataset (v1.0.1)

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68 publications cite any version of this project or its primary paper, newest first.

• 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
• Deep Neural Network Framework for Predicting Cardiovascular Diseases from ECG Signals
  Tanishq Soni, Deepali Gupta, Mudita Uppal et al. Recent Advances in Computer Science and Communications, 2024. Cites: paper
• ECG-LM: Understanding Electrocardiogram with a Large Language Model
  Kai Yang, Massimo Hong, Jiahuan Zhang et al. Health Data Science, 2024. Cites: paper
• Classification of Low-Amplitude ECG Components Using Adaptive Activation Functions of Neural Networks
  Anton Volodymyrovych Mnevets, Н. Г. Иванушкина. Radioelectronics and Communications Systems, 2024. Cites: paper
• Ten quick tips for electrocardiogram (ECG) signal processing
  Davide Chicco, Angeliki-Ιlektra Karaiskou, Maarten De Vos. PeerJ Computer Science, 2024. Cites: paper
• Benchmarking Approaches: Time Series Versus Feature-Based Machine Learning in ECG Analysis on the PTB-XL Dataset
  Lucas Bickmann, Lucas Plagwitz, Julian Varghese. Studies in health technology and informatics, 2024. Cites: paper
• Estimating ECG Intervals from Lead-I Alone: External Validation of Supervised Models
  Ridwan Alam, Collin M. Stultz. medRxiv, 2024. Cites: paper
• Development of Clinically Validated Artificial Intelligence Model for Detecting ST-segment Elevation Myocardial Infarction
  Sang‐Hyup Lee, Kyu Lee Jeon, Yong‐Joon Lee et al. Annals of Emergency Medicine, 2024. Cites: paper
• Enhancing explainability in ECG analysis through evidence-based AI interpretability
  Philip Hempel, Theresa Bender, Nicolai Spicher. Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2024. Cites: paper
• Prediction of Myocardial Infarction Using Convolutional Neural Network Method on Electrocardiogram Signal
  Prinintya Nuri Alifa, Satria Mandala. International Conference on Data Science and Its Applications (ICoDSA), 2024. Cites: paper
• Uncertainty Calibrated Deep Regression for QT Interval Measurement in Reduced Lead Set ECGs
  Peter Doggart, Alan Kennedy, Raymond Bond et al. Irish Signals and Systems Conference, 2024. Cites: paper
• Efficient electrocardiogram generation based on cardiac electric vector simulation model
  Wenge Que, Yingnan Bian, Shengjie Chen et al. Computers in Biology and Medicine, 2024. Cites: paper
• Explaining deep learning for ECG analysis: Building blocks for auditing and knowledge discovery
  Patrick Wagner, Temesgen Mehari, Wilhelm Haverkamp et al. Computers in Biology and Medicine, 2024. Cites: paper
• Heptagonal Reinforcement Learning (HRL): a novel algorithm for early prevention of non-sinus cardiac arrhythmia
  Arman Daliri, Roghaye Sadeghi, Neda Sedighian et al. Journal of Ambient Intelligence and Humanized Computing, 2024. Cites: paper
• A hybrid ResNet-ViT approach to bridge the global and local features for myocardial infarction detection
  Junaid Abdul Wahid, Xu Mingliang, Muhammad Ayoub et al. Scientific Reports, 2024. Cites: paper
• ECG Feature Importance Rankings: Cardiologists Vs. Algorithms
  Temesgen Mehari, Ashish Sundar, Alen Bošnjaković et al. IEEE Journal of Biomedical and Health Informatics, 2024. Cites: paper
• Influence of the Training Set Composition on the Estimation Performance of Linear ECG-Lead Transformations
  Daniel Guldenring, Dewar Finlay, Raymond Bond et al. Computing in cardiology, 2023. Cites: paper
• MedalCare-XL: 16,900 healthy and pathological synthetic 12 lead ECGs from electrophysiological simulations
  Karli K. Gillette, Matthias A. F. Gsell, Claudia Nagel et al. Scientific Data, 2023. Cites: paper

Citation data from OpenAlex, updated weekly. Papers that use the project without citing it may be missing.