ArticleScientific data2023
PTB-XL+, a comprehensive electrocardiographic feature dataset.
Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Clinically-aligned explainable AI for atrial fibrillation detection: A U-Net inspired multi-lead ECG analysis framework.Computer methods and programs in biomedicine · 2026Article
- Explainable electrocardiogram interpretation using deep learning-based semantic segmentation.European heart journal. Digital health · 2026Article
- ECG-image-database: large-scale paired ECG images and time-series with real-world artifacts; a foundation for computerized ECG digitization and analysis.Physiological measurement · 2026Article
- Multicenter real world validation of on device single lead ECG parameters for remote cardiac assessment.NPJ cardiovascular health · 2026Article
- A systematic evaluation of uncertainty quantification techniques in deep learning: a case study in photoplethysmography signal analysis.Machine learning. Health · 2026Article
- The Rlign algorithm for enhanced electrocardiogram analysis through heart rate-corrected ECG alignment for explainable classification and clustering.European heart journal. Digital health · 2026Article
- Teaching multimodal LLMs to comprehend 12-lead electrocardiographic images.NPJ digital medicine · 2026Article
- Precise peak width estimation for solving key challenges in biosignal and spectral analysis.Scientific reports · 2026Article
- AI-Detected Asymptomatic Atrial Fibrillation and Risk of Incident Ischemic Stroke and Cardiovascular Events: A UK Biobank Study.medRxiv : the preprint server for health sciences · 2026Article
- A lightweight and robust method for electrocardiogram anomaly detection and localization using multi-scale masked autoencoder.PloS one · 2026Article
- Interpretable detection of left ventricular hypertrophy using commercial ECG features and machine learning: a study based on the PTB-XL+ dataset.Frontiers in cardiovascular medicine · 2026Article
- Label-efficient cross-population transfer learning for electrocardiographic risk stratification: evaluation across German, Chinese and US cohorts.Frontiers in cardiovascular medicine · 2026Article
- Artificial Intelligence in Cardiac Electrophysiology: A Clinically Oriented Review with Engineering Primers.Bioengineering (Basel, Switzerland) · 2025Review
- Interpretable Deep Learning Models for Arrhythmia Classification Based on ECG Signals Using PTB-X Dataset.Diagnostics (Basel, Switzerland) · 2025Article
- Large Language Model-informed ECG Dual Attention Network for Heart Failure Risk Prediction.IEEE transactions on big data · 2025Article
- ECG-LM: Understanding Electrocardiogram with a Large Language Model.Health data science · 2025Article
- A hybrid ResNet-ViT approach to bridge the global and local features for myocardial infarction detection.Scientific reports · 2024Article
- Ten quick tips for electrocardiogram (ECG) signal processing.PeerJ. Computer science · 2024Article
- MedalCare-XL: 16,900 healthy and pathological synthetic 12 lead ECGs from electrophysiological simulations.Scientific data · 2023Article
- PTB-XL+, a comprehensive electrocardiographic feature dataset.Scientific data · 2023Article
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning (ML) methods for the analysis of electrocardiography (ECG) data are gaining importance, substantially supported by the release of large public datasets. However, these current datasets miss important derived descriptors such as ECG features that have been devised in the past hundred years and still form the basis of most automatic ECG analysis algorithms and are critical for cardiologists' decision processes. ECG features are available from sophisticated commercial software but are not accessible to the general public. To alleviate this issue, we add ECG features from two leading commercial algorithms and an open-source implementation supplemented by a set of automatic diagnostic statements from a commercial ECG analysis software in preprocessed format. This allows the comparison of ML models trained on clinically versus automatically generated label sets. We provide an extensive technical validation of features and diagnostic statements for ML applications. We believe this release crucially enhances the usability of the PTB-XL dataset as a reference dataset for ML methods in the context of ECG data.
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