ArticleScientific reports2021
Transfer learning for ECG classification.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 2 of them syntheses that pooled it.
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Who cites it
42 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Diagnostic Performance of Deep Learning and Radiomics in Extracranial Carotid Plaque Detection: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Advances in machine and deep learning for ECG beat classification: a systematic review.Frontiers in digital health · 2025Pooled it
- Cross-domain transfer learning strategy enhances interpretability of deep learning model explanations.Scientific reports · 2026Article
- Self-supervised contrastive learning enables robust electrocardiogram-based cardiac classification.Heart rhythm O2 · 2026Article
- End-to-End Platform for Electrocardiogram Analysis and Model Fine-Tuning: Development and Validation Study.Journal of medical Internet research · 2026Article
- Assessing arterial stiffness using characteristics of Korotkoff sounds.Frontiers in cardiovascular medicine · 2026Article
- Enhanced prediction of left ventricular ejection fraction using electrocardiography with the addition of clinical metadata.The Korean journal of internal medicine · 2026Article
- Higher-dimensional embedding of time-series data for machine learning.Frontiers in artificial intelligence · 2026Article
- Deep survival analysis from adult and pediatric electrocardiograms: a multi-center benchmark study.BioData mining · 2025Article
- Transfer learning for predicting acute myocardial infarction using electrocardiograms.PLOS digital health · 2025Article
- Evaluating artificial intelligence-enabled medical tests in cardiology: Best practice.International journal of cardiology. Heart & vasculature · 2025Review
- Interpretable Deep Learning Models for Arrhythmia Classification Based on ECG Signals Using PTB-X Dataset.Diagnostics (Basel, Switzerland) · 2025Article
- Artificial Intelligence and ECG: A New Frontier in Cardiac Diagnostics and Prevention.Biomedicines · 2025Review
- A deep foundation model for electrocardiogram interpretation: enabling rare disease detection through transfer learning.European heart journal. Digital health · 2025Article
- Classification of multi-lead ECG based on multiple scales and hierarchical feature convolutional neural networks.Scientific reports · 2025Article
- Detection of Atrial Fibrillation in Holter ECG Recordings by ECHOView Images: A Deep Transfer Learning Study.Diagnostics (Basel, Switzerland) · 2025Article
- Specialized ECG data augmentation method: leveraging precordial lead positional variability.Biomedical engineering letters · 2025Article
- Artificial Intelligence in Cardiology: Insights From a Multidisciplinary Perspective.Journal of the Society for Cardiovascular Angiography & Interventions · 2025Article
- A multitask deep learning model utilizing electrocardiograms for major cardiovascular adverse events prediction.NPJ digital medicine · 2025Article
- Transfer learning in ECG diagnosis: Is it effective?PloS one · 2025Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Remote monitoring devices, which can be worn or implanted, have enabled a more effective healthcare for patients with periodic heart arrhythmia due to their ability to constantly monitor heart activity. However, these devices record considerable amounts of electrocardiogram (ECG) data that needs to be interpreted by physicians. Therefore, there is a growing need to develop reliable methods for automatic ECG interpretation to assist the physicians. Here, we use deep convolutional neural networks (CNN) to classify raw ECG recordings. However, training CNNs for ECG classification often requires a large number of annotated samples, which are expensive to acquire. In this work, we tackle this problem by using transfer learning. First, we pretrain CNNs on the largest public data set of continuous raw ECG signals. Next, we finetune the networks on a small data set for classification of Atrial Fibrillation, which is the most common heart arrhythmia. We show that pretraining improves the performance of CNNs on the target task by up to [Formula: see text], effectively reducing the number of annotations required to achieve the same performance as CNNs that are not pretrained. We investigate both supervised as well as unsupervised pretraining approaches, which we believe will increase in relevance, since they do not rely on the expensive ECG annotations. The code is available on GitHub at https://github.com/kweimann/ecg-transfer-learning .
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Registered trials
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