SynthesisSensors (Basel, Switzerland)2023
A Systematic Survey of Data Augmentation of ECG Signals for AI Applications.
Synthesis in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed.
- Multi-Window Temporal Context for ECG-Based Sleep Apnea Detection Under Limited Apnea-Label Availability.Diagnostics (Basel, Switzerland) · 2026Article
- Non-invasive estimation of hemoglobin using machine learning algorithms from i-PPG and PPG signals.Scientific reports · 2026Article
- A Multi-Center Trained Residual Neural Network for Robust Classification of Atrial High-Rate Episodes in Remotely Monitored Pacemakers and Defibrillators.Sensors (Basel, Switzerland) · 2026Article
- Causal AI in Cardiac Arrhythmia: From Pattern Recognition to Mechanistic Insight.Clinical cardiology · 2026Review
- AI-Based QRS Onset Detection in the Early Ventricular Activation Site ECGs.IEEE journal of biomedical and health informatics · 2026Article
- Automated Detection of Normal, Atrial, and Ventricular Premature Beats from Single-Lead ECG Using Convolutional Neural Networks.Sensors (Basel, Switzerland) · 2026Article
- Extension of Atherosclerosis ApoE-/- Mouse-a Model of Chronic Myocardial Ischemia and Evaluation Method.Journal of cardiovascular translational research · 2026Article
- End-to-end non-invasive ECG signal generation from PPG signal: a self-supervised learning approach.Frontiers in physiology · 2026Article
- Systematic Review of Artificial Intelligence and Electrocardiography for Cardiovascular Disease Diagnosis.Bioengineering (Basel, Switzerland) · 2025Review
- A lightweight deep neural network for personalized detecting ventricular arrhythmias from a single-lead ECG device.PLOS digital health · 2025Article
- Advances in cardiovascular signal analysis with future directions: a review of machine learning and deep learning models for cardiovascular disease classification based on ECG, PCG, and PPG signals.Biomedical engineering letters · 2025Review
- Atrial Fibrillation and Atrial Flutter Detection Using Deep Learning.Sensors (Basel, Switzerland) · 2025Article
- Electrocardiogram Abnormality Detection Using Machine Learning on Summary Data and Biometric Features.Diagnostics (Basel, Switzerland) · 2025Article
- Electrocardiogram analysis for cardiac arrhythmia classification and prediction through self attention based auto encoder.Scientific reports · 2025Article
- ECG-Based Biometric Recognition: A Survey of Methods and Databases.Sensors (Basel, Switzerland) · 2025Review
- ECG-XPLAIM: eXPlainable Locally-adaptive Artificial Intelligence Model for arrhythmia detection from large-scale electrocardiogram data.Frontiers in cardiovascular medicine · 2025Article
- ECG-Mamba: Cardiac Abnormality Classification With Non-Uniform-Mix Augmentation on 12-Lead ECGs.IEEE journal of translational engineering in health and medicine · 2025Article
- Predicting troponin biomarker elevation from electrocardiograms using a deep neural network.Open heart · 2024Article
- Machine learning predictions of T cell antigen specificity from intracellular calcium dynamics.Science advances · 2024Article
- A Generative Adversarial Network to Synthesize 3D Magnetohydrodynamic Distortions for Electrocardiogram Analyses Applied to Cardiac Magnetic Resonance Imaging.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
AI techniques have recently been put under the spotlight for analyzing electrocardiograms (ECGs). However, the performance of AI-based models relies on the accumulation of large-scale labeled datasets, which is challenging. To increase the performance of AI-based models, data augmentation (DA) strategies have been developed recently. The study presented a comprehensive systematic literature review of DA for ECG signals. We conducted a systematic search and categorized the selected documents by AI application, number of leads involved, DA method, classifier, performance improvements after DA, and datasets employed. With such information, this study provided a better understanding of the potential of ECG augmentation in enhancing the performance of AI-based ECG applications. This study adhered to the rigorous PRISMA guidelines for systematic reviews. To ensure comprehensive coverage, publications between 2013 and 2023 were searched across multiple databases, including IEEE Explore, PubMed, and Web of Science. The records were meticulously reviewed to determine their relevance to the study's objective, and those that met the inclusion criteria were selected for further analysis. Consequently, 119 papers were deemed relevant for further review. Overall, this study shed light on the potential of DA to advance the field of ECG diagnosis and monitoring.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.