ReviewJournal of the Royal Society, Interface2018
Computational techniques for ECG analysis and interpretation in light of their contribution to medical advances.
Review in Journal of the Royal Society, Interface, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03662802 (Development of a Novel Convolution Neural Network for Arrhythmia Classification for Shockable Cardiac Rhythms), which is not on this map. Cited by 57 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.
Development of a Novel Convolution Neural Network for Arrhythmia Classification for Shockable Cardiac Rhythms
Who cites it
57 citing papers in PubMed.
- Application of Artificial Intelligence and Machine Learning in Arrhythmia Detection and Pacemaker Data Analysis.Cardiology research · 2026Review
- Cardiac parameter estimation from electrocardiograms based on inversion of physical simulator behavior.PLOS digital health · 2026Article
- Deep Neural Networks for Automatic Atrial Fibrillation Detection Using Long-Term Ambulatory Electrocardiography: Retrospective Diagnostic Accuracy Study.JMIR cardio · 2026Article
- Diagnostic Approaches to Cardiac Arrhythmias: Integrating Electrocardiography and Advanced Monitoring.Cureus · 2026Review
- AI-enhanced approaches for personalized cardiac treatment: insights from ECG data.NPJ systems biology and applications · 2026Article
- AI-Enabled Sensor Technologies for Remote Arrhythmic Monitoring in High-Risk Cardiomyopathy Genotypes.Sensors (Basel, Switzerland) · 2026Review
- A hybrid model combining 1D-CNN and BERT for intelligent ECG arrhythmia classification.Scientific reports · 2025Article
- Article
- Review
- The Role of AI in Cardiovascular Event Monitoring and Early Detection: Scoping Literature Review.JMIR medical informatics · 2025Article
- Addressing myocardial infarction in South-Asian populations: risk factors and machine learning approaches.NPJ cardiovascular health · 2025Review
- Deep learning for the classification of atrial fibrillation using wavelet transform-based visual images.BMC medical informatics and decision making · 2025Article
- Determining the recurrence rate of premature ventricular complexes and idiopathic ventricular tachycardia after radiofrequency catheter ablation with the help of designing a machine-learning model.Regenerative therapy · 2024Article
- Computational modeling of cardiac electrophysiology and arrhythmogenesis: toward clinical translation.Physiological reviews · 2024Review
- Decoding 2.3 million ECGs: interpretable deep learning for advancing cardiovascular diagnosis and mortality risk stratification.European heart journal. Digital health · 2024Article
- Biometric contrastive learning for data-efficient deep learning from electrocardiographic images.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Generalising electrocardiogram detection and delineation: training convolutional neural networks with synthetic data augmentation.Frontiers in cardiovascular medicine · 2024Article
- New Innovations to Address Sudden Cardiac Arrest.US cardiology · 2024Review
- Validity of Computer-interpreted "Normal" and "Otherwise Normal" ECG in Emergency Department Triage Patients.The western journal of emergency medicine · 2024Article
- ECG-based convolutional neural network in pediatric obstructive sleep apnea diagnosis.Computers in biology and medicine · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Widely developed for clinical screening, electrocardiogram (ECG) recordings capture the cardiac electrical activity from the body surface. ECG analysis can therefore be a crucial first step to help diagnose, understand and predict cardiovascular disorders responsible for 30% of deaths worldwide. Computational techniques, and more specifically machine learning techniques and computational modelling are powerful tools for classification, clustering and simulation, and they have recently been applied to address the analysis of medical data, especially ECG data. This review describes the computational methods in use for ECG analysis, with a focus on machine learning and 3D computer simulations, as well as their accuracy, clinical implications and contributions to medical advances. The first section focuses on heartbeat classification and the techniques developed to extract and classify abnormal from regular beats. The second section focuses on patient diagnosis from whole recordings, applied to different diseases. The third section presents real-time diagnosis and applications to wearable devices. The fourth section highlights the recent field of personalized ECG computer simulations and their interpretation. Finally, the discussion section outlines the challenges of ECG analysis and provides a critical assessment of the methods presented. The computational methods reported in this review are a strong asset for medical discoveries and their translation to the clinical world may lead to promising advances.
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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.