ArticleComputing in cardiology2023
Comparison of Machine Learning Detection of Low Left Ventricular Ejection Fraction Using Individual ECG Leads.
Article in Computing in cardiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The trial behind it
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Who cites it
7 citing papers in PubMed.
- Self-supervised contrastive learning enables robust electrocardiogram-based cardiac classification.Heart rhythm O2 · 2026Article
- A noninvasive hyperkalemia monitoring system for dialysis patients based on a 1D-CNN model and single-lead ECG from wearable devices.Scientific reports · 2025Article
- Machine Learning Estimation of Myocardial Ischemia Severity Using Body Surface ECG.Computing in cardiology · 2024Article
- A Survey of Augmentation Techniques for Enhancing ECG Representation Through Self-Supervised Contrastive Learning.Computing in cardiology · 2024Article
- Uncertainty Quantification of Fibrotic Conductivity Effects on Computational Model-Derived Ablation of Atypical Left Atrial Flutter.Computing in cardiology · 2024Article
- Machine Learning Prediction of Blood Potassium at Different Time Cutoffs.Computing in cardiology · 2024Article
- Comparison of Machine Learning Detection of Low Left Ventricular Ejection Fraction Using Individual ECG Leads.Computing in cardiology · 2023Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
The 12-lead electrocardiogram (ECG) is the most common front-line diagnosis tool for assessing cardiovascular health, yet traditional ECG analysis cannot detect many diseases. Machine learning (ML) techniques have emerged as a powerful set of techniques for producing automated and robust ECG analysis tools that can often predict diseases and conditions not detectable by traditional ECG analysis. Many contemporary ECG-ML studies have focused on utilizing the full 12-lead ECG; however, with the increased availability of single-lead ECG data from wearable devices, there is a clear motivation to explore the development of single-lead ECG-ML techniques. In this study we developed and applied a deep learning architecture for the detection of low left ventricular ejection fraction (LVEF), and compared the performance of this architecture when it was trained with individual leads of the 12-lead ECG to the performance when trained using the entire 12-lead ECG. We observed that single-lead-trained networks performed similarly to the full 12-lead-trained network. We also noted patterns of agreement and disagreement between network low LVEF predictions across the different lead-trained networks.
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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.