ArticlePloS one2021
Optimizing ECG to detect echocardiographic left ventricular hypertrophy with computer-based ECG data and machine learning.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning in Left Ventricular Hypertrophy Detection: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Screening for Left Ventricular Hypertrophy Using Artificial Intelligence Algorithms Based on 12 Leads of the Electrocardiogram-Applicable in Clinical Practice?-Critical Literature Review with Meta-Analysis.Healthcare (Basel, Switzerland) · 2025Review
- Deep learning assists early-detection of hypertension-mediated heart change on ECG signals.Hypertension research : official journal of the Japanese Society of Hypertension · 2025Article
- Article
- An ECG-Based Model for Left Ventricular Hypertrophy Detection: A Machine Learning Approach.IEEE open journal of engineering in medicine and biology · 2025Article
- Digital twinning of cardiac electrophysiology for congenital heart disease.Journal of the Royal Society, Interface · 2024Article
- Searching for the Best Machine Learning Algorithm for the Detection of Left Ventricular Hypertrophy from the ECG: A Review.Bioengineering (Basel, Switzerland) · 2024Review
- Digital twinning of cardiac electrophysiology for congenital heart disease.bioRxiv : the preprint server for biology · 2023Article
- Left ventricular hypertrophy detection using electrocardiographic signal.Scientific reports · 2023Article
- CoAt-Mixer: Self-attention deep learning framework for left ventricular hypertrophy using electrocardiography.PloS one · 2023Article
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4 authors.
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Abstract
backgroundLeft ventricular hypertrophy detected by echocardiography (Echo-LVH) is an independent predictor of mortality. Integration of the Philips DXL-16 algorithm into the electrocardiogram (ECG) extensively analyses the electricity of the heart. Machine learning techniques such as the C5.0 could lead to a new decision tree criterion to detect Echo-LVH.
objectivesTo search for a new combination of ECG parameters predictive of Echo-LVH. The final model is called the Cardiac Hypertrophy Computer-based model (CHCM).
methodsWe extracted the 458 ECG parameters provided by the Philips DXL-16 algorithm in patients with Echo-LVH and controls. We used the C5.0 ML algorithm to train, test, and validate the CHCM. We compared its diagnostic performance to validate state-of-the-art criteria in our patient cohort.
resultsWe included 439 patients and considered an alpha value of 0.05 and a power of 99%. The CHCM includes T voltage in I (≤0.055 mV), peak-to-peak QRS distance in aVL (>1.235 mV), and peak-to-peak QRS distance in aVF (>0.178 mV). The CHCM had an accuracy of 70.5% (CI95%, 65.2-75.5), a sensitivity of 74.3%, and a specificity of 68.7%. In the external validation cohort (n = 156), the CHCM had an accuracy of 63.5% (CI95%, 55.4-71), a sensitivity of 42%, and a specificity of 82.9%. The accuracies of the most relevant state-of-the-art criteria were: Romhilt-Estes (57.4%, CI95% 49-65.5), VDP Cornell (55.7%, CI95%47.6-63.7), Cornell (59%, CI95%50.8-66.8), Dalfó (62.9%, CI95%54.7-70.6), Sokolow Lyon (53.9%, CI95%45.7-61.9), and Philips DXL-16 algorithm (54.5%, CI95%46.3-62.5).
conclusionECG computer-based data and the C5.0 determined a new set of ECG parameters to predict Echo-LVH. The CHCM classifies patients as Echo-LVH with repolarization abnormalities or LVH with increased voltage. The CHCM has a similar accuracy, and is slightly more sensitive than the state-of-the-art criteria.
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