ArticleJournal of cardiovascular translational research2025
A MATLAB Algorithm to Automatically Estimate the QT Interval and Other ECG Parameters and Validation Using a Machine Learning Approach in Congenital Long-QT Syndrome.
Article in Journal of cardiovascular translational research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Comment on "A MATLAB Algorithm to Automatically Estimate the QT Interval and Other ECG Parameters and Validation Using a Machine Learning Approach in Congenital Long-QT Syndrome".Journal of cardiovascular translational research · 2026Article
- Polygenic risk modulates myocardial repolarization and T-wave geometry in congenital long-QT syndrome type 1: evidence from digital ECG phenotyping.Frontiers in cardiovascular medicine · 2026Article
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Authors and funding
5 authors.
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Abstract
Myocardial repolarization and QT duration are crucial markers for diagnosis and monitoring of congenital long QT syndrome (LQTS). Here we present a novel algorithm to automatically estimate the QT interval based on Lepeschkin's tangent method, as well as parameters underlying T-wave morphology in digital electrocardiograms (ECGs) of 466 patients with LQTS. The algorithm's performance was validated using ECG data from 40 healthy controls. The results were compared against expert measurement of the QT interval, as well as against the results of the ECG device MUSE™ system. Applying an optimizable Support Vector Machine classifier on the algorithm's outcomes achieved an accuracy of 78.1% and area under the curve of 0.85 in classifying LQTS patients with a prolonged QTc interval (upon QT_GS) from those with a normal QTc interval. The presented MATLAB®-based algorithm offers a transparent and reproducible approach to automatic QT interval estimation and QTc calculation in LQTS patients, potentially improving automatized screening, diagnostic precision and patient management.
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