ArticlePloS one2017
The development and validation of an easy to use automatic QT-interval algorithm.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Explainable electrocardiogram interpretation using deep learning-based semantic segmentation.European heart journal. Digital health · 2026Article
- 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 · 2025Article
- Time-dependent repolarization changes following left bundle branch area pacing vs. conventional biventricular pacing in patients with dyssynchronous heart failure.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2025Article
- Feasibility Analysis of ECG-Based pH Estimation for Asphyxia Detection in Neonates.Sensors (Basel, Switzerland) · 2024Article
- Single-lead arrhythmia detection through machine learning: cross-sectional evaluation of a novel algorithm using real-world data.Open heart · 2023Article
- Manual vs. automatic assessment of the QT-interval and corrected QT.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2023Article
- Diagnostic Accuracy of the Standing Test in Adults Suspected for Congenital Long-QT Syndrome.Journal of the American Heart Association · 2023Article
- Convulsant doses of abused synthetic cannabinoid receptor agonists AB-PINACA, 5F-AB-PINACA, 5F-ADB-PINACA and JWH-018 do not elicit electroencephalographic (EEG) seizures in male mice.Psychopharmacology · 2022Article
- QTc prolongation in adolescents with acute alcohol intoxication.European journal of pediatrics · 2022Observational
- Determining anatomical and electrophysiological detail requirements for computational ventricular models of porcine myocardial infarction.Computers in biology and medicine · 2022Article
- Demographic and Methodological Heterogeneity in Electrocardiogram Signals From Guinea Pigs.Frontiers in physiology · 2022Article
- Does pulmonary vein isolation prolong QT-interval?- Authors' reply.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2021Article
- Effect of citric-acid dialysate on the QTC-interval.Scientific reports · 2021Article
- Diagnostic accuracy of the response to the brief tachycardia provoked by standing in children suspected for long QT syndrome.Heart rhythm O2 · 2021Article
- Pulmonary vein isolation in a real-world population does not influence QTc interval.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2021Article
- Information Flow Between Heart Rhythm, Repolarization, and the Diastolic Interval Series for Healthy Individuals and LQTS1 Patients.Frontiers in physiology · 2021Article
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTo evaluate QT-interval dynamics in patients and in drug safety analysis, beat-to-beat QT-interval measurements are increasingly used. However, interobserver differences, aberrant T-wave morphologies and changes in heart axis might hamper accurate QT-interval measurements.
objectiveTo develop and validate a QT-interval algorithm robust to heart axis orientation and T-wave morphology that can be applied on a beat-to-beat basis.
methodsAdditionally to standard ECG leads, the root mean square (ECGRMS), standard deviation and vectorcardiogram were used. QRS-onset was defined from the ECGRMS. T-wave end was defined per individual lead and scalar ECG using an automated tangent method. A median of all T-wave ends was used as the general T-wave end per beat. Supine-standing tests of 73 patients with Long-QT syndrome (LQTS) and 54 controls were used because they have wide ranges of RR and QT-intervals as well as changes in T-wave morphology and heart axis orientation. For each subject, automatically estimated QT-intervals in three random complexes chosen from the low, middle and high RR range, were compared with manually measured QT-intervals by three observers.
resultsAfter visual inspection of the randomly selected complexes, 21 complexes were excluded because of evident noise, too flat T-waves or premature ventricular beats. Bland-Altman analyses of automatically and manually determined QT-intervals showed a bias of <4ms and limits of agreement of ±25ms. Intra-class coefficient indicated excellent agreement (>0.9) between the algorithm and all observers individually as well as between the algorithm and the mean QT-interval of the observers.
conclusionOur automated algorithm provides reliable beat-to-beat QT-interval assessment, robust to heart axis and T-wave morphology.
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