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Transl. Res."],"published-print":{"date-parts":[[2025,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>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\u2019s tangent method, as well as parameters underlying T-wave morphology in digital electrocardiograms (ECGs) of 466 patients with LQTS. The algorithm\u2019s 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\u2122 system. Applying an optimizable Support Vector Machine classifier on the algorithm\u2019s 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\u00ae-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.<\/jats:p>\n                  <jats:p>\n                    <jats:bold>Graphical Abstract<\/jats:bold>\n                  <\/jats:p>","DOI":"10.1007\/s12265-025-10693-0","type":"journal-article","created":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T16:25:30Z","timestamp":1759249530000},"page":"1470-1481","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["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"],"prefix":"10.1007","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2011-2101","authenticated-orcid":false,"given":"Elinor","family":"Tzvi-Minker","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sven","family":"Dittmann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Corinna","family":"Rickert","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andreas","family":"Keck","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eric","family":"Schulze-Bahr","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,9,30]]},"reference":[{"issue":"6","key":"10693_CR1","doi-asserted-by":"publisher","first-page":"418","DOI":"10.1161\/CIRCULATIONAHA.119.043132","volume":"141","author":"A Adler","year":"2020","unstructured":"Adler A, Novelli V, Amin AS, Abiusi E, Care M, Nannenberg EA, Feilotter H, Amenta S, Mazza D, Bikker H, Sturm AC, Garcia J, Ackerman MJ, Hershberger RE, Perez MV, Zareba W, Ware JS, Wilde AAM, Gollob MH. An international, multicentered, evidence-based reappraisal of genes reported to cause congenital long QT syndrome. Circulation. 2020;141(6):418\u201328. https:\/\/doi.org\/10.1161\/CIRCULATIONAHA.119.043132.","journal-title":"Circulation"},{"issue":"1","key":"10693_CR2","doi-asserted-by":"publisher","DOI":"10.1186\/S12916-022-02350-Z","volume":"20","author":"S Aufiero","year":"2022","unstructured":"Aufiero S, Bleijendaal H, Robyns T, Vandenberk B, Krijger C, Bezzina C, Zwinderman AH, Wilde AAM, Pinto YM. A deep learning approach identifies new ECG features in congenital long QT syndrome. BMC Med. 2022;20(1): 162. https:\/\/doi.org\/10.1186\/S12916-022-02350-Z.","journal-title":"BMC Med"},{"key":"10693_CR3","doi-asserted-by":"publisher","DOI":"10.1111\/ANEC.12804","author":"P Dahlberg","year":"2021","unstructured":"Dahlberg P, Diamant UB, Gilljam T, Rydberg A, Bergfeldt L. QT correction using Bazett\u2019s formula remains preferable in long QT syndrome type 1 and 2. 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