{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T12:22:13Z","timestamp":1777983733839,"version":"3.51.4"},"reference-count":26,"publisher":"IOP Publishing","issue":"2","license":[{"start":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T00:00:00Z","timestamp":1775779200000},"content-version":"vor","delay-in-days":9,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T00:00:00Z","timestamp":1775779200000},"content-version":"tdm","delay-in-days":9,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"name":"European Union\u2019s HORIZON EUROPE research and innovation programme","award":["101136607"],"award-info":[{"award-number":["101136607"]}]},{"name":"Czech Technical University in Prague, Prague, Czech Republic"}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Biomed. Phys. Eng. Express"],"published-print":{"date-parts":[[2026,4,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Long QT Syndrome (LQTS) is an inherited cardiac disorder characterized by dysfunctional cardiac ion channels, which result in prolonged QT intervals on electrocardiograms (ECGs). LQTS can lead to severe clinical manifestations, including syncope, ventricular arrhythmias, and sudden cardiac death. Effective genotype-specific management strategies are essential to mitigate the risk of life-threatening arrhythmias. This study aims to achieve automatic discrimination among the LQT1, LQT2, and LQT3 genotypes to enable targeted treatment and prevention strategies. Utilizing ECG data from the Telemetric and Holter ECG Warehouse\u2019s LQTS database, our methodology involves an automated extraction process of short ECG signals, geometric parameterization techniques, and classification using a two-stage cascade of binary support vector machine classifiers. The input features for the classifiers are derived from Lead I ECG signals sampled at 200 Hz, highlighting the potential application in developing single-lead ECG monitoring devices and applications, such as widely used smartwatches, for which short recording duration, low sampling frequency, and arm-to-arm lead measurements are fundamental prerequisites for practical use. The proposed classifier achieved 71% weighted accuracy on out-of-sample data (LQT1: 65% recall, 58% precision; LQT2: 79% recall, 82% precision; and LQT3: 71% recall, 77% precision). Our findings demonstrate the feasibility of noninvasive genotype differentiation for LQTS based on the morphological analysis of ECG signals, providing an advancement in the field of personalized cardiology and the development of portable diagnostic tools.<\/jats:p>","DOI":"10.1088\/2057-1976\/ae58ae","type":"journal-article","created":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T14:24:26Z","timestamp":1775831066000},"page":"025067","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Differentiating long QT syndrome genotypes using electrocardiographic geometric parameterization and machine learning approaches"],"prefix":"10.1088","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-4831-2391","authenticated-orcid":false,"given":"Martina","family":"Srutova","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0742-5645","authenticated-orcid":false,"given":"Lenka","family":"Lhotska","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9844-7617","authenticated-orcid":true,"given":"Vaclav","family":"Kremen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"266","published-online":{"date-parts":[[2026,4,10]]},"reference":[{"key":"bpexae58aebib1","doi-asserted-by":"publisher","first-page":"1524","DOI":"10.1161\/CIRCULATIONAHA.113.003985","type":"journal-article","article-title":"Long QT syndrome","volume":"129","author":"Abrams","year":"2014","journal-title":"Circulation"},{"key":"bpexae58aebib2","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1186\/s12916-022-02350-z","type":"journal-article","article-title":"A deep learning approach identifies new ECG features in congenital long QT syndrome","volume":"20","author":"Aufiero","year":"2022","journal-title":"BMC Med."},{"key":"bpexae58aebib3","doi-asserted-by":"publisher","first-page":"532","DOI":"10.1001\/jamacardio.2020.7422","type":"journal-article","article-title":"Use of artificial intelligence and deep neural networks in evaluation of patients with electrocardiographically concealed long QT syndrome from the surface 12-lead electrocardiogram","volume":"6","author":"Bos","year":"2021","journal-title":"JAMA Cardiol."},{"key":"bpexae58aebib4","doi-asserted-by":"publisher","first-page":"1018","DOI":"10.1161\/01.cir.94.5.1018","type":"journal-article","article-title":"Genetically defined therapy of inherited long-QT syndrome","volume":"94","author":"Compton","year":"1996","journal-title":"Circulation"},{"key":"bpexae58aebib5","doi-asserted-by":"publisher","first-page":"595","DOI":"10.1016\/j.jelectrocard.2010.07.015","type":"dataset","article-title":"A unique digital electrocardiographic repository for the development of quantitative electrocardiography and cardiac safety: the Telemetric and Holter ECG Warehouse (THEW)","volume":"43","author":"Couderc","year":"2010","unstructured":"Couderc J-P 2010 A unique digital electrocardiographic repository for the development of quantitative electrocardiography and cardiac safety: the Telemetric and Holter ECG Warehouse (THEW) J. 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