Long QT syndrome (LQTS) presents a group of inheritable channelopathies with prolonged ventricular repolarization, leading to syncope, ventricular tachycardia, and sudden death. Differentiating LQTS genotypes is crucial for targeted management and treatment, yet conventional genetic testing remains costly and time-consuming. This study aims to improve the distinction between LQTS genotypes, particularly LQT3, through a novel electrocardiogram (ECG)-based approach. Patients with LQT3 are at elevated risk due to arrhythmia triggers associated with rest and sleep. Employing a database of genotyped long QT syndrome E-HOL-03-0480-013 ECG signals, we introduced two innovative parameterization techniques-area under the ECG curve and wave transformation into the unit circle-to classify LQT3 against LQT1 and LQT2 genotypes. Our methodology utilized single-lead ECG data with a 200 Hz sampling frequency. The support vector machine (SVM) model demonstrated the ability to discriminate LQT3 with a recall of 90% and a precision of 81%, achieving an F1-score of 0.85. This parameterization offers a potential substitute for genetic testing and is practical for low frequencies. These single-lead ECG data could enhance smartwatches' functionality and similar cardiovascular monitoring applications. The results underscore the viability of ECG morphology-based genotype classification, promising a significant step towards streamlined diagnosis and improved patient care in LQTS.

Czech Institute of Informatics Robotics and Cybernetics Czech Technical University Prague 160 00 Prague Czech Republic

Department of Natural Sciences Faculty of Biomedical Engineering Czech Technical University Prague 272 01 Kladno Czech Republic

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Schwartz P.J., Priori S.G., Spazzolini C., Moss A.J., Vincent G.M., Napolitano C., Denjoy I., Guicheney P., Breithardt G., Keating M.T., et al. Genotype-Phenotype Correlation in the Long-QT Syndrome. Circulation. 2001;103:89–95. doi: 10.1161/01.cir.103.1.89. PubMed DOI

Abrams D.J., MacRae C.A. Long QT Syndrome. Circulation. 2014;129:1524–1529. doi: 10.1161/circulationaha.113.003985. PubMed DOI

Rohatgi R.K., Sugrue A., Bos J.M., Cannon B.C., Asirvatham S.J., Moir C., Owen H.J., Bos K.M., Kruisselbrink T., Ackerman M.J. Contemporary Outcomes in Patients With Long QT Syndrome. J. Am. Coll. Cardiol. 2017;70:453–462. doi: 10.1016/j.jacc.2017.05.046. PubMed DOI

Goldenberg I., Moss A.J. Long QT Syndrome. J. Am. Coll. Cardiol. 2008;51:2291–2300. doi: 10.1016/j.jacc.2008.02.068. PubMed DOI

Schwartz P.J., Crotti L., Insolia R. Long-QT Syndrome. Circ. Arrhythmia Electrophysiol. 2012;5:868–877. doi: 10.1161/CIRCEP.111.962019. PubMed DOI PMC

Zareba W., Cygankiewicz I. Long QT Syndrome and Short QT Syndrome. Prog. Cardiovasc. Dis. 2008;51:264–278. doi: 10.1016/j.pcad.2008.10.006. PubMed DOI

Khositseth A., Tester D.J., Will M.L., Bell C.M., Ackerman M.J. Identification of a Common Genetic Substrate Underlying Postpartum Cardiac Events in Congenital Long QT Syndrome. Heart Rhythm. 2004;1:60–64. doi: 10.1016/j.hrthm.2004.01.006. PubMed DOI

Moss A.J., Robinson J.L., Gessman L., Gillespie R., Zareba W., Schwartz P.J., Vincent G.M., Benhorin J., Heilbron E.L., Towbin J.A., et al. Comparison of Clinical and Genetic Variables of Cardiac Events Associated with Loud Noise Versus Swimming Among Subjects with the Long QT Syndrome. Am. J. Cardiol. 1999;84:876–879. doi: 10.1016/s0002-9149(99)00458-0. PubMed DOI

Shimizu W., Antzelevitch C. Differential Effects of Beta-Adrenergic Agonists and Antagonists in LQT1, LQT2 and LQT3 Models of the Long QT Syndrome. J. Am. Coll. Cardiol. 2000;35:778–786. doi: 10.1016/S0735-1097(99)00582-3. PubMed DOI

Tardo D.T., Peck M., Subbiah R.N., Vandenberg J.I., Hill A.P. The Diagnostic Role of T Wave Morphology Biomarkers in Congenital and Acquired Long QT Syndrome: A Systematic Review. Ann. Noninvasive Electrocardiol. 2023;28:e13015. doi: 10.1111/anec.13015. PubMed DOI PMC

Bos J.M., Attia Z.I., Albert D.E., Noseworthy P.A., Friedman P.A., Ackerman M.J. Use of Artificial Intelligence and Deep Neural Networks in Evaluation of Patients with Electrocardiographically Concealed Long QT Syndrome from the Surface 12-Lead Electrocardiogram. JAMA Cardiol. 2021;6:532–538. doi: 10.1001/jamacardio.2020.7422. PubMed DOI PMC

Gima K., Rudy Y. Ionic Current Basis of Electrocardiographic Waveforms: A Model Study. Circ. Res. 2002;90:889–896. doi: 10.1161/01.res.0000016960.61087.86. PubMed DOI PMC

Shimizu W., Antzelevitch C. Cellular Basis for the ECG Features of the LQT1 Form of the Long-QT Syndrome. Circulation. 1998;98:2314–2322. doi: 10.1161/01.cir.98.21.2314. PubMed DOI

Shimizu W., Antzelevitch C. Sodium Channel Block with Mexiletine Is Effective in Reducing Dispersion of Repolarization and Preventing Torsade de Pointes in LQT2 and LQT3 Models of the Long-QT Syndrome. Circulation. 1997;96:2038–2047. doi: 10.1161/01.cir.96.6.2038. PubMed DOI

Zhang L., Timothy K.W., Vincent G.M., Lehmann M.H., Fox J., Giuli L.C., Shen J., Splawski I., Priori S.G., Compton S.J., et al. Spectrum of ST-T–Wave Patterns and Repolarization Parameters in Congenital Long-QT Syndrome. Circulation. 2000;102:2849–2855. doi: 10.1161/01.CIR.102.23.2849. PubMed DOI

Couderc J.-P. A Unique Digital Electrocardiographic Repository for the Development of Quantitative Electrocardiography and Cardiac Safety: The Telemetric and Holter ECG Warehouse (THEW) J. Electrocardiol. 2010;43:595–600. doi: 10.1016/j.jelectrocard.2010.07.015. PubMed DOI PMC

Malik M., Bigger J.T., Camm A.J., Kleiger R.E., Malliani A., Moss A.J., Schwartz P.J. Heart Rate Variability: Standards of Measurement, Physiological Interpretation, and Clinical Use. Eur. Heart J. 1996;17:354–381. doi: 10.1093/oxfordjournals.eurheartj.a014868. PubMed DOI

Goldberger A.L., Amaral L.A., Glass L., Hausdorff J.M., Ivanov P.C., Mark R.G., Mietus J.E., Moody G.B., Peng C.-K., Stanley H.E. PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation. 2000;101:e215–e220. PubMed

Vest A.N., Poian G.D., Li Q., Liu C., Nemati S., Shah A.J., Clifford G.D. An Open Source Benchmarked Toolbox for Cardiovascular Waveform and Interval Analysis. Physiol. Meas. 2018;39:105004. doi: 10.1088/1361-6579/aae021. PubMed DOI PMC

Pilia N., Nagel C., Lenis G., Becker S., Dössel O., Loewe A. ECGdeli—An Open Source ECG Delineation Toolbox for MATLAB. SoftwareX. 2021;13:100639. doi: 10.1016/j.softx.2020.100639. DOI

Jiang R., Cheung C.C., Garcia-Montero M., Davies B., Cao J., Redfearn D., Laksman Z.M., Grondin S., Atallah J., Escudero C.A., et al. Deep Learning–Augmented ECG Analysis for Screening and Genotype Prediction of Congenital Long QT Syndrome. JAMA Cardiol. 2024;9:377–384. doi: 10.1001/jamacardio.2024.0039. PubMed DOI PMC

Andrew A.M. An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods by Nello Christianini and John Shawe-Taylor, Cambridge University Press, Cambridge, 2000, Xiii+189 pp., ISBN 0-521-78019-5 (Hbk, £27.50) Robotica. 2000;18:687–689.

Lundberg S.M., Lee S.-I. A Unified Approach to Interpreting Model Predictions; Proceedings of the 31st Annual Conference on Neural Information Processing Systems (NIPS): Advances in Neural Information Processing Systems 30; Long Beach, CA, USA. 4–9 December 2017; San Francisco, CA, USA: Curran Associates, Inc.; 2017.

Immanuel S.A., Sadrieh A., Baumert M., Couderc J.P., Zareba W., Hill A.P., Vandenberg J.I. T-Wave Morphology Can Distinguish Healthy Controls from LQTS Patients. Physiol. Meas. 2016;37:1456. doi: 10.1088/0967-3334/37/9/1456. PubMed DOI