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Type: Journal article
Title: T-wave morphology can distinguish healthy controls from LQTS patients
Author: Immanuel, S.
Sadrieh, A.
Baumert, M.
Couderc, J.
Zareba, W.
Hill, A.
Vandenberg, J.
Citation: Physiological Measurement, 2016; 37(9):1456-1473
Publisher: IOP Publishing
Issue Date: 2016
ISSN: 0967-3334
Statement of
S A Immanuel, A Sadrieh, M Baumert, J P Couderc, W Zareba, A P Hill and J I Vandenberg
Abstract: Long QT syndrome (LQTS) is an inherited disorder associated with prolongation of the QT/QTc interval on the surface electrocardiogram (ECG) and a markedly increased risk of sudden cardiac death due to cardiac arrhythmias. Up to 25% of genotype-positive LQTS patients have QT/QTc intervals in the normal range. These patients are, however, still at increased risk of life-threatening events compared to their genotype-negative siblings. Previous studies have shown that analysis of T-wave morphology may enhance discrimination between control and LQTS patients. In this study we tested the hypothesis that automated analysis of T-wave morphology from Holter ECG recordings could distinguish between control and LQTS patients with QTc values in the range 400–450 ms. Holter ECGs were obtained from the Telemetric and Holter ECG Warehouse (THEW) database. Frequency binned averaged ECG waveforms were obtained and extracted T-waves were fitted with a combination of 3 sigmoid functions (upslope, downslope and switch) or two 9th order polynomial functions (upslope and downslope). Neural network classifiers, based on parameters obtained from the sigmoid or polynomial fits to the 1 Hz and 1.3 Hz ECG waveforms, were able to achieve up to 92% discrimination between control and LQTS patients and 88% discrimination between LQTS1 and LQTS2 patients. When we analysed a subgroup of subjects with normal QT intervals (400–450 ms, 67 controls and 61 LQTS), T-wave morphology based parameters enabled 90% discrimination between control and LQTS patients, compared to only 71% when the groups were classified based on QTc alone. In summary, our Holter ECG analysis algorithms demonstrate the feasibility of using automated analysis of T-wave morphology to distinguish LQTS patients, even those with normal QTc, from healthy controls.
Keywords: cardiac arrhythmia; sudden cardiac death; long QT syndrome; Holter ECG; T-wave morphology; neural network classifier
Rights: © 2016 Institute of Physics and Engineering in Medicine
DOI: 10.1088/0967-3334/37/9/1456
Published version:;jsessionid=9C9413EE78A091B94962651E08C63656.ip-10-40-2-225
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