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The derivation of the spatial QRS-T angle and the spatial ventricular gradient using the Mason–Likar 12-lead electrocardiogram

Guldenring, Daniel, Finlay, Dewar, Bond, Raymond, Kennedy, Alan and McLaughlin, James (2015) The derivation of the spatial QRS-T angle and the spatial ventricular gradient using the Mason–Likar 12-lead electrocardiogram. Journal of Electrocardiology, 48 (6). pp. 1045-1052. [Journal article]

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URL: http://www.sciencedirect.com/science/article/pii/S0022073615002319

DOI: doi:10.1016/j.jelectrocard.2015.08.009


Research has shown that the ‘spatial QRS-T angle’ (SA) and the ‘spatial ventricular gradient’ (SVG) have clinical value in a number of different applications. The determination of the SA and the SVG requires vectorcardiographic data. Such data is seldom recorded in clinical practice. The SA and the SVG are therefore frequently derived from 12-lead electrocardiogram (ECG) data using linear lead transformation matrices. This research compares the performance of two previously published linear lead transformation matrices (Kors and ML2VCG) in deriving the SA and the SVG from Mason-Likar (ML) 12-lead ECG data. This comparison was performed through an analysis of the estimation errors that are made when deriving the SA and the SVG for all 181 subjects in the study population. The estimation errors were quantified as the systematic error (mean difference) and the random error (span of the Bland-Altman 95% limits of agreement). The random error was found to be the dominating error component for both the Kors and the ML2VCG matrix. The random error [ML2VCG; Kors; result of the paired, two-sided Pitman-Morgan test for statistical significance of differences in the error variance between ML2VCG and Kors] for the vectorcardiographic parameters SA, magnitude of the SVG, elevation of the SVG and azimuth of the SVG were found to be [37.33°; 50.52°; p < 0.001], [30.17 mV ms; 39.09 mV ms; p < 0.001], [36.77°; 47.62°; p = 0.001] and [63.45°; 80.32°; p < 0.001] respectively. The findings of this research indicate that in comparison to the Kors matrix the ML2VCG provides greater precision for estimating the SA and SVG from ML 12-lead ECG data.

Item Type:Journal article
Keywords:ECG, data science, health informatics
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
Faculty of Computing & Engineering > School of Engineering
Research Institutes and Groups:Engineering Research Institute
Engineering Research Institute > Nanotechnology & Integrated BioEngineering Centre (NIBEC)
Computer Science Research Institute > Smart Environments
Computer Science Research Institute
ID Code:32531
Deposited By: Dr Raymond Bond
Deposited On:04 Nov 2015 11:02
Last Modified:04 Nov 2015 11:02

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