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Influence of the training set composition on the estimation performance of linear ECG-lead transformations.

Guldenring, Daniel; Finlay, Dewar D.; Bond, Raymond R.; Kennedy, Alan; Doggart, Peter; Janjua, Ghalib; McLaughlin, James

Authors

Daniel Guldenring

Dewar D. Finlay

Raymond R. Bond

Alan Kennedy

Peter Doggart

James McLaughlin



Abstract

Linear ECG-lead transformations (LELTs) are used to estimate unrecorded target leads by applying a number of recorded basis leads to a LELT matrix. Such LELT matrices are commonly developed using training datasets that are composed of ECGs that belong to different diagnostic classes (DCs). The aim of our research was to assess the influence of the training set composition on the estimation performance of LELTs that estimate target leads V1, V3, V4 and V6 from basis leads I, II, V2 and V5 of the 12-lead ECG. Our assessment was performed using ECGs from the three DCs left ventricular hypertrophy, right bundle branch block and normal (ECGs without abnormalities). Training sets with different DC compositions were used for the development of LELT matrices. These matrices were used to estimate the target leads of different test sets. The estimation performance of the developed matrices was quantified using root mean square error values calculated between derived and recorded target leads. Our findings indicate that unbalanced training sets can lead to LELTs that show large estimation performance variability across different DCs. Balanced training sets were found to produce LELTs that performed well across multiple DCs. We recommend balanced training sets for the development of LELTs.

Citation

GULDENRING, D., FINLAY, D.D., BOND, R.R., KENNEDY, A., DOGGART, P., JANJUA, G. and MCLAUGHLIN, J. 2023. Influence of the training set composition on the estimation performance of linear ECG-lead transformations. In Proceedings of the 50th Computing in cardiology 2023 (CinC 2023), 1-4 October 2023, Atlanta, GA, USA. Piscataway: IEEE/CinC [online], 50, article number 263. Available from: https://doi.org/10.22489/CinC.2023.263

Conference Name 50th Computing in cardiology 2023 (CinC 2023)
Conference Location Atlanta, GA, USA
Start Date Oct 1, 2023
End Date Oct 4, 2023
Acceptance Date May 1, 2023
Online Publication Date Oct 3, 2023
Publication Date Dec 31, 2023
Deposit Date Jan 25, 2024
Publicly Available Date Jan 25, 2024
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Series Title Computing in cardiology
Series Number 50
Series ISSN 2325-8861
ISBN 9798350382525
DOI https://doi.org/10.22489/CinC.2023.263
Keywords Linear ECG-lead transformations (LELTs); Linear electrocardiographic (ECG);
Public URL https://rgu-repository.worktribe.com/output/2218670

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