Regression or Pseudo-Inverse - Which Method Should be Preferred When Developing Inverse Linear ECG-Lead Transformations?

Daniel Güldenring, Ali Rababah, D Finlay, RR Bond, Alan Kennedy, Michael Jennings, Khaled Rjoob, James McLaughlin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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Linear ECG-lead transformations estimate or derive unrecorded target leads by applying a number of recorded basis leads to a so-called linear ECG-lead transformation matrix. The inverse transform of such a linear ECG-lead transformation performs a transformation in the opposite direction (from the target leads to the basis leads). The pseudo-inverse of a given transformation matrix can be used to perform such an inverse transformation. Linear regression based inverse transformation matrices are, provided that sufficient training data for their development is available, an alternative to pseudo-inverse matrices. The aim of this research was to compare the estimation performance of pseudo-inverse and linear regression based inverse transformations. This comparison was performed for two example inverse transformations. The performance of the different transformations was assessed using root-mean-squared-error (RMSE) values between the QRS-T complexes of recorded and derived leads. Typical mean RMSE values associated with the regression based approach were found to be approximately two thirds to half of the mean RMSE values achieved by the approach based upon the pseudo-inverse. Provided that sufficient data are available, linear regression should be used for the development of inverse ECG-lead transformation matrices.
Original languageEnglish
Title of host publication2020 Computing in Cardiology
Place of PublicationRimini, Italy
PublisherIEEE Xplore
ISBN (Electronic)978-1-7281-7382-5
ISBN (Print)978-1-7281-1105-6
Publication statusPublished - 10 Feb 2021
EventComputing in Cardiology 2020 - Palacongressi, Rimini, Italy
Duration: 13 Sep 202016 Sep 2020

Publication series

NameComputing in Cardiology
ISSN (Print)2325-8861
ISSN (Electronic)2325-887X


ConferenceComputing in Cardiology 2020
Abbreviated titleCinC20


  • ECG-Lead Transformations
  • ECG data analysis
  • Inverse solution
  • regression


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