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

  • 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-meansquared-error (RMSE) values between the QRS-T complexes of recorded and derived leads. Typical mean RMSE values associated with the regressionLinear 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-meansquared-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.show moreshow less

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Metadaten
Author:Daniel GüldenringORCiD, Ali S. Rababah, Dewar D. FinlayORCiD, Raymond R. BondORCiD, Alan KennedyORCiD, Michael R. Jennings, Khaled Rjoob, James D. McLaughlinORCiD
DOI:https://doi.org/10.22489/CinC.2020.330
Identifier:9781728111056 OPAC HS OPAC extern
Identifier:2325-887X OPAC HS OPAC extern
Parent Title (English):Computing in Cardiology (CinC)
Publisher:IEEE
Place of publication:Piscataway
Document Type:conference proceeding (article)
Conference:2020 Computing in Cardiology (CinC) 13-16. Sept. 2020, Rimini, Italy
Language:English
Date of Publication (online):2021/02/10
Year of first Publication:2020
Tag:Biomedizinische Signalverarbeitung; EKG
ECG
Volume:2020
Number of pages:4 Seiten
First Page:1
Last Page:4
Institutes:Fakultät Elektrotechnik
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften
Publication Lists:Güldenring, Daniel
Publication reviewed:begutachtet
Release Date:2021/02/17
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