Effects of noise and filtering on SVD-based morphological parameters of the T wave in the ECG

Singular value decomposition (SVD) based electrocardiogram (ECG) morphology analysis is a novel method in the assessment of subtle abnormalities in the T wave morphology of 12-lead ECG. As various types of noise contaminate the ECG signal and create a bias for the morphological analyses, this study...

Full description

Saved in:
Bibliographic Details
Published in:Journal of medical engineering & technology 2008-09, Vol.32 (5), p.400-407
Main Authors: Lehtola, L., Karsikas, M., Koskinen, M., Huikuri, H., Seppänen, T.
Format: Article
Language:English
Subjects:
Electrocardiography - instrumentation
Electrocardiography - methods
Humans
Infarction
Myocardial Infarction - diagnosis
Myocardial Infarction - physiopathology
PCA ratio
Reproducibility of Results
Savitzky-Golay
Signal Processing, Computer-Assisted
TWR
Wavelet
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Singular value decomposition (SVD) based electrocardiogram (ECG) morphology analysis is a novel method in the assessment of subtle abnormalities in the T wave morphology of 12-lead ECG. As various types of noise contaminate the ECG signal and create a bias for the morphological analyses, this study was designed to estimate the effects of noise on the SVD method in an experimental setup. Ideal signals were generated by filtering real ECG signals several times with the Savitzky-Golay filter. Random and real noise samples were superimposed on the ideal signals. The noisy signals were filtered with a power line interference filter combined with the Savitzky-Golay or the wavelet filter. Results show that noise increased both the dipolar and non-dipolar components significantly unless filtering was applied. R-TWR (relative T wave residuum) and A-TWR (absolute T wave residuum) were four to eight times higher in noisy signals. The experiments with patient data demonstrated that certain types of noise may even lead to erroneous classification of patients. Filtering brings the median values closer to the correct ones and decreases significantly the variance of the values of parameters.
ISSN:0309-1902
1464-522X
DOI:10.1080/03091900701248713