Analysis of atrial fibrillation after CABG using wavelets

Author

Maglaveras, Nicos ; Chouvarda, I. ; Dakos, G. ; Vasilikos, V. ; Mochlas, S. ; Louridas, G.

Author_Institution

Lab. of Med. Inf., Aristotelian Univ. of Thessaloniki, Greece

fYear

2002

fDate

22-25 Sept. 2002

Firstpage

89

Lastpage

92

Abstract

In the present study, Wavelet analysis of P wave for the prediction of Atrial Fibrillation after CABG is evaluated. Continuous Wavelet Transform is applied to ECG and Mean/Max parameters are calculated within the P window for different frequency bands. Thus, 24 parameters are available, which, along with the 4 window lengths (corresponding to P wave length of X, Y, Z, V signals), make a pool of available parameters to be used for classification. Linear regression is used for the classification of the two groups and bootstrapping is applied in order to enhance statistical robustness. The features to be used in the regression model are selected from the pool of available parameters by use of an iterative procedure. The outcome of the feature selection procedure shows that X and Z-axis features as well as vector-magnitude features are the most important ones for the prediction of Atrial Fibrillation after CABG.

Keywords

electrocardiography; medical signal processing; statistical analysis; wavelet transforms; CABG; P wave; atrial fibrillation; continuous wavelet transform; feature selection procedure; iterative procedure; linear regression; statistical robustness; vector-magnitude features; wavelet analysis; Atrial fibrillation; Biomedical informatics; Cardiology; Continuous wavelet transforms; Electrocardiography; Frequency; Hospitals; Robustness; Wavelet analysis; Wavelet transforms;

fLanguage

English

Publisher

ieee

Conference_Titel

Computers in Cardiology, 2002

ISSN

0276-6547

Print_ISBN

0-7803-7735-4

Type

conf

DOI

10.1109/CIC.2002.1166714

Filename

1166714