Applications of rank-reduction to ECG analysis

Author

Semnani, RJ ; Womack, BF ; Diller, KR

Author_Institution

Dept. of Electr. & Biomed. Eng., Texas Univ., Austin, TX, USA

fYear

1998

fDate

13-16 Sep 1998

Firstpage

57

Lastpage

60

Abstract

The authors demonstrate application of SVD-based subspace techniques to electrocardiography. SVD, a high resolution spectrum estimation tool, is used to decompose the ECG data matrix into orthogonal subspaces. Due to the energy-preserving orthogonal transformations in the SVD, these subspaces correspond to the signal and noise components contained in the ECG data. Projection of the data onto the desired subspace eliminates the noise and the unwanted signal components

Keywords

electrocardiography; medical signal processing; singular value decomposition; spectral analysis; ECG analysis; ECG data matrix decomposition; SVD-based subspace techniques; data projection; electrodiagnostics; energy-preserving orthogonal transformations; high resolution spectrum estimation tool; noise components; orthogonal subspaces; rank-reduction applications; signal components; unwanted signal components; Biomedical engineering; Distortion; Electrocardiography; Energy resolution; Event detection; Matrix decomposition; Monitoring; Signal processing; Signal resolution; Spectral analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Computers in Cardiology 1998

Conference_Location

Cleveland, OH

ISSN

0276-6547

Print_ISBN

0-7803-5200-9

Type

conf

DOI

10.1109/CIC.1998.731714

Filename

731714