Noninvasive detection of coronary artery disease using parametric spectral analysis methods

Author

Semmlow, J.L. ; Akay, M. ; Welkowitz, W.

Author_Institution

Dept. of Biomed. Eng., Rutgers Univ., NJ, USA

Volume

9

Issue

1

fYear

1990

fDate

3/1/1990 12:00:00 AM

Firstpage

33

Lastpage

36

Abstract

The detection of coronary artery disease by noninvasive analysis of isolated diastolic heart sounds is considered. It is based on identifying features associated with turbulent blood flow in partially occluded coronary arteries. The application of two types of parametric spectral analysis-autoregressive methods and eigenvector methods-to identify the additional signal components is discussed. Results obtained with one eigenvector method, (the MUSIC method) for spectra obtained from an angioplasty patient and results obtained with the autoregressive model in a comparison study of ten diseased and five normal patients are presented and discussed.<>

Keywords

acoustic signal processing; bioacoustics; cardiology; haemodynamics; spectral analysis; MUSIC method; additional signal components; autoregressive methods; coronary artery disease; eigenvector methods; isolated diastolic heart sounds; noninvasive detection; parametric spectral analysis methods; partially occluded coronary arteries; turbulent blood flow; Arteries; Autoregressive processes; Biological system modeling; Blood flow; Coronary arteriosclerosis; Diseases; Heart; Signal processing; Signal to noise ratio; Spectral analysis;

fLanguage

English

Journal_Title

Engineering in Medicine and Biology Magazine, IEEE

Publisher

ieee

ISSN

0739-5175

Type

jour

DOI

10.1109/51.62901

Filename

62901