DocumentCode :
1397853
Title :
Modeling sound generation in stenosed coronary arteries
Author :
Wang, Jin-zhao ; Tie, Bing ; Welkowitz, W. ; Semmlow, John L. ; Kostis, John B.
Author_Institution :
Dept. of Biomed. Eng., Rutgers Univ., Piscataway, NJ, USA
Volume :
37
Issue :
11
fYear :
1990
Firstpage :
1087
Lastpage :
1094
Abstract :
Acoustic measurements obtained from sensitive microphones placed on the chest are used in a procedure to noninvasively diagnose coronary artery disease. Utilizing specially developed signal processing techniques, the spectral content of isolated diastolic heart sounds has been estimated, and these sounds usually show an increase in high-frequency components in patients with occlusive coronary arteries. In order to establish a theory for the origin of these spectral features, a sound source model has been developed which combines an incremental network model of the left coronary artery tree with a transfer function model describing arterial chamber resonant characteristics. The network model predicts flow in both normal and stenosed coronary arteries. From this flow information, the arterial chamber transfer function model predicts the development of acoustic signals from the chamber resonant characteristics. The transfer function of a segment of coronary artery demonstrates two resonance frequencies. These resonance frequencies depend on the length and diameter of the chamber segment, as well as on the distal hydraulic impedance loading the segment.
Keywords :
bioacoustics; cardiology; physiological models; arterial chamber resonant characteristics; distal hydraulic impedance; isolated diastolic heart sounds; left coronary artery tree; network model; sound generation modelling noninvasive diagnosis; stenosed coronary arteries; transfer function model; Acoustic measurements; Acoustic signal processing; Arteries; Coronary arteriosclerosis; Heart; Microphones; Predictive models; Resonance; Resonant frequency; Transfer functions; Coronary Circulation; Coronary Disease; Heart Sounds; Humans; Models, Cardiovascular; Predictive Value of Tests; Signal Processing, Computer-Assisted;
fLanguage :
English
Journal_Title :
Biomedical Engineering, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9294
Type :
jour
DOI :
10.1109/10.61034
Filename :
61034
Link To Document :
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