Acoustic detection of coronary artery disease.
Coronary artery disease (CAD) occurs when the arteries to the heart (the coronary arteries) become blocked by deposition of plaque, depriving the heart of oxygen-bearing blood. This disease is arguably the most important fatal disease in industrialized countries, causing one-third to one-half of all deaths in persons between the ages of 35 and 64 in the United States. Despite the fact that early detection of CAD allows for successful and cost-effective treatment of the disease, only 20% of CAD cases are diagnosed prior to a heart attack. The development of a definitive, noninvasive test for detection of coronary blockages is one of the holy grails of diagnostic cardiology. One promising approach to detecting coronary blockages noninvasively is based on identifying acoustic signatures generated by turbulent blood flow through partially occluded coronary arteries. In fact, no other approach to the detection of CAD promises to be as inexpensive, simple to perform, and risk free as the acoustic-based approach. Although sounds associated with partially blocked arteries are easy to identify in more superficial vessels such as the carotids, sounds from coronary arteries are very faint and surrounded by noise such as the very loud valve sounds. To detect these very weak signals requires sophisticated signal processing techniques. This review describes the work that has been done in this area since the 1980s and discusses future directions that may fulfill the promise of the acoustic approach to detecting coronary artery disease.
Semmlow
John
J
Robert Wood Johnson Medical School, Rutgers University, Piscataway, New Jersey 08854, USA. Semmlow@biomed.rutgers.edu
Rahalkar
Ketaki
K
D000162:Acoustics; D000818:Animals; D001783:Blood Flow Velocity; D003198:Computer Simulation; D003324:Coronary Artery Disease / Q000175:diagnosis* / Q000503:physiopathology*; D006326:Heart Auscultation / Q000379:methods*; D006801:Humans; D008955:Models, Cardiovascular; D012815:Signal Processing, Computer-Assisted; D013018:Sound Spectrography / Q000379:methods*