NOISE DETECTION AND ELIMINATION FOR IMPROVED ACOUSTIC DETECTION OF CORONARY ARTERY DISEASE

Abstract

There is extensive prior research in coronary artery disease (CAD) detection using sounds recorded from the chest. The acoustic approach to CAD detection is cost-effective, noninvasive and easy to implement; however, it is sensitive to external and internal noise commonly encountered in the clinical setting, which can reduce its reliability. If this noise can be accurately detected and eliminated, the accuracy of the acoustic approach to CAD detection can be significantly improved. In this paper, we develop and evaluate an algorithm to detect noise in acoustic recordings made in the clinical setting. This approach uses signals from a reference microphone placed near the stomach that detects both external and internal noise in the acoustic recording. Applying spectral subtraction and a histogram-based threshold to the reference signal, we are able to identify and eliminate segments that contain noise in the acoustic recording. This algorithm is effective in detecting noise from both external and internal sources.