An Application Of Data Mining In Detection Of Myocardial Ischemia Utilizing Pre- And Post-Stress Echo Images

Pramod K. Singh, Simeon J. Simoff, David Dagan Feng

Abstract: Automatic identification of endocardial and epicardial boundaries of LV has been a focus of research attention in the development of computational methods and computer support for cardiologists in identifying clinical heart disease and their diagnosis. Among heart imaging techniques, echocardiography offers significant advantages because of its low cost, portability, minimal discomfort, the absence of ionizing radiation, and its possible application for patient monitoring through real time processing. However, images generated from echocardiogram data are of poor quality. This paper presents the initial work in the development of a data mining approach for computer-assisted detection of myocardial ischemia, which includes Left Ventricle (LV) wall boundary identification, segmentation and further comparative analysis of wall segments in pre- and post stress echocardiograms.