Detection of left ventricular motion abnormality via information measures and Bayesian filtering

We present an original information theoretic measure of heart motion based on the Shannon's differential entropy (SDE), which allows heart wall motion abnormality detection. Based on functional images, which are subject to noise and segmentation inaccuracies, heart wall motion analysis is acknowledged as a difficult problem, and as such, incorporation of prior knowledge is crucial for improving accuracy. Given incomplete, noisy data and a dynamic model, the Kalman filter, a well-known recursive Bayesian filter, is devised in this study to the estimation of the left ventricular (LV) cavity points. However, due to similarity between the statistical information of normal and abnormal heart motions, detecting and classifying abnormality is a challenging problem, which we investigate with a global measure based on the SDE. We further derive two other possible information theoretic abnormality detection criteria, one is based on R

• Type: Journal
• Year: 2010
• Where: TITB
• Authors: Kumaradevan Punithakumar, Ismail Ben Ayed, Ian G. Ross, Ali Islam, Jaron Chong, Shuo Li
• Keywords: Abnormality Detection, Education, Heart Wall Motion, TITB 2010, Wall Motion