Wall motion classification of stress echocardiography based on combined rest-and-stress data

Med Image Comput Comput Assist Interv. 2008;11(Pt 2):139-46. doi: 10.1007/978-3-540-85990-1_17.

Abstract

In this paper, we represent a new framework that performs automated local wall motion analysis based on the combined information derived from a rest and stress sequence (a full stress echocardiography study). Since cardiac data inherits time-varying and sequential properties, we introduce a Hidden Markov Model (HMM) approach to classify stress echocardiography. A wall segment model is developed for a normal and an abnormal heart and experiments are performed on rest, stress and rest-and-stress sequences. In an assessment using n = 44 datasets, combined rest-and-stress analysis shows an improvement in classification (84.17%) over individual rest (73.33%) and stress (68.33%).

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Artificial Intelligence*
  • Echocardiography / methods*
  • Exercise Test*
  • Humans
  • Image Enhancement / methods
  • Image Interpretation, Computer-Assisted / methods*
  • Pattern Recognition, Automated / methods*
  • Reproducibility of Results
  • Rest
  • Sensitivity and Specificity
  • Ventricular Dysfunction, Left / diagnostic imaging*