Wall Motion Classification of Stress Echocardiography Based on Combined Rest-and-Stress Data

  • Sarina Mansor
  • Nicholas P. Hughes
  • J. Alison Noble
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5242)


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%).


Hide Markov Model Wall Motion Stress Echocardiography Wall Motion Analysis Cardiac Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Sarina Mansor
    • 1
  • Nicholas P. Hughes
    • 1
  • J. Alison Noble
    • 1
  1. 1.Biomedical Image Analysis (BioMedIA) Laboratory, Institute of Biomedical Engineering, Department of Engineering ScienceUniversity of OxfordUnited Kingdom

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