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

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%).