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%). © 2008 Springer Berlin Heidelberg.
CITATION STYLE
Mansor, S., Hughes, N. P., & Noble, J. A. (2008). Wall motion classification of stress echocardiography based on combined rest-and-stress data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5242 LNCS, pp. 139–146). Springer Verlag. https://doi.org/10.1007/978-3-540-85990-1_17
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