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%).
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Mansor, S., Hughes, N.P., Noble, J.A. (2008). Wall Motion Classification of Stress Echocardiography Based on Combined Rest-and-Stress Data. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2008. MICCAI 2008. Lecture Notes in Computer Science, vol 5242. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85990-1_17
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DOI: https://doi.org/10.1007/978-3-540-85990-1_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85989-5
Online ISBN: 978-3-540-85990-1
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