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%).
Mansor
Sarina
S
Biomedical Image Analysis (BioMedIA) Laboratory, Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, United Kingdom. sarina@robots.ox.ac.uk
Hughes
Nicholas P
NP
Noble
J Alison
JA
D000465:Algorithms; D001185:Artificial Intelligence; D004452:Echocardiography / Q000379:methods*; D005080:Exercise Test; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012146:Rest; D012680:Sensitivity and Specificity; D018487:Ventricular Dysfunction, Left / Q000000981:diagnostic imaging*