Abstract
Clinical value of the quantitative assessment of regional myocardial function through segmental strain and strain rate has already been demonstrated. Traditional methods for diagnosing heart diseases are based on values extracted at specific time points during the cardiac cycle, known as ‘techno-markers’, and as a consequence they may fail to provide an appropriate description of the strain (rate) characteristics. This study concerns the statistical analysis of the whole cardiac cycle by the Principal Component Analysis (PCA) method and modeling the major patterns of the strain (rate) curves. Experimental outcomes show that the PCA features can outperform their traditional counterparts in categorizing healthy and infarcted myocardial segments and are able to drive considerable benefit to a classification system by properly modeling the complex structure of the strain rate traces.
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Tabassian, M. et al. (2015). Principal Component Analysis for the Classification of Cardiac Motion Abnormalities Based on Echocardiographic Strain and Strain Rate Imaging. In: van Assen, H., Bovendeerd, P., Delhaas, T. (eds) Functional Imaging and Modeling of the Heart. FIMH 2015. Lecture Notes in Computer Science(), vol 9126. Springer, Cham. https://doi.org/10.1007/978-3-319-20309-6_10
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DOI: https://doi.org/10.1007/978-3-319-20309-6_10
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