Abstract
This paper introduces a new temporal version of Principal Component Analysis by using a Hidden Markov Model in order to obtain optimized representations of observed data through time. The novelty of the proposed method consists mainly in the way in which a static dimensionality reduction technique has been combined with a classic mixture model in time, to enhance the capabilities of dimensionality reduction and classification of myocardial ischemia data. Experimental results show improvements in classification accuracies even with highly reduced representations.
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Alvarez López, M.A., Henao, R., Orozco, A. (2007). Myocardial Ischemia Detection using Hidden Markov Principal Component Analysis. In: Müller-Karger, C., Wong, S., La Cruz, A. (eds) IV Latin American Congress on Biomedical Engineering 2007, Bioengineering Solutions for Latin America Health. IFMBE Proceedings, vol 18. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74471-9_24
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DOI: https://doi.org/10.1007/978-3-540-74471-9_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74470-2
Online ISBN: 978-3-540-74471-9
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