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. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Alvarez López, M. A., Henao, R., & Orozco, A. (2008). Myocardial ischemia detection using Hidden Markov principal component analysis. In IFMBE Proceedings (Vol. 18, pp. 99–103). Springer Verlag. https://doi.org/10.1007/978-3-540-74471-9_24
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