Myocardial ischemia detection using Hidden Markov principal component analysis

1Citations
Citations of this article
4Readers
Mendeley users who have this article in their library.
Get full text

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. © Springer-Verlag Berlin Heidelberg 2007.

Cite

CITATION STYLE

APA

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

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free