Myocardial ischemia detection using Hidden Markov principal component analysis

Mauricio Alexánder Alvarez López, R. Henao, A. Orozco

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

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.
Original languageEnglish (US)
Title of host publicationIFMBE Proceedings
PublisherSpringer Verlagservice@springer.de
Pages99-103
Number of pages5
ISBN (Print)9783540744702
DOIs
StatePublished - Jan 1 2008
Externally publishedYes

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