TY - GEN
T1 - Myocardial ischemia detection using Hidden Markov principal component analysis
AU - Alvarez López, Mauricio Alexánder
AU - Henao, R.
AU - Orozco, A.
N1 - Generated from Scopus record by KAUST IRTS on 2023-02-15
PY - 2008/1/1
Y1 - 2008/1/1
N2 - 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.
AB - 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.
UR - http://link.springer.com/10.1007/978-3-540-74471-9_24
UR - http://www.scopus.com/inward/record.url?scp=75149132126&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-74471-9_24
DO - 10.1007/978-3-540-74471-9_24
M3 - Conference contribution
SN - 9783540744702
SP - 99
EP - 103
BT - IFMBE Proceedings
PB - Springer [email protected]
ER -