DocumentCode :
3442224
Title :
Morphology analysis of physiological signals using hidden Markov models
Author :
Novák, D. ; Lhotská, L. ; Al-Ani, T. ; Hamam, Y. ; Cuesta-Frau, D. ; Mico, P. ; Aboy, M.
Author_Institution :
Dept. of Cybernetics, Czech Tech. Univ., Prague, Czech Republic
Volume :
3
fYear :
2004
fDate :
23-26 Aug. 2004
Firstpage :
754
Abstract :
We describe a clustering algorithm based on continuous hidden Markov models (HMM) to automatically classify both electrocardiogram (ECG) and intracranial pressure (ICP) beats based on their morphology. The algorithm detects, classifies and labels each beat based on morphology. In order to avoid the numerical problems with classical expectation-maximization (EM) algorithm we apply a novel method of simulated annealing (SIM) for HMM optimization. We show that better results are achieved using simulated annealing approach.
Keywords :
electrocardiography; hidden Markov models; medical signal detection; pattern classification; pattern clustering; signal classification; simulated annealing; ECG; HMM optimization; clustering algorithm; electrocardiogram; expectation maximization algorithm; hidden Markov models; intracranial pressure beats; morphology analysis; physiological signal analysis; simulated annealing; Clustering algorithms; Cranial pressure; Electrocardiography; Hidden Markov models; Laboratories; Morphology; Sequences; Signal analysis; Signal processing algorithms; Simulated annealing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on
ISSN :
1051-4651
Print_ISBN :
0-7695-2128-2
Type :
conf
DOI :
10.1109/ICPR.2004.1334638
Filename :
1334638
Link To Document :
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