DocumentCode
2156432
Title
Ensemble hidden Markov models for biosignal analysis
Author
Rezek, Iead ; Roberts, Stephen J.
Author_Institution
Dept. of Eng. Sci., Oxford Univ., UK
Volume
1
fYear
2002
fDate
2002
Firstpage
387
Abstract
Variational learning theory allows the estimation of posterior probability distributions of model parameters, rather than the parameters themselves. We demonstrate the use of variational learning methods on hidden Markov models with different observation models and apply the HMM to a range of biomedical signals, such as EEG, periodic breathing and RR-interval series.
Keywords
electrocardiography; electroencephalography; estimation theory; hidden Markov models; inference mechanisms; learning (artificial intelligence); lung; medical signal processing; variational techniques; EEG; HMM; RR-interval series; biomedical signals; biosignal analysis; hidden Markov models; model parameters; periodic breathing; posterior probability distributions; variational learning theory; Bayesian methods; Biomedical engineering; Brain modeling; Electroencephalography; Estimation theory; Hidden Markov models; Learning systems; Maximum likelihood estimation; Probability distribution; Robots;
fLanguage
English
Publisher
ieee
Conference_Titel
Digital Signal Processing, 2002. DSP 2002. 2002 14th International Conference on
Print_ISBN
0-7803-7503-3
Type
conf
DOI
10.1109/ICDSP.2002.1027907
Filename
1027907
Link To Document