Incremental HMM training applied to ECG signal analysis.
This work discusses the implementation of incremental hidden Markov model (HMM) training methods for electrocardiogram (ECG) analysis. The HMMs are used to model the ECG signal as a sequence of connected elementary waveforms. Moreover, an adaptation process is implemented to adapt the HMMs to the ECG signal of a particular individual. The adaptation training strategy is based on incremental versions of the expectation-maximization, segmental k-means and Bayesian approaches. Performance of the training methods was assessed through experiments considering the QT and ST-T databases. The results obtained show that the incremental training improves beat segmentation and ischemia detection performance with the advantage of low computational effort.
Andreão
Rodrigo V
RV
Coordenadoria de Eletrotécnica, CEFETES, Av. Vitória, 1729, Jucutuquara, Vitória, ES, CEP 29040-780, Brazil. rodrigo@ele.ufes.br
Muller
Sandra M T
SM
Boudy
Jérôme
J
Dorizzi
Bernadette
B
Bastos-Filho
Teodiano F
TF
Sarcinelli-Filho
Mário
M
D000465:Algorithms; D016208:Databases, Factual; D004562:Electrocardiography / Q000706:statistics & numerical data*; D006801:Humans; D007511:Ischemia / Q000175:diagnosis; D016013:Likelihood Functions; D008390:Markov Chains; D012815:Signal Processing, Computer-Assisted