title: Incremental HMM training applied to ECG signal analysis.
abstract: 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.
authors.affiliation: Coordenadoria de Eletrotécnica, CEFETES, Av. Vitória, 1729, Jucutuquara, Vitória, ES, CEP 29040-780, Brazil. rodrigo@ele.ufes.br
authors.forename: Rodrigo V
authors.initials: RV
authors.lastname: Andreão
authors.forename: Sandra M T
authors.initials: SM
authors.lastname: Muller
authors.forename: Jérôme
authors.initials: J
authors.lastname: Boudy
authors.forename: Bernadette
authors.initials: B
authors.lastname: Dorizzi
authors.forename: Teodiano F
authors.initials: TF
authors.lastname: Bastos-Filho
authors.forename: Mário
authors.initials: M
authors.lastname: Sarcinelli-Filho
mesh_terms: 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