Adaptação dos modelos de Markov para um sistema de segmentação e classificação de sinais de eletrocardiograma

Abstract

In this work three incremental adaptation methods for the hidden Markov models (HMM) are studied and implemented, which are based on the Expectation-Maximization (EM), Segmental k-Means and Maximum a Posteriori (MAP) algorithms. These methods, already used in the speech recognition field, are applied here in the electrocardiogram (ECG) segmentation problem. For that, it was used an ECG analysis system able to segment and classify cardiac diseases, like premature ventricular contraction (PVC) and ischemia. The use of these methods allow us to adjust the models to the signal fluctuations commonly met during ambulatory recording. The methods can also be implemented for other kinds of biomedical signals, like electroencephalogram (EEG).