MODELING ECG SIGNALS WITH HIDDEN MARKOV-MODELS

In this paper, we have studied the use of continuous probability densi ty function hidden Markov models for the ECG signal analysis problem. Our previous work has focused on syntactic pattern recognition methods in signal processing. Hidden Markov model is basically a non-determin istic probabilistic finite state machine, which can be constructed ind uctively. It has been widely used in speech recognition and DNA modell ing. We have found that hidden Markov models are very suitable for ECG recognition and analysis problems and that they are able to model acc urately segmented ECG signals.