title: Modelling ECG signals with hidden Markov models.
abstract: In this paper, we have studied the use of continuous probability density 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-deterministic probabilistic finite state machine, which can be constructed inductively. It has been widely used in speech recognition and DNA modelling. We have found that hidden Markov models are very suitable for ECG recognition and analysis problems and that they are able to model accurately segmented ECG signals.
authors.affiliation: Department of Computer Science, University of Turku, Finland. akoski@cs.utu.fi
authors.forename: A
authors.initials: A
authors.lastname: Koski
mesh_terms: D001185:Artificial Intelligence; D004562:Electrocardiography; D006801:Humans; D008390:Markov Chains; D008954:Models, Biological; D010363:Pattern Recognition, Automated; D011338:Probability Theory; D012815:Signal Processing, Computer-Assisted