Modelling ECG signals with hidden Markov models

Artificial Intelligence in Medicine
A Koski

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.

References

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Citations

Jan 24, 2012·Computer Methods in Biomechanics and Biomedical Engineering·Xiaoying TangYanjun Zeng
Mar 6, 1999·Computers and Biomedical Research, an International Journal·T AittokallioO Polo
Sep 6, 2012·Journal of Medical Engineering & Technology·S K MukhopadhyayS Mitra
Jul 31, 2008·Journal of Medical Engineering & Technology·J RaskuE Toppila
Oct 29, 2008·Journal of Electrocardiology·Fabio BadiliniPierre Maison-Blanche
Nov 13, 2007·Computer Methods and Programs in Biomedicine·Rémi DuboisGérard Dreyfus
Feb 14, 2008·IEEE Transactions on Bio-medical Engineering·Frida SandbergLeif Sörnmo
Nov 15, 2000·IEEE Transactions on Bio-medical Engineering·Y ZigelA Katz
Nov 29, 2005·Computers in Biology and Medicine·Lina El Khansa, Amine Naït-Ali
Jan 13, 2018·Journal of the Royal Society, Interface·Aurore LyonBlanca Rodriguez
Jun 26, 2020·Physiological Measurement·Nasim KatebiGari D Clifford
Sep 28, 2017·Biomedical Engineering Online·David BeloHugo Gamboa
Jul 25, 2021·Scientific Reports·Sahil Dalal, Virendra P Vishwakarma

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