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arXiv · 2211.09478

Parameterization of state duration in Hidden semi-Markov Models: an application in electrocardiography

Adrián Pérez Herrero·Paulo Félix Lamas·Jesús María Rodríguez Presedo
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Abstract

This work aims at providing a new model for time series classification based on learning from just one example. We assume that time series can be well characterized as a parametric random process, a sort of Hidden semi-Markov Model representing a sequence of regression models with variable duration. We introduce a parametric stochastic model for time series pattern recognition and provide a maximum-likelihood estimation of its parameters. Particularly, we are interested in examining two different representations for state duration: i) a discrete density distribution requiring an estimate for each possible duration; and ii) a parametric family of continuous density functions, here the Gamma distribution, with just two parameters to estimate. An application on heartbeat classification reveals the main strengths and weaknesses of each alternative.

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Adrián Pérez Herrero, Paulo Félix Lamas, Jesús María Rodríguez Presedo. 2022-11-17. Parameterization of state duration in Hidden semi-Markov Models: an application in electrocardiography. https://arxiv.org/abs/2211.09478

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