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

• URL: http://arxiv.org/abs/2211.09478v1
• Date: Thu, 17 Nov 2022 11:51:35 GMT
• Title: Parameterization of state duration in Hidden semi-Markov Models: an application in electrocardiography
• Authors: Adri\'an P\'erez Herrero and Paulo F\'elix Lamas and Jes\'us Mar\'ia Rodr\'iguez Presedo
• Abstract summary: We introduce a parametric model for time series pattern recognition and provide a maximum-likelihood estimation of its parameters. An application on classification reveals the main strengths and weaknesses of each alternative.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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