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

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....

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Bibliographic Details
Published inarXiv.org
Main Authors Adrián Pérez Herrero, Paulo Félix Lamas, Rodríguez Presedo, Jesús María
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 17.11.2022
Subjects
Classification
Continuity (mathematics)
Density distribution
Electrocardiography
Markov chains
Maximum likelihood estimation
Parameter estimation
Parameterization
Pattern recognition
Probability distribution functions
Random processes
Regression models
Statistical analysis
Stochastic models
Time series
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Summary: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.
ISSN:2331-8422