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

arXiv: https://arxiv.org/abs/2211.09478 · HTML: https://mlsanity.com/paper/2211.09478/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 15 references that are in the map.
- First posted on arXiv 17 Nov 2022.

## Builds on (most cited first, 1 of 1)

- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)

## Abstract (arXiv)

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.