Generative models for periodicity detection in noisy signals
Additional information
Authors
Barnett E.,
Kaiser O.,
Masci J.,
Wit E. J.,
Fulda S.
Type
Journal Article
Year
2024
Language
English
Abstract
We present the Gaussian Mixture Periodicity Detection Algorithm (GMPDA), a novel method for detecting periodicity in the binary time series of event onsets. The GMPDA addresses the periodicity detection problem by inferring parameters of a generative model. We introduce two models, the Clock Model and the Random Walk Model, which describe distinct periodic phenomena and provide a comprehensive generative framework. The GMPDA demonstrates robust performance in test cases involving single and multiple periodicities, as well as varying noise levels. Additionally, we evaluate the GMPDA on real-world data from recorded leg movements during sleep, where it successfully identifies expected periodicities despite high noise levels. The primary contributions of this paper include the development of two new models for generating periodic event behavior and the GMPDA, which exhibits high accuracy in detecting multiple periodicities even in noisy environments.
Keywords
Periodicity, Periodicity detection, Algorithm, Generative models, Periodic leg movements during sleep
Journal
Clocks & sleep
Volume
6
Number ( Month )
3
Pages (or article number)
359-388
Diffusion
License
CC BY
Visibility
Public
Status open access
Gold
