Generative models for periodicity detection in noisy signals
- Barnett, Ezekiel NNAISENSE, Lugano, Switzerland
- Kaiser, Olga NNAISENSE, Lugano, Switzerland
- Masci, Jonathan NNAISENSE, Lugano, Switzerland
- Wit, Ernst C. ORCID Institute of Computing (CI), Facoltà di scienze informatiche, Università della Svizzera italiana, Svizzera
- Fulda, Stephany Sleep Medicine Unit, Neurocenter of Southern Switzerland, EOC, Lugano, Switzerland
- 2024
Published in:
- Clocks & sleep. - 2024, vol. 6, no. 3, p. 359-388
English
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.
- Collections
- Language
-
- English
- Classification
- Computer science and technology
- License
- CC BY
- Open access status
- gold
- Identifiers
-
- DOI 10.3390/clockssleep6030025
- ARK ark:/12658/srd1332082
- Persistent URL
- https://n2t.net/ark:/12658/srd1332082
Statistics
Document views: 6
File downloads:
- Wit_2024_MDPI_clockssleep_Generative Models for Periodicity.pdf: 8