Publication: Uncovering personal circadian responses to light through particle swarm optimization
Authors
Vicente-Martínez, Jesús ; Madrid, Juan Antonio ; Rol, María Ángeles ; Bonmatí Carrión, María de los Ángeles
item.page.secondaryauthor
item.page.director
Publisher
Elsevier
publication.page.editor
publication.page.department
DOI
https://doi.org/10.1016/j.cmpb.2023.107933
item.page.type
info:eu-repo/semantics/article
Description
© 2023 The Authors. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
This document is the Published Manuscript, version of a Published Work that appeared in final form in Computer Methods and Programs in Biomedicine. To access the final edited and published work see https://doi.org/10.1016/j.cmpb.2023.107933
Abstract
Background and objectives: Kronauer’s oscillator model of the human central pacemaker is one of the most
commonly used approaches to study the human circadian response to light. Two sources of error when applying
it to a personal light exposure have been identified: (1) as a populational model, it does not consider interindividual variability, and (2) the initial conditions needed to integrate the model are usually unknown, and
thus subjectively estimated. In this work, we evaluate the ability of particle swarm optimization (PSO) algorithms to simultaneously uncover the optimal initial conditions and individual parameters of a pre-defined
Kronauer’s oscillator model.
Methods: A Canonical PSO, a Dynamic Multi-Swarm PSO and a novel modification of the latter, namely Hierarchical Dynamic Multi-Swarm PSO, are evaluated. Two different target models (under a regular and an irregular
schedule) are defined, and the same realistic light profile is fed to them. Based on their output, a fitness function
is proposed, which is minimized by the algorithms to find the optimum set of parameters and initial conditions of
the model.
Results: We demonstrate that Dynamic Multi-Swarm and Hierarchical Dynamic Multi-Swarm algorithms can
accurately uncover personal circadian parameters under both regular and irregular schedules, but as expected,
optimization is easier under a regular schedule. Circadian parameters play the most important role in the
optimization process and should be prioritized over initial conditions, although assessment of the impact of
misestimating the latter is recommended. The log-log linear relationship between mean absolute error and
computational cost shows that the number of particles to use is at the discretion of the user.
Conclusions: The robustness and low errors achieved by the algorithms support their further testing, validation
and systematic application to empirical data under a regular or irregular schedule. Uncovering personal circadian
parameters can improve the assessment of the circadian status of a person and the applicability of personalized
light therapies, as well as help to discover other factors that may lie behind the interindividual variability in the
circadian response to light.
Citation
Computer Methods and Programs in Biomedicine, Vol. 243, 2024 : 107933
item.page.embargo
Collections
Ir a Estadísticas
Este ítem está sujeto a una licencia Creative Commons. http://creativecommons.org/licenses/by-nc-nd/4.0/