Por favor, use este identificador para citar o enlazar este ítem: https://doi.org/10.1016/j.cmpb.2023.107933

Título: Uncovering personal circadian responses to light through particle swarm optimization
Fecha de publicación: 19-nov-2023
Editorial: Elsevier
Cita bibliográfica: Computer Methods and Programs in Biomedicine, 2024, Vol. 243 : 107933
ISSN: Print: 0169-2607
Electronic: 1872-7565
Palabras clave: Particle swarm optimization
Kronauer's oscillator model
Circadian personalization
Circadian response to light
Parameter optimization of ordinary differential equations
Heuristic algorithms
Resumen: 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 inter-individual 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.
Autor/es principal/es: Vicente Martínez, Jesús
Bonmatí Carrión, María Ángeles
Madrid, Juan Antonio
Rol de Lama, María de los Ángeles
Versión del editor: https://www.sciencedirect.com/science/article/pii/S0169260723005990
URI: http://hdl.handle.net/10201/154386
DOI: https://doi.org/10.1016/j.cmpb.2023.107933
Tipo de documento: info:eu-repo/semantics/article
Número páginas / Extensión: 14
Derechos: info:eu-repo/semantics/openAccess
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Descripción: © 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
Aparece en las colecciones:Artículos

Ficheros en este ítem:
Fichero Descripción TamañoFormato 
1-s2.0-S0169260723005990-main.pdf6,41 MBAdobe PDFVista previa
Visualizar/Abrir


Este ítem está sujeto a una licencia Creative Commons Licencia Creative Commons Creative Commons