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dc.contributor.authorVicente Martínez, Jesús-
dc.contributor.authorBonmatí Carrión, María Ángeles-
dc.contributor.authorMadrid, Juan Antonio-
dc.contributor.authorRol de Lama, María de los Ángeles-
dc.date.accessioned2025-05-12T08:31:05Z-
dc.date.available2025-05-12T08:31:05Z-
dc.date.issued2023-11-19-
dc.identifier.citationComputer Methods and Programs in Biomedicine, 2024, Vol. 243 : 107933es
dc.identifier.issnPrint: 0169-2607-
dc.identifier.issnElectronic: 1872-7565-
dc.identifier.urihttp://hdl.handle.net/10201/154386-
dc.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.2023.107933es
dc.description.abstractBackground 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.es
dc.formatapplication/pdfes
dc.format.extent14es
dc.languageenges
dc.publisherElsevieres
dc.relationThis paper has been funded by the Ministry of Economy and Competitiveness, the Instituto de Salud Carlos III through a CIBERFES grant (CB16/10/00239) and Diabfrail LatAm (European Union Horizon 2020 research and innovation program No. 825546) awarded to MAR (all co-financed by FEDER). Grant RTI2018-093528-B-I00, funded by MCIN/AEI/ 10.13039/501100011033 and by “ERDF A way of making Europe”, by the “European Union” and by the “European Union NextGenerationEU/PRTR”. JVM predoctoral contract has been funded by grant '21610/FPI/21'. Fundación Séneca. Región de Murcia (Spain).es
dc.rightsinfo:eu-repo/semantics/openAccesses
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectParticle swarm optimizationes
dc.subjectKronauer's oscillator modeles
dc.subjectCircadian personalizationes
dc.subjectCircadian response to lightes
dc.subjectParameter optimization of ordinary differential equationses
dc.subjectHeuristic algorithmses
dc.titleUncovering personal circadian responses to light through particle swarm optimizationes
dc.typeinfo:eu-repo/semantics/articlees
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S0169260723005990es
dc.identifier.doihttps://doi.org/10.1016/j.cmpb.2023.107933-
dc.contributor.departmentDepartamento de Fisiología-
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