Optimizing the detection of nonstationary signals by using recurrence analysis

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dc.contributor.authorPrado, Thiago de Lima
dc.contributor.authorLima, Gustavo Zampier dos Santos
dc.contributor.authorLobão-Soares, Bruno
dc.contributor.authorNascimento, George Carlos do
dc.contributor.authorCorso, Gilberto
dc.contributor.authorAraújo, John Fontenele
dc.contributor.authorKurths, Jürgen
dc.contributor.authorLopes, Sérgio Roberto
dc.date.accessioned2020-12-04T19:42:22Z
dc.date.available2020-12-04T19:42:22Z
dc.date.issued2018-08-24
dc.description.resumoRecurrence analysis and its quantifiers are strongly dependent on the evaluation of the vicinity threshold parameter, i.e., the threshold to regard two points close enough in phase space to be considered as just one. We develop a new way to optimize the evaluation of the vicinity threshold in order to assure a higher level of sensitivity to recurrence quantifiers to allow the detection of even small changes in the dynamics. It is used to promote recurrence analysis as a tool to detect nonstationary behavior of time signals or space profiles. We show that the ability to detect small changes provides information about the present status of the physical process responsible to generate the signal and offers mechanisms to predict future states. Here, a higher sensitive recurrence analysis is proposed as a precursor, a tool to predict near future states of a particular system, based on just (experimentally) obtained signals of some available variables of the system. Comparisons with traditional methods of recurrence analysis show that the optimization method developed here is more sensitive to small variations occurring in a signal. The method is applied to numerically generated time series as well as experimental data from physiologypt_BR
dc.identifier.citationPRADO, Thiago de Lima; LIMA, Gustavo Zampier dos Santos; LOBÃO-SOARES, Bruno; NASCIMENTO, George C. do; CORSO, Gilberto; FONTENELE-ARAUJO, John; KURTHS, Jürgen; LOPES, Sergio Roberto. Optimizing the detection of nonstationary signals by using recurrence analysis. Chaos: An Interdisciplinary Journal of Nonlinear Science, [S.L.], v. 28, n. 8, p. 085703-085703, ago. 2018. Disponível em: https://aip.scitation.org/doi/10.1063/1.5022154. Acesso em: 20 nov. 2020. http://dx.doi.org/10.1063/1.5022154.pt_BR
dc.identifier.doi10.1063/1.5022154
dc.identifier.issn1054-1500
dc.identifier.issn1089-7682
dc.identifier.urihttps://repositorio.ufrn.br/handle/123456789/30824
dc.languageenpt_BR
dc.publisherAmerican Institute of Physicspt_BR
dc.subjectCoupled oscillatorspt_BR
dc.subjectMeasuring instrumentspt_BR
dc.subjectMusculoskeletal systempt_BR
dc.subjectData visualizationpt_BR
dc.subjectLorenz systempt_BR
dc.subjectFourier analysispt_BR
dc.subjectData acquisitionpt_BR
dc.subjectMammalspt_BR
dc.subjectDynamical systemspt_BR
dc.subjectNeuroanatomypt_BR
dc.titleOptimizing the detection of nonstationary signals by using recurrence analysispt_BR
dc.typearticlept_BR

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