Boosting confidence in detecting time-dependent ultradian rhythms using wavelet analysis
- Autores
- Flesia, Ana Georgina
- Año de publicación
- 2021
- Idioma
- inglés
- Tipo de recurso
- documento de conferencia
- Estado
- versión publicada
- Descripción
- Recently, biologists have shown fractal and oscillatory characteristics in animal behaviortime series. Aspects so different can be explained by a model with added components thatinclude deterministic cycles (ultradian and circadian rhythms), polynomial tendencies, and anunderlying nonlinear process with stationary increments. Such components can be extractedfrom the data using wavelet analysis by selecting the transformation appropriately. In this talk, we will discuss a five-step method that describes the data without making any parametric assumptions about trends in the frequency or amplitude of the components signals and is resilient to noise.1. Visual inspection by Continuous wavelet transform based on real Gaussian motherwavelet in the Cartesian time scale plane2. Visual inspection by Continuous wavelet transform based on complex Morlet motherwavelet in the Polar time scale plane.3. Modal frequency detection by Synchrosqueezed wavelet transform, a linear timescale analysis followed by a synchrosqueezing technique.4. Modal frequency corroboration by Empirical wavelet transform, a wavelet analysis in theFourier domain followed by frequency segmentation to extract the modal components.5- Quantification of coherence and phase difference between different series.
Fil: Flesia, Ana Georgina. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Centro de Investigación y Estudios de Matemática. Universidad Nacional de Córdoba. Centro de Investigación y Estudios de Matemática; Argentina
SMB 2021 Annual Meeting
Riverside
Estados Unidos
Society for Mathematical Biology
University of California - Materia
-
WAVELET
ULTRADIAN RHYTHMS
ANIMAL BEHAVIOR - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
- Repositorio
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/193598
Ver los metadatos del registro completo
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Boosting confidence in detecting time-dependent ultradian rhythms using wavelet analysisFlesia, Ana GeorginaWAVELETULTRADIAN RHYTHMSANIMAL BEHAVIORhttps://purl.org/becyt/ford/1.1https://purl.org/becyt/ford/1Recently, biologists have shown fractal and oscillatory characteristics in animal behaviortime series. Aspects so different can be explained by a model with added components thatinclude deterministic cycles (ultradian and circadian rhythms), polynomial tendencies, and anunderlying nonlinear process with stationary increments. Such components can be extractedfrom the data using wavelet analysis by selecting the transformation appropriately. In this talk, we will discuss a five-step method that describes the data without making any parametric assumptions about trends in the frequency or amplitude of the components signals and is resilient to noise.1. Visual inspection by Continuous wavelet transform based on real Gaussian motherwavelet in the Cartesian time scale plane2. Visual inspection by Continuous wavelet transform based on complex Morlet motherwavelet in the Polar time scale plane.3. Modal frequency detection by Synchrosqueezed wavelet transform, a linear timescale analysis followed by a synchrosqueezing technique.4. Modal frequency corroboration by Empirical wavelet transform, a wavelet analysis in theFourier domain followed by frequency segmentation to extract the modal components.5- Quantification of coherence and phase difference between different series.Fil: Flesia, Ana Georgina. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Centro de Investigación y Estudios de Matemática. Universidad Nacional de Córdoba. Centro de Investigación y Estudios de Matemática; ArgentinaSMB 2021 Annual MeetingRiversideEstados UnidosSociety for Mathematical BiologyUniversity of CaliforniaSociety for Mathematical Biology2021info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjectEncuentroJournalhttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/193598Boosting confidence in detecting time-dependent ultradian rhythms using wavelet analysis; SMB 2021 Annual Meeting; Riverside; Estados Unidos; 2021; 1-2CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://2021.smb.org/NEUR/NEUR-CT09.html#author2Internacionalinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T10:03:40Zoai:ri.conicet.gov.ar:11336/193598instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 10:03:40.615CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse |
dc.title.none.fl_str_mv |
Boosting confidence in detecting time-dependent ultradian rhythms using wavelet analysis |
title |
Boosting confidence in detecting time-dependent ultradian rhythms using wavelet analysis |
spellingShingle |
Boosting confidence in detecting time-dependent ultradian rhythms using wavelet analysis Flesia, Ana Georgina WAVELET ULTRADIAN RHYTHMS ANIMAL BEHAVIOR |
title_short |
Boosting confidence in detecting time-dependent ultradian rhythms using wavelet analysis |
title_full |
Boosting confidence in detecting time-dependent ultradian rhythms using wavelet analysis |
title_fullStr |
Boosting confidence in detecting time-dependent ultradian rhythms using wavelet analysis |
title_full_unstemmed |
Boosting confidence in detecting time-dependent ultradian rhythms using wavelet analysis |
title_sort |
Boosting confidence in detecting time-dependent ultradian rhythms using wavelet analysis |
dc.creator.none.fl_str_mv |
Flesia, Ana Georgina |
author |
Flesia, Ana Georgina |
author_facet |
Flesia, Ana Georgina |
author_role |
author |
dc.subject.none.fl_str_mv |
WAVELET ULTRADIAN RHYTHMS ANIMAL BEHAVIOR |
topic |
WAVELET ULTRADIAN RHYTHMS ANIMAL BEHAVIOR |
purl_subject.fl_str_mv |
https://purl.org/becyt/ford/1.1 https://purl.org/becyt/ford/1 |
dc.description.none.fl_txt_mv |
Recently, biologists have shown fractal and oscillatory characteristics in animal behaviortime series. Aspects so different can be explained by a model with added components thatinclude deterministic cycles (ultradian and circadian rhythms), polynomial tendencies, and anunderlying nonlinear process with stationary increments. Such components can be extractedfrom the data using wavelet analysis by selecting the transformation appropriately. In this talk, we will discuss a five-step method that describes the data without making any parametric assumptions about trends in the frequency or amplitude of the components signals and is resilient to noise.1. Visual inspection by Continuous wavelet transform based on real Gaussian motherwavelet in the Cartesian time scale plane2. Visual inspection by Continuous wavelet transform based on complex Morlet motherwavelet in the Polar time scale plane.3. Modal frequency detection by Synchrosqueezed wavelet transform, a linear timescale analysis followed by a synchrosqueezing technique.4. Modal frequency corroboration by Empirical wavelet transform, a wavelet analysis in theFourier domain followed by frequency segmentation to extract the modal components.5- Quantification of coherence and phase difference between different series. Fil: Flesia, Ana Georgina. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Centro de Investigación y Estudios de Matemática. Universidad Nacional de Córdoba. Centro de Investigación y Estudios de Matemática; Argentina SMB 2021 Annual Meeting Riverside Estados Unidos Society for Mathematical Biology University of California |
description |
Recently, biologists have shown fractal and oscillatory characteristics in animal behaviortime series. Aspects so different can be explained by a model with added components thatinclude deterministic cycles (ultradian and circadian rhythms), polynomial tendencies, and anunderlying nonlinear process with stationary increments. Such components can be extractedfrom the data using wavelet analysis by selecting the transformation appropriately. In this talk, we will discuss a five-step method that describes the data without making any parametric assumptions about trends in the frequency or amplitude of the components signals and is resilient to noise.1. Visual inspection by Continuous wavelet transform based on real Gaussian motherwavelet in the Cartesian time scale plane2. Visual inspection by Continuous wavelet transform based on complex Morlet motherwavelet in the Polar time scale plane.3. Modal frequency detection by Synchrosqueezed wavelet transform, a linear timescale analysis followed by a synchrosqueezing technique.4. Modal frequency corroboration by Empirical wavelet transform, a wavelet analysis in theFourier domain followed by frequency segmentation to extract the modal components.5- Quantification of coherence and phase difference between different series. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/conferenceObject Encuentro Journal http://purl.org/coar/resource_type/c_5794 info:ar-repo/semantics/documentoDeConferencia |
status_str |
publishedVersion |
format |
conferenceObject |
dc.identifier.none.fl_str_mv |
http://hdl.handle.net/11336/193598 Boosting confidence in detecting time-dependent ultradian rhythms using wavelet analysis; SMB 2021 Annual Meeting; Riverside; Estados Unidos; 2021; 1-2 CONICET Digital CONICET |
url |
http://hdl.handle.net/11336/193598 |
identifier_str_mv |
Boosting confidence in detecting time-dependent ultradian rhythms using wavelet analysis; SMB 2021 Annual Meeting; Riverside; Estados Unidos; 2021; 1-2 CONICET Digital CONICET |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/url/http://2021.smb.org/NEUR/NEUR-CT09.html#author2 |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
eu_rights_str_mv |
openAccess |
rights_invalid_str_mv |
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
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application/pdf application/pdf |
dc.coverage.none.fl_str_mv |
Internacional |
dc.publisher.none.fl_str_mv |
Society for Mathematical Biology |
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Society for Mathematical Biology |
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reponame:CONICET Digital (CONICET) instname:Consejo Nacional de Investigaciones Científicas y Técnicas |
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Consejo Nacional de Investigaciones Científicas y Técnicas |
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CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas |
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dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar |
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