Circadian Phase Prediction From Non-Intrusive and Ambulatory Physiological Data

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dc.contributor.authorSuárez Pinto, Alexis Adrián
dc.contributor.authorNúñez Retamal, Felipe Eduardo
dc.contributor.authorRodríguez Fernandez, María
dc.date.accessioned2022-05-18T14:39:52Z
dc.date.available2022-05-18T14:39:52Z
dc.date.issued2021
dc.description.abstractChronotherapy aims to treat patients according to their endogenous biological rhythms and requires, therefore, knowing their circadian phase. Circadian phase is partially determined by genetics and, under natural conditions, is normally entrained by environmental signals (zeitgebers), predominantly by light. Physiological data such as melatonin concentration and core body temperature (CBT) have been used to estimate circadian phase. However, due to their expensive and intrusive obtention, other physiological variables that also present circadian rhythmicity, such as heart rate variability, skin temperature, activity, and body position, have recently been proposed in several studies to estimate circadian phase. This study aims to predict circadian phase using minimally intrusive ambulatory physiological data modeled with machine learning techniques. Two approaches were considered; first, time-series were used to train artificial neural networks (ANNs) that predict CBT and melatonin dynamics and, second, a novel approach that uses scalar variables to build regression models that predict the time of the minimum CBT and the dim light melatonin onset (DLMO). ANNs require less than 48 hours of minimally intrusive data collection to predict circadian phase with an accuracy of less than one hour. On the other hand, regression models that use only three variables (body mass index, activity, and heart rate) are simpler and show higher accuracy with less than one minute of error, although they require longer times of data collection. This is a promising approach that should be validated in further studies considering a broader population and a wider range of conditions, including circadian misalignment.
dc.fuente.origenIEEE
dc.identifier.doi10.1109/JBHI.2020.3019789
dc.identifier.issn2168-2208
dc.identifier.urihttps://doi.org/10.1109/JBHI.2020.3019789
dc.identifier.urihttps://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9178962
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/64191
dc.information.autorucEscuela de ingeniería ; Suárez Pinto, Alexis Adrián ; S/I ; 213768
dc.information.autorucEscuela de ingeniería ; Núñez Retamal, Felipe Eduardo ; S/I ; 131441
dc.information.autorucEscuela de ingeniería ; Rodríguez Fernandez, María ; S/I ; 1031920
dc.issue.numero5
dc.language.isoen
dc.nota.accesoContenido parcial
dc.pagina.final1571
dc.pagina.inicio1561
dc.revistaIEEE Journal of Biomedical and Health Informatics
dc.rightsacceso restringido
dc.subjectTemperature measurement
dc.subjectPredictive models
dc.subjectPhysiology
dc.subjectTemperature sensors
dc.subjectAutoregressive processes
dc.subjectSchedules
dc.subjectBiomedical measurement
dc.titleCircadian Phase Prediction From Non-Intrusive and Ambulatory Physiological Dataes_ES
dc.typeartículo
dc.volumen25
sipa.codpersvinculados213768
sipa.codpersvinculados131441
sipa.codpersvinculados1031920
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