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dc.contributor.authorValderas Yamuza, Maria Teresa
dc.contributor.authorBolea, Juan
dc.contributor.authorOrini, M
dc.contributor.authorLaguna Lasaosa, Pablo
dc.contributor.authorVallverdú Ferrer, Montserrat
dc.contributor.authorBailón Luesma, Raquel
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial
dc.date.accessioned2019-06-26T07:16:58Z
dc.date.available2019-06-26T07:16:58Z
dc.date.issued2019-01-28
dc.identifier.citationValderas, M. [et al.]. Human emotion characterization by heart rate variability analysis guided by respiration. "IEEE Journal of Biomedical and Health Informatics", 28 Gener 2019, vol.23, núm.6, p. 2446-2454
dc.identifier.issn2168-2194
dc.identifier.urihttp://hdl.handle.net/2117/135357
dc.description© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
dc.description.abstractDeveloping a tool which identifies emotions based on their effect on cardiac activity may have a potential impact on clinical practice, since it may help in the diagnosing of psycho-neural illnesses. In this study, a method based on the analysis of heart rate variability (HRV) guided by respiration is proposed. The method was based on redefining the high frequency (HF) band, not only to be centered at the respiratory frequency, but also to have a bandwidth dependent on the respiratory spectrum. The method was first tested using simulated HRV signals, yielding the minimum estimation errors as compared to classical and respiratory frequency centered at HF band based definitions, independently of the values of the sympathovagal ratio. Then, the proposed method was applied to discriminate emotions in a database of video-induced elicitation. Five emotional states, relax, joy, fear, sadness and anger, were considered. The maximum correlation between HRV and respiration spectra discriminated joy vs. relax, joy vs. each negative valence emotion, and fear vs. sadness with p-value = 0.05 and AUC = 0.70. Based on these results, human emotion characterization may be improved by adding respiratory information to HRV analysis.
dc.format.extent10 p.
dc.language.isoeng
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Ciències de la salut
dc.subject.lcshBiomedical engineering
dc.subject.lcshHeart beat
dc.subject.lcshRespiration - Measurement
dc.subject.otherHeart rate variability
dc.subject.otherVideos
dc.subject.otherResonant frequency
dc.subject.otherCorrelation
dc.subject.otherBandwidth
dc.subject.otherFrequency estimation
dc.titleHuman emotion characterization by heart rate variability analysis guided by respiration
dc.typeArticle
dc.subject.lemacEnginyeria biomèdica
dc.subject.lemacCor -- Batecs
dc.subject.lemacRespiració -- Mesurament
dc.contributor.groupUniversitat Politècnica de Catalunya. B2SLab - Bioinformatics and Biomedical Signals Laboratory
dc.identifier.doi10.1109/JBHI.2019.2895589
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/8627372
dc.rights.accessOpen Access
local.identifier.drac25227392
dc.description.versionPostprint (author's final draft)
local.citation.authorValderas, M.; Bolea , J.; Orini, M.; Laguna, P.; Vallverdu, M.; Bailón, R.
local.citation.publicationNameIEEE Journal of Biomedical and Health Informatics
local.citation.volume23
local.citation.number6
local.citation.startingPage2446
local.citation.endingPage2454


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