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<dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:invenio="http://invenio-software.org/elements/1.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><dc:identifier>doi:10.1109/CIC.2015.7411083</dc:identifier><dc:language>eng</dc:language><dc:creator>Hernando, Alberto</dc:creator><dc:creator>Lázaro, Jesús</dc:creator><dc:creator>Arza, Adriana</dc:creator><dc:creator>Garzón, Jorge Mario</dc:creator><dc:creator>Gil, Edurado</dc:creator><dc:creator>Laguna, Pablo.</dc:creator><dc:creator>Aguiló, Jordi</dc:creator><dc:creator>Bailón, Raquel</dc:creator><dc:title>Changes in respiration during emotional stress</dc:title><dc:identifier>ART-2016-94652</dc:identifier><dc:description>In this work, we analyze changes in respiration during emotional stress induced by a modification of the Trier Social Stress Test. The following stages in the test were analyzed: the pre-relaxing stage, the story telling stage, the anticipation of stress and the video exposition stage. Respiration signal is recorded during the whole test using a thoracic band. Power spectral density (PSD) of respiration is computed in running windows using a modification of Welch periodogram in which sufficiently peaked spectra are averaged. Then, respiratory frequency (FR) is estimated from the peaked-conditioned averaged spectra. Results show that respiratory rate is significantly (p &lt; 0.05 according to the Friedman test) higher, while a measure of spectral peakness and the percentage of PSD used to compute (FR) is lower during stress stages than during relax. These results suggest that the respiration-related parameters have potential discrimination power for stress level assessment.</dc:description><dc:date>2016</dc:date><dc:source>http://zaguan.unizar.es/record/63102</dc:source><dc:doi>10.1109/CIC.2015.7411083</dc:doi><dc:identifier>http://zaguan.unizar.es/record/63102</dc:identifier><dc:identifier>oai:zaguan.unizar.es:63102</dc:identifier><dc:relation>info:eu-repo/grantAgreement/ES/FIS/PI12-00514</dc:relation><dc:relation>info:eu-repo/grantAgreement/ES/MINECO/TIN2014-5356-R</dc:relation><dc:relation>info:eu-repo/grantAgreement/ES/UZ/PIFUZ2011-TRCA-003</dc:relation><dc:relation>info:eu-repo/grantAgreement/ES/UZ/UZ2014-TEC-01</dc:relation><dc:identifier.citation>Computing in Cardiology 42 (2016), 1005-1008</dc:identifier.citation><dc:rights>by</dc:rights><dc:rights>http://creativecommons.org/licenses/by/3.0/es/</dc:rights><dc:rights>info:eu-repo/semantics/openAccess</dc:rights></dc:dc>

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