000169400 001__ 169400 000169400 005__ 20260225153354.0 000169400 0247_ $$2doi$$a10.22489/cinc.2017.280-141 000169400 0248_ $$2sideral$$a148337 000169400 037__ $$aART-2018-148337 000169400 041__ $$aeng 000169400 100__ $$0(orcid)0000-0002-0690-3193$$aPelaez Coca, María Dolores 000169400 245__ $$aSignificant Physiological Features to Identify High Performance States 000169400 260__ $$c2018 000169400 5060_ $$aAccess copy available to the general public$$fUnrestricted 000169400 5203_ $$aOur long-term goal is the development of an automaticidentifier of attentional states. In order to accomplish it, we should firstly be able to identify different states. So, the first aim of this work is to identify the most appropriate features, to detect a subject high performance state. For that, a database of electrocardiographic (ECG) signal of two unequivocally defined states (rest and attention task) is needed. To achieve this goal, ECG signal is recorded, in those cognitive states from up to 54 subjects as a sample of the population. Temporal and frequency parameters of heart rate variability have been computed from ECG signal. Additionally, the respiratory rate has been estimated from the same signal. In total, ten features are obtained for each subject. They provide information about the physiological response of the subject and about his autonomic nervous system. Results show that eight from these features present significant differences between subject’s baseline and subject’s attentional state; and selecting only four of them, state classification accuracy reaches a mean of 75.91%. 000169400 536__ $$9info:eu-repo/grantAgreement/ES/DGA/T04-FSE$$9info:eu-repo/grantAgreement/ES/MINECO/TEC2014-54143-P$$9info:eu-repo/grantAgreement/ES/MINECO/TIN2014-5356-R$$9info:eu-repo/grantAgreement/ES/UZ/CUD2013-11$$9info:eu-repo/grantAgreement/ES/UZ/CUD2016-TEC-03$$9info:eu-repo/grantAgreement/ES/UZ/CUD2016-18 000169400 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttps://creativecommons.org/licenses/by/4.0/deed.es 000169400 592__ $$a0.202$$b2018 000169400 593__ $$aComputer Science (miscellaneous)$$c2018 000169400 593__ $$aCardiology and Cardiovascular Medicine$$c2018 000169400 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion 000169400 700__ $$0(orcid)0000-0003-0630-4366$$aLozano Albalate, María Teresa 000169400 700__ $$0(orcid)0000-0002-8953-0600$$aAiger, Montserrat 000169400 700__ $$0(orcid)0000-0003-2596-7237$$aHernando, Alberto 000169400 700__ $$0(orcid)0000-0001-7285-0715$$aGil, Eduardo$$uUniversidad de Zaragoza 000169400 7102_ $$15007$$2520$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Ingen.Sistemas y Automát. 000169400 773__ $$g44 (2018), [4 pp.]$$pComput. cardiol.$$tComputing in Cardiology$$x2325-8861 000169400 8564_ $$s229522$$uhttps://zaguan.unizar.es/record/169400/files/texto_completo.pdf$$yVersión publicada 000169400 8564_ $$s2748300$$uhttps://zaguan.unizar.es/record/169400/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada 000169400 909CO $$ooai:zaguan.unizar.es:169400$$particulos$$pdriver 000169400 951__ $$a2026-02-25-14:58:36 000169400 980__ $$aARTICLE