169400 20260225153354.0 doi 10.22489/cinc.2017.280-141 sideral 148337 ART-2018-148337 eng Pelaez Coca, María Dolores (orcid)0000-0002-0690-3193 Significant Physiological Features to Identify High Performance States 2018 Our 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%. Access copy available to the general public Unrestricted info:eu-repo/grantAgreement/ES/DGA/T04-FSE info:eu-repo/grantAgreement/ES/MINECO/TEC2014-54143-P info:eu-repo/grantAgreement/ES/MINECO/TIN2014-5356-R info:eu-repo/grantAgreement/ES/UZ/CUD2013-11 info:eu-repo/grantAgreement/ES/UZ/CUD2016-TEC-03 info:eu-repo/grantAgreement/ES/UZ/CUD2016-18 info:eu-repo/semantics/openAccess by https://creativecommons.org/licenses/by/4.0/deed.es 0.202 2018 Computer Science (miscellaneous) 2018 Cardiology and Cardiovascular Medicine 2018 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Lozano Albalate, María Teresa (orcid)0000-0003-0630-4366 Aiger, Montserrat (orcid)0000-0002-8953-0600 Hernando, Alberto (orcid)0000-0003-2596-7237 Gil, Eduardo Universidad de Zaragoza (orcid)0000-0001-7285-0715 5007 520 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Ingen.Sistemas y Automát. 44 (2018), [4 pp.] Comput. cardiol. Computing in Cardiology 2325-8861 229522 http://zaguan.unizar.es/record/169400/files/texto_completo.pdf Versión publicada 2748300 http://zaguan.unizar.es/record/169400/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:169400 articulos driver 2026-02-25-14:58:36 ARTICLE