86116 20221020131653.0 doi 10.1109/JBHI.2018.2882142 sideral 106753 ART-2019-106753 eng Peláez Coca, María Dolores (orcid)0000-0002-0690-3193 Photoplethysmographic Waveform Versus Heart Rate Variability to Identify Low Stress States. Attention Test 2019 Access copy available to the general public Unrestricted Our long-term goal is the development of an automatic identifier of attentional states. In order to accomplish it, we should firstly be able to identify different states based on physiological signals. 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) and photopletysmographic (PPG) signals is recorded in two unequivocally defined states (rest and attention task) from up to 50 subjects as a sample of the population. Time and frequency parameters of heart/pulse rate variability have been computed from the ECG/PPG signals respectively. Additionally, the respiratory rate has been estimated from both signals and also six morphological parameters from PPG. In total, twenty six features are obtained for each subject. They provide information about the autonomic nervous system and the physiological response of the subject to an attention demand task. Results show an increase of sympathetic activation when the subjects perform the attention test. The amplitude and width of the PPG pulse were more sensitive that the classical sympathetic markers (normalised power in LF and LF/HF ratio) for identifying this attentional state. State classification accuracy reaches a mean of 89 $\pm$ 2%, a maximum of 93% and a minimum of 85%, in the hundred classifications made by only selecting four parameters extracted from the PPG signal (pulse amplitude, pulse width, pulse downward slope and mean pulse rate). These results suggest that attentional states could be identified by PPG. info:eu-repo/grantAgreement/ES/DGA-FEDER/T39-17R-BSICoS 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/UZ2017-TEC-04 info:eu-repo/semantics/openAccess All rights reserved http://www.europeana.eu/rights/rr-f/ 5.223 2019 COMPUTER SCIENCE, INFORMATION SYSTEMS 15 / 156 = 0.096 2019 Q1 T1 MATHEMATICAL & COMPUTATIONAL BIOLOGY 5 / 59 = 0.085 2019 Q1 T1 MEDICAL INFORMATICS 1 / 27 = 0.037 2019 Q1 T1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS 12 / 109 = 0.11 2019 Q1 T1 1.306 2019 Biotechnology 2019 Q1 Health Information Management 2019 Q1 Electrical and Electronic Engineering 2019 Q1 Computer Science Applications 2019 Q1 info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion Lozano Albalate, María Teresa (orcid)0000-0003-0630-4366 Hernando Sanz, Alberto (orcid)0000-0003-2596-7237 Aiger Vallés, Montserrat Universidad de Zaragoza (orcid)0000-0002-8953-0600 Gil, Eduardo Universidad de Zaragoza (orcid)0000-0001-7285-0715 4009 740 Universidad de Zaragoza Dpto. Psicología y Sociología Área Psicología Social 5007 520 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Ingen.Sistemas y Automát. 23, 5 (2019), 1940-1951 IEEE j. biomed. health inform. IEEE journal of biomedical and health informatics 2168-2194 208357 http://zaguan.unizar.es/record/86116/files/texto_completo.pdf Postprint 139352 http://zaguan.unizar.es/record/86116/files/texto_completo.jpg?subformat=icon icon Postprint oai:zaguan.unizar.es:86116 articulos driver 2022-10-20-13:05:38 ARTICLE