Photoplethysmographic Waveform Versus Heart Rate Variability to Identify Low Stress States. Attention Test

Peláez Coca, María Dolores ; Lozano Albalate, María Teresa ; Hernando Sanz, Alberto ; Aiger Vallés, Montserrat (Universidad de Zaragoza) ; Gil, Eduardo (Universidad de Zaragoza)

Resumen: 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.

Idioma: Inglés

DOI: 10.1109/JBHI.2018.2882142

Año: 2019

Publicado en: IEEE journal of biomedical and health informatics 23, 5 (2019), 1940-1951
ISSN: 2168-2194

Factor impacto JCR: 5.223 (2019)

Categ. JCR: COMPUTER SCIENCE, INFORMATION SYSTEMS rank: 15 / 156 = 0.096 (2019) - Q1 - T1
Categ. JCR: MATHEMATICAL & COMPUTATIONAL BIOLOGY rank: 5 / 59 = 0.085 (2019) - Q1 - T1
Categ. JCR: MEDICAL INFORMATICS rank: 1 / 27 = 0.037 (2019) - Q1 - T1
Categ. JCR: COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS rank: 12 / 109 = 0.11 (2019) - Q1 - T1

Factor impacto SCIMAGO: 1.306 - Biotechnology (Q1) - Health Information Management (Q1) - Electrical and Electronic Engineering (Q1) - Computer Science Applications (Q1)

Financiación: info:eu-repo/grantAgreement/ES/DGA-FEDER/T39-17R-BSICoS
Financiación: info:eu-repo/grantAgreement/ES/MINECO/TEC2014-54143-P
Financiación: info:eu-repo/grantAgreement/ES/MINECO/TIN2014-5356-R
Financiación: info:eu-repo/grantAgreement/ES/UZ/CUD2013-11
Financiación: info:eu-repo/grantAgreement/ES/UZ/UZ2017-TEC-04

Tipo y forma: Article (PostPrint)

Área (Departamento): Área Psicología Social (Dpto. Psicología y Sociología)
Área (Departamento): Área Ingen.Sistemas y Automát. (Dpto. Informát.Ingenie.Sistms.)