000098281 001__ 98281 000098281 005__ 20230914083306.0 000098281 0247_ $$2doi$$a10.23919/CinC49843.2019.9005748 000098281 0248_ $$2sideral$$a121999 000098281 037__ $$aART-2019-121999 000098281 041__ $$aeng 000098281 100__ $$aRinkevicius, M. 000098281 245__ $$aPhotoplethysmogram Signal Morphology-Based Stress Assessment 000098281 260__ $$c2019 000098281 5060_ $$aAccess copy available to the general public$$fUnrestricted 000098281 5203_ $$aStress is a healthy natural response to a perceived or actual threat. However, when stress is persistent, it may decrease work productivity, increase the risk of diseases, and affect the quality of life. Stress is reflected in physiological variables, such as heart rate, blood pressure, and pulse wave velocity among others. A photoplethys-mogram (PPG) contains information related to pulse rate and blood pressure. This study analyses parameters derived from PPG signal morphology for mental stress assessment.A low-complexity algorithm is designed using bandpass filtered higher-order derivatives of the PPG signal for estimation of six morphological parameters: the forward pulse wave amplitude A1, the systole and diastole durations T1 and Td, the time delays of reflected waves T12 and T13 from the renal and iliac sites in the central arteries, and the pulse duration Tp. The parameters were investigated on a set of 18 healthy subjects by using a modified Trier Social Stress Test.The results show that the most sensitive PPG morphology parameters to mental stress are the amplitude of forward wave A1, the duration of diastole Td, the time delay of the reflected wave T13, and the pulse-to-pulse interval Tp. 000098281 536__ $$9info:eu-repo/grantAgreement/ES/MICIU/RTI2018-097723-B-I00$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 745755-WECARMON$$9info:eu-repo/grantAgreement/EC/H2020/745755/EU/Wearable Cardiorespiratory Monitor/WECARMON$$9info:eu-repo/grantAgreement/ES/DGA-FSE/LMP44-18$$9info:eu-repo/grantAgreement/ES/DGA-FEDER/T39-17R-BSICoS$$9info:eu-repo/grantAgreement/ES/CIBER-Ibercaja-CAI/IT 16-18 000098281 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/ 000098281 592__ $$a0.296$$b2019 000098281 593__ $$aComputer Science (miscellaneous)$$c2019 000098281 593__ $$aCardiology and Cardiovascular Medicine$$c2019 000098281 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion 000098281 700__ $$0(orcid)0000-0002-1297-0691$$aKontaxis, S.$$uUniversidad de Zaragoza 000098281 700__ $$0(orcid)0000-0001-7285-0715$$aGil, E.$$uUniversidad de Zaragoza 000098281 700__ $$0(orcid)0000-0003-1272-0550$$aBailon, R.$$uUniversidad de Zaragoza 000098281 700__ $$0(orcid)0000-0001-8742-0072$$aLázaro, J.$$uUniversidad de Zaragoza 000098281 700__ $$0(orcid)0000-0003-3434-9254$$aLaguna, P.$$uUniversidad de Zaragoza 000098281 700__ $$aMarozas, V. 000098281 7102_ $$15007$$2520$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Ingen.Sistemas y Automát. 000098281 7102_ $$15008$$2800$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Teoría Señal y Comunicac. 000098281 773__ $$g46 (2019), [4 pp]$$pComput. cardiol.$$tComputing in Cardiology$$x2325-8861 000098281 8564_ $$s355959$$uhttps://zaguan.unizar.es/record/98281/files/texto_completo.pdf$$yVersión publicada 000098281 8564_ $$s2609158$$uhttps://zaguan.unizar.es/record/98281/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada 000098281 909CO $$ooai:zaguan.unizar.es:98281$$particulos$$pdriver 000098281 951__ $$a2023-09-13-10:56:09 000098281 980__ $$aARTICLE