000075921 001__ 75921
000075921 005__ 20200117221646.0
000075921 0247_ $$2doi$$a10.1016/j.medengphy.2017.11.004
000075921 0248_ $$2sideral$$a104213
000075921 037__ $$aART-2018-104213
000075921 041__ $$aeng
000075921 100__ $$aHolmer, M.
000075921 245__ $$aDetection of ventricular premature beats based on the pressure signals of a hemodialysis machine
000075921 260__ $$c2018
000075921 5060_ $$aAccess copy available to the general public$$fUnrestricted
000075921 5203_ $$aMonitoring of ventricular premature beats (VPBs), being abundant in hemodialysis patients, can provide information on cardiovascular instability and electrolyte imbalance. In this paper, we describe a method for VPB detection which explores the signals acquired from the arterial and the venous pressure sensors, located in the extracorporeal blood circuit of a hemodialysis machine. The pressure signals are mainly composed of a pump component and a cardiac component. The cardiac component, severely overshadowed by the pump component, is estimated from the pressure signals using an earlier described iterative method. A set of simple features is extracted, and linear discriminant analysis is performed to classify beats as either normal or ventricular premature. Performance is evaluated on signals from nine hemodialysis treatments, using leave-one-out crossvalidation. The simultaneously recorded and annotated photoplethysmographic signal serves as the reference signal, with a total of 149, 686 normal beats and 3574 VPBs. The results show that VPBs can be reliably detected, quantified by a Youden''s J statistic of 0.9, for average cardiac pulse pressures exceeding 1 mmHg; for lower pressures, the J statistic drops to 0.55. It is concluded that the cardiac pressure signal is suitable for VPB detection, provided that the average cardiac pulse pressure exceeds 1 mmHg.
000075921 536__ $$9info:eu-repo/grantAgreement/ES/DGA/T96$$9info:eu-repo/grantAgreement/ES/MINECO/DPI2016-75458-R$$9info:eu-repo/grantAgreement/ES/MINECO/TIN2014-5356-R
000075921 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
000075921 590__ $$a1.785$$b2018
000075921 591__ $$aENGINEERING, BIOMEDICAL$$b55 / 80 = 0.688$$c2018$$dQ3$$eT3
000075921 592__ $$a0.66$$b2018
000075921 593__ $$aBiophysics$$c2018$$dQ2
000075921 593__ $$aBiomedical Engineering$$c2018$$dQ2
000075921 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000075921 700__ $$0(orcid)0000-0002-7503-3339$$aMartínez, J.P.$$uUniversidad de Zaragoza
000075921 700__ $$0(orcid)0000-0001-7285-0715$$aGil, E.$$uUniversidad de Zaragoza
000075921 700__ $$aSandberg, F.
000075921 700__ $$aOlde, B.
000075921 700__ $$aSörnmo, L.
000075921 7102_ $$15007$$2520$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Ingen.Sistemas y Automát.
000075921 7102_ $$15008$$2800$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Teoría Señal y Comunicac.
000075921 773__ $$g51 (2018), 49-55$$pMed. eng. phys.$$tMEDICAL ENGINEERING & PHYSICS$$x1350-4533
000075921 8564_ $$s887474$$uhttps://zaguan.unizar.es/record/75921/files/texto_completo.pdf$$yPostprint
000075921 8564_ $$s57274$$uhttps://zaguan.unizar.es/record/75921/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000075921 909CO $$ooai:zaguan.unizar.es:75921$$particulos$$pdriver
000075921 951__ $$a2020-01-17-22:06:44
000075921 980__ $$aARTICLE