000089638 001__ 89638
000089638 005__ 20210902121631.0
000089638 0247_ $$2doi$$a10.1109/JBHI.2019.2921881
000089638 0248_ $$2sideral$$a117239
000089638 037__ $$aART-2020-117239
000089638 041__ $$aeng
000089638 100__ $$0(orcid)0000-0002-1927-1762$$aSampedro-Puente, David Adolfo
000089638 245__ $$aData-Driven Identification of Stochastic Model Parameters and State Variables: Application to the Study of Cardiac Beat-to-Beat Variability
000089638 260__ $$c2020
000089638 5060_ $$aAccess copy available to the general public$$fUnrestricted
000089638 5203_ $$aEnhanced spatiotemporal ventricular repolarization variability has been associated with ventricular arrhythmias and sudden cardiac death, but the involved mechanisms remain elusive. In this paper, a methodology for estimation of parameters and state variables of stochastic human ventricular cell models from input voltage data is proposed for investigation of repolarization variability. Methods: The proposed methodology formulates state-space representations based on developed stochastic cell models and uses the unscented Kalman filter to perform joint parameter and state estimation. Evaluation over synthetic and experimental data is presented. Results: Results on synthetically generated data show the ability of the methodology to: first, filter out measurement noise from action potential (AP) traces; second, identify model parameters and state variables from each of those individual AP traces, thus allowing robust characterization of cell-to-cell variability; and, third, replicate statistical population''s distributions of input AP-based markers, including dynamic markers quantifying beat-to-beat variability. Application onto experimental data demonstrates the ability of the methodology to match input AP traces while concomitantly inferring the characteristics of underlying stochastic cell models. Conclusion: A novel methodology is presented for estimation of parameters and hidden variables of stochastic cardiac computational models, with the advantage of providing a one-to-one match between each individual AP trace and a corresponding set of model characteristics. Significance: The proposed methodology can greatly help in the characterization of temporal (beat-to-beat) and spatial (cell-to-cell) variability in human ventricular repolarization and in ascertaining the corresponding underlying mechanisms, particularly in scenarios with limited available experimental data.
000089638 536__ $$9info:eu-repo/grantAgreement/ES/MINECO/DPI2016-75458-R$$9info:eu-repo/grantAgreement/ES/ISCIII/CIBER-BBN-MULTITOOLS2HEART$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 638284-MODELAGE$$9info:eu-repo/grantAgreement/EC/H2020/638284/EU/Is your heart aging well? A systems biology approach to characterize cardiac aging from the cell to the body surface/MODELAGE$$9info:eu-repo/grantAgreement/ES/DGA-FEDER/T39-17R-BSICoS
000089638 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000089638 590__ $$a5.772$$b2020
000089638 591__ $$aCOMPUTER SCIENCE, INFORMATION SYSTEMS$$b28 / 162 = 0.173$$c2020$$dQ1$$eT1
000089638 591__ $$aMATHEMATICAL & COMPUTATIONAL BIOLOGY$$b5 / 58 = 0.086$$c2020$$dQ1$$eT1
000089638 591__ $$aMEDICAL INFORMATICS$$b4 / 30 = 0.133$$c2020$$dQ1$$eT1
000089638 591__ $$aCOMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS$$b17 / 112 = 0.152$$c2020$$dQ1$$eT1
000089638 592__ $$a1.292$$b2020
000089638 593__ $$aBiotechnology$$c2020$$dQ1
000089638 593__ $$aHealth Information Management$$c2020$$dQ1
000089638 593__ $$aElectrical and Electronic Engineering$$c2020$$dQ1
000089638 593__ $$aComputer Science Applications$$c2020$$dQ1
000089638 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000089638 700__ $$0(orcid)0000-0001-5175-3166$$aFernández-Bes, Jesús
000089638 700__ $$aVirág, László
000089638 700__ $$aVarró, András
000089638 700__ $$0(orcid)0000-0002-1960-407X$$aPueyo, Esther$$uUniversidad de Zaragoza
000089638 7102_ $$15008$$2800$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Teoría Señal y Comunicac.
000089638 773__ $$g24, 3 (2020), 693-704$$pIEEE j. biomed. health inform.$$tIEEE journal of biomedical and health informatics$$x2168-2194
000089638 8564_ $$s2581755$$uhttps://zaguan.unizar.es/record/89638/files/texto_completo.pdf$$yPostprint
000089638 8564_ $$s749128$$uhttps://zaguan.unizar.es/record/89638/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000089638 909CO $$ooai:zaguan.unizar.es:89638$$particulos$$pdriver
000089638 951__ $$a2021-09-02-08:53:13
000089638 980__ $$aARTICLE