Data-Driven Identification of Stochastic Model Parameters and State Variables: Application to the Study of Cardiac Beat-to-Beat Variability

Sampedro-Puente, David Adolfo ; Fernández-Bes, Jesús ; Virág, László ; Varró, András ; Pueyo, Esther (Universidad de Zaragoza)

Resumen: Enhanced 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.

Idioma: Inglés

DOI: 10.1109/JBHI.2019.2921881

Año: 2020

Publicado en: IEEE journal of biomedical and health informatics 24, 3 (2020), 693-704
ISSN: 2168-2194

Factor impacto JCR: 5.772 (2020)

Categ. JCR: COMPUTER SCIENCE, INFORMATION SYSTEMS rank: 28 / 162 = 0.173 (2020) - Q1 - T1
Categ. JCR: MATHEMATICAL & COMPUTATIONAL BIOLOGY rank: 5 / 58 = 0.086 (2020) - Q1 - T1
Categ. JCR: MEDICAL INFORMATICS rank: 4 / 30 = 0.133 (2020) - Q1 - T1
Categ. JCR: COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS rank: 17 / 112 = 0.152 (2020) - Q1 - T1

Factor impacto SCIMAGO: 1.292 - 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/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
Financiación: info:eu-repo/grantAgreement/ES/ISCIII/CIBER-BBN-MULTITOOLS2HEART
Financiación: info:eu-repo/grantAgreement/ES/MINECO/DPI2016-75458-R

Tipo y forma: Article (PostPrint)

Área (Departamento): Área Teoría Señal y Comunicac. (Dpto. Ingeniería Electrón.Com.)