Unscented Kalman Filter for Unobservable Parameter Estimation in Heart Cell Signals

  • David Adolfo Sampedro-Puente Biomedical Signal Interpretation and Computational Simulation (BSICoS) Instituto de Investigación en Ingeniería de Aragón (I3A) Universidad de Zaragoza,
  • Jesús Fernández-Bes CIBER-BBN: Centro de Investigación Biomédica en Red – Bioingeniería, Biomateriales y Nanomedicina Biomedical Signal Interpretation and Computational Simulation (BSICoS) Instituto de Investigación en Ingeniería de Aragón (I3A) Universidad de Zaragoza
  • Esther Pueyo Biomedical Signal Interpretation and Computational Simulation (BSICoS) Instituto de Investigación en Ingeniería de Aragón (I3A) Universidad de Zaragoza CIBER-BBN: Centro de Investigación Biomédica en Red – Bioingeniería, Biomateriales y Nanomedicina

Resumen

One interesting feature of biological systems is that minor subcellular changes can cause alterations at the whole organ level. In the heart, the random dynamics of cell membrane ion channels contributes to beat-to-beat repolarization variability, which has been related to proarrhythmic risk. Inference of unobservable cellular parameters, such as the number of channels, is key to characterize such random ion channel dynamics. In this work, a methodology based on the use of Unscented Kalman Filters is proposed to infer the number of channel from action potential signals, like those commonly recorded experimentally.