Characterization of Spatio-Temporal Cardiac Action Potential Variability at Baseline and Under $\beta$-Adrenergic Stimulation by Combined Unscented Kalman Filter and Double Greedy Dimension Reduction

Sampedro-Puente, David Adolfo; Raphel, Fabien; Fernandez-Bes, Jesus; Laguna, Pablo; Lombardi, Damiano; Pueyo, Esther

doi:10.1109/jbhi.2020.2984647

Cited by 3 publications

(5 citation statements)

References 50 publications

(91 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The currents were: I Ks , I Kr , I to , I CaL , I K1 , I N a , I N aCa and I N aK . From the 8000 models initially generated by a Monte-Carlo method, only 2373 models were retained after a calibration step based on physiological limits of electrical properties, as described in [6]. The calibrated models represent virtual cells with distinct ionic properties.…”

Section: Stochastic Population Of Ap Modelsmentioning

confidence: 99%

“…DGDR, a methodology for high-dimensional data analysis [3], was first applied to estimate the AP model parameters by projecting the input data into a low subspace through a sparse linear combination of dictionary entries. The dictionary was composed by different AP markers, like AP duration at different percentages of repolarization, short-term variability (STV) of AP duration, peak voltage, resting membrane potential or wavelet decomposition components, among others, as well as linear combinations of these markers [6].…”

Section: Parameter Estimationmentioning

confidence: 99%

“…Such models could allow investigating the mechanisms underlying electrophysiological properties and their relation to increased arrhythmic risk, narrowing the number of experiments by guiding them according to simulation predictions. In recent studies, parameter estimation approaches based on Double Greedy Dimension Reduction (DGDR) [3] and the Unscented Kalman Filter (UKF) [4] have been shown to accurately identify the parameters and state variables of stochastic human ventricular AP models [5,6].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

In Silico Characterization of Repolarization Duration and Variability in the LQT1 Syndrome Under β-Adrenergic Stimulation

Sampedro-Puente

Raphel

Fernandez-Bes

et al. 2020

2020 Computing in Cardiology Conference (CinC)

Self Cite

View full text Add to dashboard Cite

Enhanced temporal repolarization variability facilitates ventricular arrhythmias in the long QT 1 (LQT1) syndrome, particularly under β-adrenergic stimulation (β-AS). The underlying mechanisms are, however, not fully elucidated. In silico investigation of such mechanisms first requires methods able to reproduce the experimental observations. Here, we describe a method for identification of in silico action potential (AP) models from input voltage traces and we apply it to investigate repolarization variability in LQT1. A combination of Double Greedy Dimension Reduction (DGDR) Unscented Kalman Filter (UKF) was used to estimate the ionic conductances and phosphorylation levels of coupled AP and β-AS models. Over synthetic AP traces from an experimentally-calibrated population of LQT1 cells, combined DGDR-UKF accurately estimated the model parameters, with reduced estimation uncertainty and convergence time. Importantly, combined DGDR-UKF was able to reliably replicate the statistical distributions of AP duration and short-term variability, both at baseline and under β-AS, with relative errors below 4%. Arrhythmogenic AP alternans were reproduced too. In conclusion, our method allows characterization of repolarization duration and variability in LQT1, which is expected to help disentangling the mechanisms underlying adrenergic-induced arrhythmias in this syndrome.

show abstract

Section: Stochastic Population Of Ap Modelsmentioning

confidence: 99%

Section: Parameter Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

In Silico Characterization of Repolarization Duration and Variability in the LQT1 Syndrome Under β-Adrenergic Stimulation

Sampedro-Puente

Raphel

Fernandez-Bes

et al. 2020

2020 Computing in Cardiology Conference (CinC)

Self Cite

View full text Add to dashboard Cite

show abstract

“…The mechanisms underlying such a QT lag reduction are yet unclear. In silico studies have investigated the dynamics of cellular repolarization duration in response to β -adrenergic stimulation [ 34 , 35 ] and have shown that the time lag of the action potential (AP) duration (APD) in response to β -adrenergic stimulation becomes reduced for higher pre-stimulation levels of β -adrenoceptors [ 36 ]. Based on these studies, we hypothesized that the enhanced sympathetic activity when getting closer to the stress peak, and thus increasingly higher β -adrenergic stimulation, could play a role in the observed QT lag reduction at the end of the exercise.…”

Section: Introductionmentioning

confidence: 99%

The role of β-adrenergic stimulation in QT interval adaptation to heart rate during stress test

Pérez

Cebollada²,

Mountris³

et al. 2023

PLoS ONE

Self Cite

View full text Add to dashboard Cite

The adaptation lag of the QT interval after heart rate (HR) has been proposed as an arrhythmic risk marker. Most studies have quantified the QT adaptation lag in response to abrupt, step-like changes in HR induced by atrial pacing, in response to tilt test or during ambulatory recordings. Recent studies have introduced novel methods to quantify the QT adaptation lag to gradual, ramp-like HR changes in stress tests by evaluating the differences between the measured QT series and an estimated, memoryless QT series obtained from the instantaneous HR. These studies have observed the QT adaptation lag to progressively reduce when approaching the stress peak, with the underlying mechanisms being still unclear. This study analyzes the contribution of β-adrenergic stimulation to QT interval rate adaptation in response to gradual, ramp-like HR changes. We first quantify the QT adaptation lag in Coronary Artery Disease (CAD) patients undergoing stress test. To uncover the involved mechanisms, we use biophysically detailed computational models coupling descriptions of human ventricular electrophysiology and β-adrenergic signaling, from which we simulate ventricular action potentials and ECG signals. We characterize the adaptation of the simulated QT interval in response to the HR time series measured from each of the analyzed CAD patients. We show that, when the simulated ventricular tissue is subjected to a time-varying β-adrenergic stimulation pattern, with higher stimulation levels close to the stress peak, the simulated QT interval presents adaptation lags during exercise that are more similar to those measured from the patients than when subjected to constant β-adrenergic stimulation. During stress test recovery, constant and time-varying β-adrenergic stimulation patterns render similar adaptation lags, which are generally shorter than during exercise, in agreement with results from the patients. In conclusion, our findings support the role of time-varying β-adrenergic stimulation in contributing to QT interval adaptation to gradually increasing HR changes as those seen during the exercise phase of a stress test.

show abstract

“…In the last decades, computational modeling and simulation has taken a growing role as a method to deepen the understanding of cardiac function in health and disease [1,2]. Novel in silico models of increasing complexity are continuously being developed to simulate the electrophysiology [3] and mechanics of the heart [4] from the cell [5] to the whole-organ level [6]. At the tissue and organ levels, electrophysiology is simulated by using the well-known bidomain [7] and monodomain [8] models.…”

Section: Introductionmentioning

confidence: 99%

Cardiac Electrophysiology Meshfree Modeling through the Mixed Collocation Method

Mountris,

Pueyo

2023

Applied Sciences

View full text Add to dashboard Cite

We present the meshfree mixed collocation method (MCM) for cardiac electrophysiology simulation. Capitalizing on the meshfree property of MCM, we introduce an immersed grid approach for automated generation of meshfree node grids from medical image data. This approach allows us to avoid the time-consuming mesh generation and processing that mesh-based methods like the finite element method (FEM) require. We employ the MCM to solve the cardiac monodomain model considering electrical propagation in 2D tissue sheets, 3D tissue slabs, and a realistic biventricular anatomy. We demonstrate that the solutions obtained by the MCM are in good agreement with the FEM, particularly when immersed grid is used. These findings confirm the suitability of the MCM for cardiac electrophysiology simulation and make the MCM a promising alternative to the FEM for cardiac electrical investigations.

show abstract

Characterization of Spatio-Temporal Cardiac Action Potential Variability at Baseline and Under $\beta$-Adrenergic Stimulation by Combined Unscented Kalman Filter and Double Greedy Dimension Reduction

Cited by 3 publications

References 50 publications

In Silico Characterization of Repolarization Duration and Variability in the LQT1 Syndrome Under β-Adrenergic Stimulation

In Silico Characterization of Repolarization Duration and Variability in the LQT1 Syndrome Under β-Adrenergic Stimulation

The role of β-adrenergic stimulation in QT interval adaptation to heart rate during stress test

Cardiac Electrophysiology Meshfree Modeling through the Mixed Collocation Method

Contact Info

Product

Resources

About