Jove
Visualize
Contact Us

Related Concept Videos

Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

161
Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
161
Neural Control of Respiration01:18

Neural Control of Respiration

3.7K
The neural regulation of respiration is a meticulously coordinated process primarily controlled by the respiratory centers located within the brainstem. These centers, composed of specialized neurons, transmit nerve impulses that control the contraction and relaxation of our respiratory muscles.
Respiratory Centers in the Brainstem
Two primary areas comprise the respiratory center: the medullary respiratory center in the medulla oblongata and the pontine respiratory group in the pons. The...
3.7K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

159
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
159
Equipments Used To Measure Blood Pressure01:30

Equipments Used To Measure Blood Pressure

2.8K
Direct Method
This invasive approach involves cannulating a peripheral artery. During each cardiac contraction, pressure generates mechanical motion within the catheter, transmitted through rigid, fluid-filled tubing to a transducer. This transducer converts mechanical motion into electrical signals displayed as waveforms on a monitor. An automatic flushing system prevents blood backflow. Due to the potential risk of unexpected arterial blood loss, this method is primarily used in intensive...
2.8K
Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

10.3K
The electrical signals recorded on an electrocardiogram (ECG) occur before the mechanical processes of contraction and relaxation during the cardiac cycle.
A cardiac action potential originates in the SA node and spreads throughout the atria and the AV node in approximately 0.03 seconds. This results in the P wave in an ECG and triggers atrial contraction. The action potential is then briefly slowed at the AV node, allowing the atria to contract and fill the ventricles with blood before...
10.3K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Controllability of voltage- and calcium-driven cardiac alternans in a map model.

Chaos (Woodbury, N.Y.)·2021
Same author

Dynamical mechanism of atrial fibrillation: A topological approach.

Chaos (Woodbury, N.Y.)·2017
Same journal

Multiscale dynamics of special memristive ion channels in a neural circuit.

Chaos (Woodbury, N.Y.)·2026
Same journal

Symmetry-protected delay spectroscopy in oscillator networks.

Chaos (Woodbury, N.Y.)·2026
Same journal

Mesoscale community organization governs epidemic onset and spread in metapopulations.

Chaos (Woodbury, N.Y.)·2026
Same journal

Topological dependence of viral mutation spread in complex host-interaction networks.

Chaos (Woodbury, N.Y.)·2026
Same journal

Multifractal signatures of Hamiltonian chaos in Hyperion's rotational dynamics.

Chaos (Woodbury, N.Y.)·2026
Same journal

Exploring mechanisms for reversal of flow in tunicate hearts.

Chaos (Woodbury, N.Y.)·2026
See all related articles
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Nov 11, 2025

Software for Analysis of Heart Rate and Blood Pressure Time-series Data from the Valsalva Maneuver
14:28

Software for Analysis of Heart Rate and Blood Pressure Time-series Data from the Valsalva Maneuver

Published on: June 27, 2025

632

Robust data assimilation with noise: Applications to cardiac dynamics.

Christopher D Marcotte1, Flavio H Fenton2, Matthew J Hoffman3

  • 1School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, USA.

Chaos (Woodbury, N.Y.)
|March 23, 2021
PubMed
Summary
This summary is machine-generated.

Stochastic modeling improves cardiac excitation pattern reconstruction by accounting for uncertainties. Combining stochastic processes enhances data assimilation accuracy and ensemble spread, outperforming classical methods.

More Related Videos

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations
12:09

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

Published on: January 8, 2013

13.9K
Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring
06:32

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring

Published on: July 14, 2023

1.6K

Related Experiment Videos

Last Updated: Nov 11, 2025

Software for Analysis of Heart Rate and Blood Pressure Time-series Data from the Valsalva Maneuver
14:28

Software for Analysis of Heart Rate and Blood Pressure Time-series Data from the Valsalva Maneuver

Published on: June 27, 2025

632
Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations
12:09

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

Published on: January 8, 2013

13.9K
Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring
06:32

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring

Published on: July 14, 2023

1.6K

Area of Science:

  • Computational Biology
  • Biophysics
  • Applied Mathematics

Background:

  • Cardiac excitation pattern reconstruction faces challenges from model and observation errors, and hidden variables.
  • Accurate state reconstructions require addressing uncertainties in model specification and dynamics.

Purpose of the Study:

  • To introduce and evaluate stochastic modeling methods for improved state reconstruction in cardiac dynamics.
  • To enhance ensemble-based data assimilation by incorporating model uncertainties.

Main Methods:

  • Developed two classes of stochastic modeling methods: stochastic differential equation formalism and parameter space perturbation.
  • Applied these methods to one- and three-dimensional cardiac systems with fast-slow time-scale separation.
  • Investigated the role of time-scale separation in formulating stochastic assimilation schemes.

Main Results:

  • Stochastic forcing term formulation, based on slow or fast time scales, is analogous to ensemble inflation techniques.
  • Careful parameter selection is crucial to prevent over-driving the data assimilation process.
  • Combining stochastic processes improved assimilation error and ensemble spread compared to classical methods.

Conclusions:

  • Stochastic modeling offers a robust approach to managing uncertainties in cardiac dynamics reconstruction.
  • The proposed methods enhance the accuracy and reliability of data assimilation in complex biological systems.
  • Further research can optimize parameter choices for specific cardiac models and assimilation tasks.