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Related Concept Videos

State Space Representation01:27

State Space Representation

The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

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...
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length, the...
State Space to Transfer Function01:21

State Space to Transfer Function

The conversion of state-space representation to a transfer function is a fundamental process in system analysis. It provides a method for transitioning from a time-domain description to a frequency-domain representation, which is crucial for simplifying the analysis and design of control systems.
The transformation process begins with the state-space representation, characterized by the state equation and the output equation. These equations are typically represented as:
Kinematic Equations - II01:17

Kinematic Equations - II

The second kinematic equation expresses the final position of an object in terms of its initial position, the distance traveled with the initial constant velocity, and the distance traveled due to a change in velocity. Similar to the first kinematic equation, this equation is also only valid when the acceleration is constant throughout the motion of an object.
Suppose a car merges into freeway traffic on a 200 m long ramp. If its initial velocity is 10 m/s and it accelerates at 2 m/s2, then the...
Kinematic Equations - I01:26

Kinematic Equations - I

When an object moves with constant acceleration, the velocity of the object changes at a constant rate throughout the motion. The kinematic equations of motions are derived for such cases where the acceleration of the object is constant. The first kinematic equation gives an insight into the relationship between velocity, acceleration, and time. We can see, for example:

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Related Experiment Video

Updated: Jul 16, 2026

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

Physiome model based state-space framework for cardiac kinematics recovery.

Ken C L Wong1, Heye Zhang, Huafeng Liu

  • 1Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong. eewclken@ust.hk

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|March 16, 2007
PubMed
Summary

This study introduces a cardiac physiome model for more accurate heart motion analysis. This approach improves cardiac information recovery from noisy patient data, enhancing diagnostic capabilities.

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Area of Science:

  • Biomedical Engineering
  • Computational Biology
  • Medical Imaging

Background:

  • Accurate cardiac motion recovery is crucial for diagnosing heart conditions.
  • Existing biomechanical models have limitations in accounting for active myocyte forces.
  • Noise in patient-specific measurements complicates reliable cardiac information extraction.

Purpose of the Study:

  • To develop a novel approach for enhanced heart motion analysis using a cardiac physiome model.
  • To overcome the limitations of passive biomechanical constraints in cardiac modeling.
  • To improve the recovery of cardiac information from noisy patient data.

Main Methods:

  • A cardiac physiome model integrating electric wave propagation, electromechanical coupling, and biomechanical models was employed.
  • A multiframe state-space framework was utilized to manage model and measurement uncertainties.
  • The coupled model was optimized against patient-specific measurements for kinematic estimation.

Main Results:

  • The proposed cardiac physiome model demonstrated improved accuracy in heart motion recovery compared to traditional biomechanical models.
  • The framework effectively handled uncertainties in both the model and patient data.
  • Experiments on synthetic and MR image data validated the model's capabilities.

Conclusions:

  • The cardiac physiome model offers a more comprehensive approach to heart motion analysis by incorporating active physiological processes.
  • This method enhances the reliability of cardiac information recovery from noisy measurements.
  • The findings suggest potential for improved clinical diagnostics and personalized cardiac treatment planning.