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Related Concept Videos

Sound as Pressure Waves01:17

Sound as Pressure Waves

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Sound waves, which are longitudinal waves, can be modeled as the displacement amplitude varying as a function of the spatial and temporal coordinates. As a column of the medium is displaced, its successive columns are also displaced. As the successive displacements differ relatively, a pressure difference with the surrounding pressure is created. The gauge pressure varies across the medium.
The pressure fluctuation depends on the difference in displacements between the successive points in the...
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Intensity and Pressure of Sound Waves01:05

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The intensity of sound waves can be related to displacement and pressure amplitudes by using their wave expressions and the definition of intensity. The critical step to achieve this is to write the power delivered by the particles on the wave as the product of force and velocity and simplify the force per unit area as the pressure. The velocity of the medium's particles can be derived from the displacement.
Unlike the time average of a sinusoidal term, which is zero since it is positive...
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Measurement of Fluid Pressure01:16

Measurement of Fluid Pressure

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Fluid pressure is commonly measured using devices called manometers, which rely on liquid columns to indicate pressure differences. The height of a liquid column in a manometer reflects the pressure exerted by the fluid, providing a simple yet effective means of measurement. Different types of manometers serve specific purposes based on their configurations and the type of fluids involved.
A basic form of manometer is the piezometer, a vertical tube open at the top and filled with the same...
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Pressure Variation in a Fluid at Rest01:11

Pressure Variation in a Fluid at Rest

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In a fluid at rest, the pressure at any point beneath the fluid surface depends solely on the depth, not on the container's shape or size. This principle, known as hydrostatic pressure, arises because, in stationary fluids, there is no acceleration, meaning the forces within the fluid balance out. Only vertical forces, caused by the weight of the fluid above, contribute to pressure changes with depth.
When measuring pressure at two different levels within the fluid, the difference in...
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Properties of Laplace Transform-II01:16

Properties of Laplace Transform-II

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Time differentiation, convolution, integration, and periodicity are fundamental concepts in analyzing functions and signals over time. Each concept provides a unique perspective on how functions evolve, interact, and repeat, offering essential tools for various scientific and engineering applications.
Time differentiation involves analyzing the rate of change of a function over time. Mathematically, it is the derivative of a function with respect to time. This concept can be likened to tracking...
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Basic Equation for Pressure Field01:13

Basic Equation for Pressure Field

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The basic equation for a pressure field in fluid mechanics captures the balance of forces within any segment of fluid, providing a foundational understanding of how pressure changes within fluids under various forces. Generally, two main types of forces act on any part of a fluid: surface forces and body forces. Surface forces arise from pressure differences across points within the fluid, which result in net forces that can vary depending on the local pressure gradient. Body forces, on the...
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Updated: Sep 13, 2025

The Measurement of Unsteady Surface Pressure Using a Remote Microphone Probe
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Time-frequency machine learning transfer function for central pressure waveforms.

Soha Niroumandi1, Heng Wei1, Faisal Amlani2

  • 1Department of Aerospace and Mechanical Engineering, University of Southern California, 3650 McClintock Ave. Room 400, Los Angeles, CA 90089, USA.

European Heart Journal Open
|July 28, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel hybrid machine learning model to accurately reconstruct central arterial pressure waveforms from peripheral measurements, improving diagnosis for hypertension and heart failure.

Keywords:
Arterial haemodynamicsCardiovascular transfer functionCentral blood pressureTime-frequency machine learning

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Area of Science:

  • Cardiovascular Physiology
  • Biomedical Engineering
  • Machine Learning in Medicine

Background:

  • Pulsatile hemodynamics and pressure waveform analysis are crucial for diagnosing hypertension and heart failure.
  • Generalized transfer functions (GTFs) have limitations in capturing central hemodynamics.
  • Accurate central pressure waveform reconstruction is needed for improved clinical assessment.

Purpose of the Study:

  • To develop and validate a hybrid time-frequency, machine learning-based transfer function for reconstructing central pressure waveforms from peripheral measurements.
  • To overcome the limitations of existing GTFs in capturing central pulsatile hemodynamics.
  • To accurately capture arterial wave-based information for improved cardiovascular assessment.

Main Methods:

  • Utilized Fourier harmonics to approximate pressure waveforms.
  • Employed a feed-forward neural network (FNN) with a custom time-domain cost function.
  • Trained, tested, and validated the hybridized-FNN model on data from the Framingham Heart Study (6698 participants).

Main Results:

  • The hybridized-FNN method achieved significantly lower normalized mean squared error (NMSE) for carotid waveform reconstruction (0.09-0.10) compared to GTF (0.26-0.42).
  • Demonstrated high correlation coefficients for wave times and amplitudes (0.79-0.97) compared to GTF (0.22-0.31).
  • Significantly improved correlations across similarity, morphology, and wave-based parameters.

Conclusions:

  • The hybridized FNN transfer function enables robust calculation of central arterial pressure waveforms from peripheral measurements.
  • This method accurately preserves key physiological sequences within a cardiac cycle.
  • Offers a promising tool for enhanced diagnosis and prognosis of cardiovascular conditions.