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Related Concept Videos

Wave Parameters01:10

Wave Parameters

The simplest mechanical waves are associated with simple harmonic motion and repeat themselves for several cycles. These simple harmonic waves can be modeled using a combination of sine and cosine functions. Consider a simplified surface water wave that moves across the water's surface. Unlike complex ocean waves, in surface water waves, water moves vertically, oscillating up and down, whereas the disturbance of the wave moves horizontally through the medium. If a seagull is floating on the...
Inductively Coupled Plasma–Mass Spectrometry (ICP–MS): Overview01:19

Inductively Coupled Plasma–Mass Spectrometry (ICP–MS): Overview

In inductively coupled plasma–mass spectrometry (ICP–MS), an inductively coupled plasma (ICP) torch is used as an atomizer and ionizer. Solid samples are dissolved and volatilized before being introduced into the high-temperature argon plasma, while solution samples are nebulized and passed through the high-temperature argon plasma. Plasma dissociates the analytes and ionizes their component atoms to form a mixture of positive ions and molecular species. The positive ions are then passed on to...
Sound as Pressure Waves01:17

Sound as Pressure Waves

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...
Intensity and Pressure of Sound Waves01:05

Intensity and Pressure of Sound Waves

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 and...
Inductively Coupled Plasma-Mass Spectrometry (ICP-MS): Interferences01:20

Inductively Coupled Plasma-Mass Spectrometry (ICP-MS): Interferences

Inductively coupled plasma–mass spectrometry (ICP–MS) is a highly selective and sensitive technique for accurate elemental analysis. Though the analysis of ICP–MS mass spectra is comparatively straightforward, it is affected by spectroscopic and non-spectroscopic interferences. Spectroscopic interferences arise when the plasma contains ionic species with an m/z value the same as the analyte ion. Spectroscopic interference can be categorized as isobaric, polyatomic ions, and refractory oxide ion...
Convolution Properties II01:17

Convolution Properties II

The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...

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Related Experiment Video

Updated: Jun 21, 2026

Measurements of Waves in a Wind-wave Tank Under Steady and Time-varying Wind Forcing
08:54

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Published on: February 13, 2018

ICP-WAVES: Intracranial Pressure Waveform Analysis and Visualization for Enhanced Signal Processing.

Murad Megjhani, Yanwei Li, Giselle Grassi

    IEEE Transactions on Bio-Medical Engineering
    |March 3, 2026
    PubMed
    Summary
    This summary is machine-generated.

    A transformer-based deep learning model analyzes intracranial pressure (ICP) waveforms to assess brain compliance. It effectively distinguishes compliant from non-compliant waveforms, improving clinical decision-making for patients with neurological conditions.

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    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Artificial Intelligence in Medicine

    Background:

    • Intracranial pressure (ICP) waveform morphology is crucial for understanding brain compliance and cerebrospinal fluid dynamics.
    • Current monitoring methods lack the ability to fully analyze complex temporal patterns in ICP waveforms or offer real-time interactive analysis.
    • This limits their utility in improving clinical decision-making.

    Purpose of the Study:

    • To develop and validate a transformer-based foundation model for analyzing invasive ICP waveforms.
    • To enable real-time interactive analysis of ICP data to enhance clinical decision-making.
    • To accurately classify different ICP waveform morphologies reflective of brain compliance.

    Main Methods:

    • A transformer-based foundation model was trained on physiological data to capture temporal dynamics and generate embeddings.
    • The model was fine-tuned on ICP waveform data from patients with intracerebral hemorrhage (ICH) and validated on external ventricular drainage (EVD) and synthetic ICP datasets.
    • Embeddings were used to train a support vector machine (SVM) classifier for morphology classification, with performance evaluated using AUC and confusion matrices. A GUI was developed for ad-hoc analysis.

    Main Results:

    • The foundation model achieved high AUCs for classifying ICP waveform morphologies: 0.90 for 3-peak compliant, 0.93 for single-peak non-compliant, and 0.78 for multi-peak non-compliant waveforms.
    • On simulated ICP data, the model achieved AUCs of 1.00 for all classes.
    • Confusion matrix analysis showed 77.5% accuracy for 1-peak compliant waveforms and 100% accuracy for other categories.

    Conclusions:

    • A deep learning foundation model effectively analyzes invasive ICP waveforms to extract clinically relevant information on cerebral compliance.
    • The model demonstrates strong performance in differentiating compliant from non-compliant waveforms.
    • This approach holds promise for advancing ICP monitoring and clinical decision support.