Jove
Visualize
Contact Us
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 Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Endocrine disorders after aneurysmal subarachnoid hemorrhage: Acute neuroendocrine responses and long-term sequelae. A narrative review.

Advances in clinical and experimental medicine : official organ Wroclaw Medical University·2026
Same author

Metabolic Chaos After Aneurysmal Subarachnoid Haemorrhage: Longitudinal Glucose-Potassium Ratio Dynamics and Clinical Outcomes.

Biomedicines·2026
Same author

Viral pneumonia detection during the COVID-19 pandemic using deep learning and DCGAN-based data augmentation.

Scientific reports·2026
Same author

Intracranial compliance monitoring using pulse shape index in traumatic brain injury: relation to cerebral physiology and clinical outcome.

Critical care (London, England)·2026
Same author

The Prognostic Significance of Low-Triiodothyronine Syndrome in Aneurysmal Subarachnoid Hemorrhage.

Biomedicines·2026
Same author

Impact of positive end-expiratory pressure on autonomic nervous system activity and its interaction with cerebrovascular reactivity - an experimental study.

Journal of clinical monitoring and computing·2026

Related Experiment Video

Updated: Nov 2, 2025

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

43.0K

End-to-End Automatic Morphological Classification of Intracranial Pressure Pulse Waveforms Using Deep Learning.

Cyprian Mataczynski, Agnieszka Kazimierska, Agnieszka Uryga

    IEEE Journal of Biomedical and Health Informatics
    |June 11, 2021
    PubMed
    Summary

    Analyzing intracranial pressure (ICP) pulse waveforms using deep learning can reveal patient health status. This method offers insights beyond mean ICP, aiding in the management of intracranial pathologies.

    More Related Videos

    A Detailed Protocol for Physiological Parameters Acquisition and Analysis in Neurosurgical Critical Patients
    05:01

    A Detailed Protocol for Physiological Parameters Acquisition and Analysis in Neurosurgical Critical Patients

    Published on: October 17, 2017

    7.2K
    Assessing Cerebral Autoregulation via Oscillatory Lower Body Negative Pressure and Projection Pursuit Regression
    11:26

    Assessing Cerebral Autoregulation via Oscillatory Lower Body Negative Pressure and Projection Pursuit Regression

    Published on: December 10, 2014

    12.6K

    Related Experiment Videos

    Last Updated: Nov 2, 2025

    Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
    14:08

    Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

    Published on: April 13, 2013

    43.0K
    A Detailed Protocol for Physiological Parameters Acquisition and Analysis in Neurosurgical Critical Patients
    05:01

    A Detailed Protocol for Physiological Parameters Acquisition and Analysis in Neurosurgical Critical Patients

    Published on: October 17, 2017

    7.2K
    Assessing Cerebral Autoregulation via Oscillatory Lower Body Negative Pressure and Projection Pursuit Regression
    11:26

    Assessing Cerebral Autoregulation via Oscillatory Lower Body Negative Pressure and Projection Pursuit Regression

    Published on: December 10, 2014

    12.6K

    Area of Science:

    • Neurology
    • Biomedical Engineering
    • Data Science

    Background:

    • Mean intracranial pressure (ICP) is a standard metric for managing intracranial pathologies.
    • ICP pulse waveform morphology contains crucial information about the craniospinal space.
    • Current methods may not fully leverage the diagnostic potential of ICP waveform characteristics.

    Purpose of the Study:

    • To develop an end-to-end deep learning approach for classifying ICP waveforms.
    • To assess the clinical applicability of ICP waveform classification.
    • To correlate ICP waveform patterns with patient outcomes in neurocritical care.

    Main Methods:

    • Long-term ICP recordings from 50 neurointensive care unit (NICU) patients were analyzed.
    • ICP waveforms were manually classified into normal, pathological, and artifact classes.
    • Deep learning models, including Residual Neural Networks, were evaluated using 1-D ICP signals.

    Main Results:

    • A Residual Neural Network achieved 93% accuracy in validation and 82% in testing datasets.
    • Patients with unfavorable outcomes showed significantly fewer normal ICP waveforms (9%) compared to those with favorable outcomes (63%).
    • This difference was observed even at ICP levels below 20 mm Hg.

    Conclusions:

    • ICP pulse waveform analysis is feasible in long-term NICU recordings.
    • The proposed deep learning approach can provide additional clinical information beyond mean ICP.
    • ICP waveform morphology may serve as a valuable prognostic indicator in neurocritical care.