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

Increased Intracranial Pressure l: Introduction01:14

Increased Intracranial Pressure l: Introduction

Intracranial hypertension is a sustained elevation of intracranial pressure (ICP) above 22 mm Hg. In supine adults, normal ICP is ~7–15 mm Hg.The rigid, nonexpandable cranium contains three components—brain tissue, blood, and cerebrospinal fluid (CSF)—that total ~1,700 mL in a typical adult: 1,400 mL brain (~80%), 150 mL blood (~10%), and 150 mL CSF (~10%). According to the Monro–Kellie doctrine, total intracranial volume is effectively fixed. When one component expands, CSF and venous blood...
Increased Intracranial Pressure ll: Pathophysiology01:29

Increased Intracranial Pressure ll: Pathophysiology

Increased intracranial pressure (ICP) refers to a potentially life-threatening rise in pressure inside the skull. This usually happens when there is a major change in the volume of brain tissue, blood, or cerebrospinal fluid (CSF) — the three components inside the skull. According to the Monro-Kellie doctrine, if the volume of one component increases, the volumes of the other components must decrease to maintain normal pressure. If this does not happen, ICP rises.The process often begins with...

You might also read

Related Articles

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

Sort by
Same author

A comprehensive inference-time augmentation framework in physiological signals: application to PPG-based AF detection.

Physiological measurement·2026
Same author

DietAI24 as a framework for comprehensive nutrition estimation using multimodal large language models.

Communications medicine·2025
Same author

A machine learning model to predict optimal antibiotic use in hospital medicine patients.

Antimicrobial stewardship & healthcare epidemiology : ASHE·2025
Same author

Physics-informed neural networks for physiological signal processing and modeling: a narrative review.

Physiological measurement·2025
Same author

Leveraging Artificial Intelligence for Digital Symptom Management in Oncology: The Development of CRCWeb.

JMIR cancer·2025
Same author

Automated classification of seizure onset pattern using intracranial electroencephalogram signal of non-human primates.

Physiological measurement·2025

Related Experiment Video

Updated: Jul 17, 2026

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

43.2K

Patient-adaptable intracranial pressure morphology analysis using a probabilistic model-based approach.

Paria Rashidinejad1, Xiao Hu2, Stuart Russell1

  • 1Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, United States of America.

Physiological Measurement
|September 29, 2020
PubMed
Summary

We developed a dynamic Bayesian network for analyzing intracranial pressure (ICP) morphology. This automated framework accurately detects ICP pulsatile components, improving patient care for neurological conditions.

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

7.3K
Translaminar Autonomous System Model for the Modulation of Intraocular and Intracranial Pressure in Human Donor Posterior Segments
08:55

Translaminar Autonomous System Model for the Modulation of Intraocular and Intracranial Pressure in Human Donor Posterior Segments

Published on: April 24, 2020

3.4K

Related Experiment Videos

Last Updated: Jul 17, 2026

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

43.2K
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

7.3K
Translaminar Autonomous System Model for the Modulation of Intraocular and Intracranial Pressure in Human Donor Posterior Segments
08:55

Translaminar Autonomous System Model for the Modulation of Intraocular and Intracranial Pressure in Human Donor Posterior Segments

Published on: April 24, 2020

3.4K

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Intracranial pressure (ICP) signal analysis is complex due to non-linear dynamics, noise, and individual patient variations.
  • Existing ICP analysis frameworks often rely on unrealistic assumptions and lack patient-specific tuning.
  • Accurate ICP morphology analysis is crucial for managing neurological conditions like traumatic brain injuries.

Purpose of the Study:

  • To present a novel framework for analyzing intracranial pressure (ICP) morphology.
  • To develop an automated method for detecting major ICP pulsatile components.
  • To create a patient-specific ICP analysis model that adapts to individual measurements.

Main Methods:

  • A dynamic Bayesian network was proposed to model non-linear and non-Gaussian ICP dynamics.
  • The framework incorporates evidence reversal and an inference algorithm for robust pulsatile component detection.
  • The model is designed for unsupervised learning of patient-specific ICP parameters.

Main Results:

  • The automated approach achieved high detection accuracies for ICP pulsatile components: 96.56%, 92.39%, and 94.04%.
  • The framework demonstrated significant improvements over existing ICP analysis methods.
  • Evaluation was performed on over 700 hours of recordings from 66 neurological patients.

Conclusions:

  • The proposed Bayesian network framework offers an accurate and adaptive solution for ICP morphology analysis.
  • This automated approach enhances patient care by providing reliable ICP monitoring with minimal supervision.
  • The framework's ability to provide uncertainty estimates and reveal ICP dynamics aids in monitoring pathophysiological changes relevant to acute brain injuries.