Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Aneurysm II: Clinical Manifestations and Diagnostic Studies

Aneurysm II: Clinical Manifestations and Diagnostic Studies

Thoracic, aortic arch and abdominal aneurysms are significant vascular conditions that can present with various clinical manifestations and lead to serious complications. Understanding these manifestations and the appropriate diagnostic studies is essential for effective management and treatment.Thoracic Aortic AneurysmsThoracic aortic aneurysms often remain asymptomatic until they reach a size that impinges on adjacent structures. They typically cause deep, diffuse chest pain that radiates to...

You might also read

Related Articles

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

Sort by

Same author

Surface EEG to Identify Cognitive Motor Dissociation After Acute Brain Injury.

Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society·2026

Same author

Investigating the Dynamic Relationship Between Anxiety and Spatial Memory Using Autonomous Ecological Momentary Assessment.

Research square·2026

Same author

Subclinical Hypercoagulable Viscoelastic Characteristics Associated with Post-Acute Sequelae of SARS-CoV2 Physical Symptoms in COVID-19 ALI/ARDS Survivors.

Journal of intensive care medicine·2026

Same author

Supratentorial Brain Lesions in Unconscious Patients Map to a Common Human Brain Circuit.

Annals of neurology·2026

Same author

Accuracy of Electronic Medical Records for Quantifying Rates of Sedative and Analgesic Infusions for Acute Disorders of Consciousness in Big Data Research.

Neurocritical care·2026

Same author

Risk Factors and Outcomes Associated with Atrial Fibrillation Detected after Intracerebral Hemorrhage.

Neurocritical care·2026

Same journal

Low-Dose Buprenorphine Initiations During Opioid and Sedative Weaning in Mechanically Ventilated Neurocritical Care Patients: A Retrospective Pilot Cohort Study.

Neurocritical care·2026

Same journal

A Sinus Arrhythmia that Reversed Cerebral Blood Flow in Pediatric Trauma.

Neurocritical care·2026

Same journal

Neurocritical Care Fellowship Training in the USA: Challenges and Potential Solutions.

Neurocritical care·2026

Same journal

CT Quantification of Intraventricular Hemorrhage Volume in Poor-Grade Aneurysmal Subarachnoid Hemorrhage: Impact on Mortality and Long-Term Disability.

Neurocritical care·2026

Same journal

Respiratory Consequences of Systemic Milrinone Therapy in Cerebral Vasospasm.

Neurocritical care·2026

Same journal

Determinants of Brain Tissue Normoxia in Patients with Large Spontaneous Intracerebral Hemorrhage.

Neurocritical care·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Oct 26, 2025

A Bedside, Single Burr Hole Approach to Multimodality Monitoring in Severe Brain Injury

A Bedside, Single Burr Hole Approach to Multimodality Monitoring in Severe Brain Injury

Published on: March 26, 2019

Dynamic Intracranial Pressure Waveform Morphology Predicts Ventriculitis.

Murad Megjhani^1,2, Kalijah Terilli^1,2, Lakshman Kalasapudi³

¹Department of Neurology, Columbia University Vagelos College of Physicians and Surgeons, 177 Fort Washington Ave, 8 Milstein - 300 Center, New York, NY, USA.

Neurocritical Care

|July 31, 2021

Summary

This summary is machine-generated.

Intracranial pressure waveform analysis can predict ventriculitis, an infection of the brain lining. This method offers a non-invasive way to detect the condition early.

Keywords:

Clustering External ventricular drainage ICP waveform Machine learning Neurocritical care Ventriculitis

More Related Videos

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

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

Published on: October 17, 2017

Use of a Wireless Video-EEG System to Monitor Epileptiform Discharges Following Lateral Fluid-Percussion Induced Traumatic Brain Injury

Use of a Wireless Video-EEG System to Monitor Epileptiform Discharges Following Lateral Fluid-Percussion Induced Traumatic Brain Injury

Published on: June 21, 2019

Related Experiment Videos

Last Updated: Oct 26, 2025

A Bedside, Single Burr Hole Approach to Multimodality Monitoring in Severe Brain Injury

A Bedside, Single Burr Hole Approach to Multimodality Monitoring in Severe Brain Injury

Published on: March 26, 2019

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

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

Published on: October 17, 2017

Use of a Wireless Video-EEG System to Monitor Epileptiform Discharges Following Lateral Fluid-Percussion Induced Traumatic Brain Injury

Use of a Wireless Video-EEG System to Monitor Epileptiform Discharges Following Lateral Fluid-Percussion Induced Traumatic Brain Injury

Published on: June 21, 2019

Area of Science:

Neuroscience
Medical Technology
Infectious Disease

Background:

Intracranial pressure (ICP) waveform morphology reflects intracranial compliance.
Ventriculitis, an infection of the brain's ventricles, can decrease intracranial compliance.
Predicting ventriculitis onset using ICP dynamics is crucial for timely intervention.

Purpose of the Study:

To investigate the potential of intracranial pressure (ICP) waveform morphologic analysis in predicting the onset of ventriculitis.
To develop and validate a computational pipeline for automated ICP waveform analysis.
To assess the performance of a machine learning classifier in distinguishing ventriculitis from control cases.

Main Methods:

A pipeline was developed to isolate ICP waveform segments and extract dominant pulses.
Clinician-supervised active learning identified metaclusters of waveform morphologies (triphasic, single-peak, artifactual).
A L2-regularized logistic regression classifier integrated metacluster distributions, temperature, and lab values to predict ventriculitis.

Main Results:

Analysis included 58 patients (27 with ventriculitis, 31 controls) with 1590 hours of segmented ICP data.
Significant differences in metacluster distributions were observed between ventriculitis and control groups (p < 0.001).
The classifier achieved a median area under the receiver operating characteristic curve of 0.70, with a favorable true to false alert ratio of 1.5:1.

Conclusions:

Intracranial pressure waveform morphology analysis demonstrates potential for classifying ventriculitis.
This non-invasive method may enable early detection of ventriculitis without cerebrospinal fluid sampling.
Further validation is warranted to integrate this technique into clinical practice for improved patient outcomes.