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

Physical Assessment of the Respiratory Tract II: Inspection01:27

Physical Assessment of the Respiratory Tract II: Inspection

632
Physical assessment of the respiratory tract through inspection is a crucial step in understanding the patient's respiratory health. It provides insights into the functioning of the respiratory system, the musculoskeletal structure, and even the patient's nutritional status. This comprehensive approach involves observing several vital aspects: chest configuration, breathing patterns, respiratory rates, skin color, and use of accessory muscles.
Chest Configuration
The chest configuration...
632
Assessment of Ventilation I: Respiratory Rate01:20

Assessment of Ventilation I: Respiratory Rate

1.6K
Assessment of Ventilation
A Ventilation assessment is critical for monitoring a patient's health status. Respiration, one of the most accessible vital signs, provides insights into the function of numerous body systems and can indicate serious health issues, such as brainstem injuries from head trauma.
Critical Guidelines for Assessing Ventilation:
1.6K
Assessment of Ventilation II: Respiratory Depth and Rhythm01:29

Assessment of Ventilation II: Respiratory Depth and Rhythm

2.1K
Respiratory Depth
Respiratory depth measures the volume of air inhaled or exhaled during a breath. It can vary from shallow to deep and typically remains consistent when a person is at rest or asleep. Occasionally, individuals will automatically inhale deeply, known as sighing, which inflates the lungs with more air than normal breathing.
To assess respiratory depth, observe the degree of chest excursion or movement:
2.1K
Respiratory Volumes and Capacities I01:26

Respiratory Volumes and Capacities I

1.4K
Assessing the respiratory rate and rhythm for a complete minute is crucial for evaluating the breathing pattern. Even a minor increase in the patient's average respiratory rate, by as little as three to five breaths per minute, is an early and vital indicator of respiratory distress. Patients with a respiratory rate exceeding twenty-four breaths per minute require close monitoring to determine the physiological alterations. This careful observation is essential for prompt recognition and...
1.4K
Respiratory Volumes01:15

Respiratory Volumes

2.5K
Respiratory volumes are crucial metrics, meticulously measured to quantify the air exchanged in and out of the lungs during various phases of the breathing cycle. These precise measurements are vital for assessing lung function, diagnosing respiratory conditions, and monitoring overall respiratory health. Each parameter provides specific insights into the mechanics of breathing and the functional capacity of the lungs.
Tidal Volume (TV) Tidal volume (TV) is the air inhaled or exhaled in a...
2.5K
Alterations in Respiration II01:30

Alterations in Respiration II

1.2K
There are numerous types of normal and abnormal respiration. Based on ventilatory movements, breathing patterns are classified as regular, deep, or shallow. Examples include Biot's breathing, Cheyne-Stokes respiration, Kussmaul's breathing, hyperventilation, and hypoventilation. Each pattern is clinically significant and aids in evaluating patients.
In Biot's breathing, the respiratory rate and depth are irregular, alternating between periods of deep gasping and apnea. Common causes...
1.2K

You might also read

Related Articles

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

Sort by
Same author

KMO deletion preserves non-associative learning and SVZ neurogenesis in aging mice.

Behavioural brain research·2026
Same author

Evaluating the quality of brainstem ROI registration using structural and diffusion MRI.

Frontiers in neuroscience·2026
Same author

Multiscale modes of functional brain connectivity.

Imaging neuroscience (Cambridge, Mass.)·2025
Same author

Machine Learning Model for Response to Internet-Delivered CBT vs Antidepressant Medication.

JAMA network open·2025
Same author

Increased Belowground Carbon Allocation Reduces Soil Carbon Losses Under Long-Term Warming.

Global change biology·2025
Same author

A unipolar head gradient for high-field MRI without encoding ambiguity.

Magnetic resonance in medicine·2025
Same journal

Investigating the Neural Origins of Ear-EEG: A Correlation Study Using Scalp EEG Source Reconstruction.

NeuroImage·2026
Same journal

Hysteresis effects in visual and auditory perception and the comparison of underlying neural mechanisms - an EEG study.

NeuroImage·2026
Same journal

Short-term audio-tactile training affects cortical auditory speech-envelope tracking for incongruent but not congruent stimuli.

NeuroImage·2026
Same journal

Dissociable Neurocognitive Mechanisms of State and Trait Anxiety in Working Memory: Threat-Induced Alterations in Decision Dynamics and Attenuation of Large-Scale Network Reconfiguration.

NeuroImage·2026
Same journal

Neuro-Ocular Amyloid Characterization in Alzheimer's Disease via Cross-Site PET-MRI and Hierarchical Cross-Attention Driven Multimodal Representation Learning.

NeuroImage·2026
Same journal

Whole-brain network dynamics underlying intolerance of uncertainty.

NeuroImage·2026
See all related articles

Related Experiment Video

Updated: Nov 19, 2025

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
10:44

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

841

A Hilbert-based method for processing respiratory timeseries.

Samuel J Harrison1, Samuel Bianchi2, Jakob Heinzle3

  • 1Translational Neuromodeling Unit, University of Zurich & ETH Zurich, Zurich, Switzerland; FMRIB, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK.

Neuroimage
|January 31, 2021
PubMed
Summary
This summary is machine-generated.

We present a novel Hilbert transform method for estimating respiratory volume per unit time (RVT) from bellows recordings, improving physiological noise correction in functional magnetic resonance imaging (fMRI) by capturing breathing irregularities.

More Related Videos

Author Spotlight: Enhancing Diagnostic Strategies and Biomarker Development for Comprehensive Lung Function Analysis
05:56

Author Spotlight: Enhancing Diagnostic Strategies and Biomarker Development for Comprehensive Lung Function Analysis

Published on: August 9, 2024

2.1K
Author Spotlight: Optimized Lung MRI Protocol with Computationally Efficient Reconstruction Methods
05:07

Author Spotlight: Optimized Lung MRI Protocol with Computationally Efficient Reconstruction Methods

Published on: September 6, 2024

568

Related Experiment Videos

Last Updated: Nov 19, 2025

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
10:44

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

841
Author Spotlight: Enhancing Diagnostic Strategies and Biomarker Development for Comprehensive Lung Function Analysis
05:56

Author Spotlight: Enhancing Diagnostic Strategies and Biomarker Development for Comprehensive Lung Function Analysis

Published on: August 9, 2024

2.1K
Author Spotlight: Optimized Lung MRI Protocol with Computationally Efficient Reconstruction Methods
05:07

Author Spotlight: Optimized Lung MRI Protocol with Computationally Efficient Reconstruction Methods

Published on: September 6, 2024

568

Area of Science:

  • Neuroimaging
  • Physiological Monitoring
  • Signal Processing

Background:

  • Accurate physiological noise correction is crucial for functional magnetic resonance imaging (fMRI) data quality.
  • Current methods for estimating respiratory volume per unit time (RVT) may lack the necessary time resolution to capture complex breathing patterns.
  • Atypical breathing events can introduce significant physiological noise into fMRI recordings.

Purpose of the Study:

  • To introduce a novel, high-time-resolution method for estimating respiratory volume per unit time (RVT) from respiratory bellows.
  • To improve the characterization of breathing rhythms for enhanced physiological noise correction in fMRI.
  • To demonstrate the effectiveness of the new RVT estimation method in reducing respiration-related variance in fMRI data.

Main Methods:

  • Application of the Hilbert transform, a technique from electrophysiology, to respiratory bellows recordings.
  • Development of a new method for estimating respiratory volume per unit time (RVT) with improved temporal resolution.
  • Integration of the novel RVT estimation into a standard fMRI preprocessing pipeline.

Main Results:

  • The proposed Hilbert transform-based RVT estimation provides higher time resolution compared to traditional peak-based methods.
  • The new method better characterizes atypical breathing events and respiratory rhythms.
  • Using the enhanced RVT estimation in preprocessing significantly increased the removal of respiration-related variance from fMRI data.

Conclusions:

  • The Hilbert transform offers a superior approach for RVT estimation from respiratory bellows data.
  • This method enhances physiological noise correction in fMRI by providing a more accurate representation of breathing patterns.
  • The publicly available PhysIO implementation facilitates the adoption of this improved noise correction technique in neuroimaging research.