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

Assessment of Ventilation II: Respiratory Depth and Rhythm

Assessment of Ventilation II: Respiratory Depth and Rhythm

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:

Respiratory Volumes and Capacities I

Respiratory Volumes and Capacities I

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...

Application of Integration: Problem Solving

Application of Integration: Problem Solving

The process of breathing involves the periodic intake and expulsion of air, known as the respiratory cycle, which typically lasts about five seconds. Modeling the volume of air inhaled into the lungs as a function of time provides insight into both the dynamics and efficiency of pulmonary ventilation. This volume is determined by integrating the airflow rate over time, which captures the cumulative effect of air entering the lungs.Sinusoidal Model of AirflowAirflow during respiration is not...

Assessment of Ventilation I: Respiratory Rate

Assessment of Ventilation I: Respiratory Rate

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:

Special considerations while measuring oxygen saturation

Special considerations while measuring oxygen saturation

Assessing respiratory rate concurrently with pulse measurement is fundamental to patient care, providing valuable insights into the patient's respiratory function. The normal breathing rate for an adult usually falls within a normal range of 12 to 20 breaths per minute. Abnormal respiratory rates can signal underlying health conditions or the need for immediate intervention.
Ensuring accuracy in vital sign recordings while prioritizing patient comfort and minimizing anxiety is important.

Physical Assessment of the Respiratory Tract II: Inspection

Physical Assessment of the Respiratory Tract II: Inspection

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 can...

You might also read

Related Articles

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

Sort by

Same author

Neurophysiological correlates of delayed recovery of consciousness in a critically ill patient with COVID-19 with repeated cardiac arrest.

British journal of anaesthesia·2026

Same author

Determinants of Delayed Recovery of Consciousness After Analgosedation Discontinuation in the ICU: Insights From Patients With COVID-19 Hypoxemic Respiratory Failure.

Critical care medicine·2026

Same author

User experience and real-world implementation of a peer-integrated digital health intervention for substance use disorder.

Research square·2026

Same author

Electroencephalographic Monitoring in the Recovery Room for Identification of Patients at Risk for Postoperative Delirium.

Anesthesiology·2026

Same author

Similar destabilization of neural dynamics under different general anesthetics.

Cell reports·2026

Same author

Time-frequency embedding with contrastive pre-training allows sub-second seizure detection.

bioRxiv : the preprint server for biology·2026

Search research articles

Related Experiment Video

Updated: May 24, 2026

Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities

Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities

Published on: May 10, 2017

Point process time-frequency analysis of dynamic respiratory patterns during meditation practice.

Sandun Kodituwakku¹, Sara W Lazar, Premananda Indic

¹Applied Signal Processing Group, School of Engineering, The Australian National University, Canberra, Australia. sandun.kodituwakku@anu.edu.au

Medical & Biological Engineering & Computing

|February 22, 2012

Summary

We developed a new algorithm to measure respiratory sinus arrhythmia (RSA), a heart rhythm influenced by breathing. This method accurately tracks changes in cardiorespiratory interactions during meditation.

More Related Videos

Method to Obtain Pattern of Breathing in Senescent Mice through Unrestrained Barometric Plethysmography

Method to Obtain Pattern of Breathing in Senescent Mice through Unrestrained Barometric Plethysmography

Published on: April 28, 2020

Investigation into Deep Breathing through Measurement of Ventilatory Parameters and Observation of Breathing Patterns

Investigation into Deep Breathing through Measurement of Ventilatory Parameters and Observation of Breathing Patterns

Published on: September 16, 2019

Related Experiment Videos

Last Updated: May 24, 2026

Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities

Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities

Published on: May 10, 2017

Method to Obtain Pattern of Breathing in Senescent Mice through Unrestrained Barometric Plethysmography

Method to Obtain Pattern of Breathing in Senescent Mice through Unrestrained Barometric Plethysmography

Published on: April 28, 2020

Investigation into Deep Breathing through Measurement of Ventilatory Parameters and Observation of Breathing Patterns

Investigation into Deep Breathing through Measurement of Ventilatory Parameters and Observation of Breathing Patterns

Published on: September 16, 2019

Area of Science:

Cardiology
Autonomic Nervous System Physiology
Biomedical Signal Processing

Background:

Respiratory sinus arrhythmia (RSA) reflects autonomic nervous system (ANS) influence on heart rate.
Quantifying RSA under dynamic breathing conditions remains challenging.
Existing methods may not capture beat-to-beat variations effectively.

Purpose of the Study:

To develop and validate a robust algorithm for quantifying instantaneous respiratory sinus arrhythmia (RSA).
To assess cardiorespiratory interactions during dynamic breathing patterns, specifically during meditative practices.
To investigate changes in RSA gain during meditation compared to resting states.

Main Methods:

Modeling pulse intervals (PIs) using an inverse Gaussian point process with bivariate regression.
Estimating instantaneous RSA via a frequency domain transfer function and maximum likelihood estimation.
Statistically validating the algorithm using Kolmogorov-Smirnov tests and independence analyses.

Main Results:

The algorithm accurately quantifies instantaneous RSA under dynamic breathing patterns.
Statistically significant increases in RSA gain were observed during meditative practice.
The algorithm successfully tracked cardiorespiratory interaction changes during meditation, not evident in resting states.

Conclusions:

The proposed algorithm provides a robust method for quantifying instantaneous RSA.
Meditation elicits distinct cardiorespiratory dynamics, characterized by increased RSA gain.
This approach enhances the understanding of ANS modulation of heart rate during specific physiological states.