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Alterations in Respiration II

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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.
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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.
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Expired CO2 Measurement in Intubated or Spontaneously Breathing Patients from the Emergency Department

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Increased ventilatory variability and complexity in patients with hyperventilation disorder.

Plamen Bokov¹, Marie-Noëlle Fiamma², Brigitte Chevalier-Bidaud³

¹AP-HP, Hôpital Européen Georges Pompidou, Service de Physiologie, Clinique de la Dyspnée, Paris, and Université Paris Descartes, Sorbonne Paris Cité, Faculté de Médecine, Paris, France;

Journal of Applied Physiology (Bethesda, Md. : 1985)

|February 13, 2016

Summary

This summary is machine-generated.

Patients with hyperventilation disorder exhibit increased resting ventilation variability and complexity. This dysfunctional breathing pattern is linked to altered ventilatory control, not increased chemical drive, impacting respiratory stability.

Keywords:

chaotic-like ventilation controller gain loop gain plant gain ventilatory control

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A Model to Simulate Clinically Relevant Hypoxia in Humans

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Published on: December 22, 2016

Area of Science:

Respiratory Physiology
Pulmonary Medicine
Breathing Disorders

Background:

Hyperventilation disorders may stem from abnormal ventilatory control, leading to heightened resting ventilation variability.
Tidal volume (VT) variability often shows a non-normal distribution, characterized by augmented breaths on a log-log scale.

Purpose of the Study:

To investigate resting ventilation variability, respiratory control stability, and chaotic dynamics in patients with dysfunctional breathing.
To compare these parameters in patients experiencing air hunger and low PaCO2 with healthy controls.

Main Methods:

Assessed variability (CV of VT, slope) and respiratory control (loop, controller, plant gains) in 23 hyperventilation patients and 14 healthy subjects.
Analyzed chaotic-like dynamics using embedding dimension and Kappa values to quantify ventilatory complexity.

Main Results:

Hyperventilation patients showed significantly increased VT variability (CV and slope) compared to controls.
Increased variability was associated with higher ventilatory complexity (Kappa values), not augmented chemical drive (loop gain).
Plant gain was reduced in patients and correlated with complexity.

Conclusions:

Patients with hyperventilation disorder exhibit increased resting ventilation variability driven by enhanced ventilatory complexity.
Ventilatory-chemoresponsiveness interactions remained stable despite the observed variability and complexity.