Identification of Breathing Patterns through EEG Signal Analysis Using Machine Learning.

Area of Science:

• Neuroscience
• Physiology
• Biomedical Engineering

Background:

• Mouth breathing can lead to reduced oxygen supply to the brain.
• The impact of mouth breathing on brain function, particularly during cognitive tasks, requires further investigation.
• Understanding these effects is crucial for optimizing work environments and employee well-being.

Purpose of the Study:

• To investigate the effects of mouth breathing on brain function due to oxygen deprivation.
• To propose a method for mitigating the negative impacts of mouth breathing on brain function via supplemental oxygen.
• To differentiate breathing patterns and their effects using electroencephalogram (EEG) signals and machine learning.

Main Methods:

• Recruited twenty subjects to perform a working memory task.
• Recorded electroencephalogram (EEG) signals during three breathing conditions: nose breathing, mouth breathing, and mouth breathing with supplemental oxygen.
• Utilized machine learning techniques to classify breathing patterns and analyze their impact on brain activity.

Main Results:

• Nose breathing ensures adequate oxygen supply to the brain.
• Mouth breathing was found to significantly interrupt oxygen supply to the brain.
• Machine learning analysis of EEG signals confirmed that differences in oxygen supply are key to distinguishing the effects of mouth versus nose breathing on brain function.

Conclusions:

• Mouth breathing negatively impacts brain function by reducing oxygen supply.
• Supplemental oxygen during mouth breathing can counteract these detrimental effects.
• Workplace environments, especially those with prolonged work hours in confined spaces, should ensure adequate oxygen levels to maintain employee efficiency and cognitive function.