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

This study was to investigate the changes in brain function due to lack of oxygen (O) caused by mouth breathing, and to suggest a method to alleviate the side effects of mouth breathing on brain function through an additional O supply. For this purpose, we classified the breathing patterns according to EEG signals using a machine learning technique and proposed a method to reduce the side effects of mouth breathing on brain function. Twenty subjects participated in this study, and each subject performed three different breathings: nose and mouth breathing and mouth breathing with O supply during a working memory task. The results showed that nose breathing guarantees normal O supply to the brain, but mouth breathing interrupts the O supply to the brain. Therefore, this comparative study of EEG signals using machine learning showed that one of the most important elements distinguishing the effects of mouth and nose breathing on brain function was the difference in O supply. These findings have important implications for the workplace environment, suggesting that special care is required for employees who work long hours in confined spaces such as public transport, and that a sufficient O supply is needed in the workplace for working efficiency.