Year: 2018
Type: article
Abstract: Respiration is commonly monitored using direct measurement of airflow with a mouthpiece or other access to breathing airflow.Indirect methods, such as those based on electrocardiography or chest impedance, may be advantageous especially if they already need to be performed.When direct access to breathing airflow is difficult or inconvenient an indirect approach may be needed.Some physiological signals are known to be affected by respiration, suggesting possible utility for extracting a respiratory signal from these signals.In the current study, the performance of several non-invasive methods for respiratory flow monitoring was compared.The study investigated the correlation between lung volume changes and other physiological signals including: chest wall movement (WM), chest galvanic skin response (GSR) and seismocardiographic (SCG) signals.Respiratory airflow was simultaneously measured along with the proposed candidate modalities.The measured airflow signal was integrated to determine lung volume changes, which was chosen as the reference signal.A respiratory signal was extracted from each of the proposed modalities and compared with the lung volume signal.Using airflow measurements, the average respiratory rate was found to be 13.8 breath per minute in the study subjects.Comparisons of the respiratory rate from different methods suggested that the respiratory rate was successfully detected by all proposed methods (accuracy between 85-100%).The ability of different methods to detect the correct respiratory phase in the time domain varied.Among the proposed methods, GSR was found to have the highest agreement with the reference method in respiratory phase detection (94-98%).This indicates that GSR may be used to accurately identify both respiratory rate and lung volume phases.One important GSR benefit is that it only requires chest surface sensors, which would offer operational advantages when direct access to breathing airflow stream is undesirable.
Institution: University of Central Florida
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W2919437992