Assessing Pulmonary Function Parameters Non-invasively by Electrical Bioimpedance Tomography

• Published in: Journal of medical and biological engineering 2024-02, Vol.44 (1), p.67-78
• Main Authors: Vargas-Luna, F. M., Delgadillo-Cano, M. I., Riu-Costa, J. P., Kashina, S., Balleza-Ordaz, J. M.
• Format: Article
• Language: English
• Subjects: Air flow; Biological Techniques; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedical Engineering/Biotechnology; Biomedicine; Chronic obstructive pulmonary disease; Correlation; Data points; Electrical impedance; Frequency ranges; Lung diseases; Lungs; Mathematical analysis; Measurement methods; Obstructive lung disease; Original Article; Parameters; Pulmonary functions; Regenerative Medicine/Tissue Engineering; Respiratory function; Thorax; Tomography
• ISSN: 1609-0985; 2199-4757
• DOI: 10.1007/s40846-023-00842-8

Purpose Electrical Impedance Tomography (EIT) holds promise as a non-invasive method for measuring lung airflow, particularly in patients diagnosed with Chronic Obstructive Pulmonary Disease (COPD). Nonetheless, there are challenges regarding the clinical relevance of EIT. The main purpose of the present research was to identify the primary frequency components of impedance changes recorded by EIT and correlate them with pulmonary function parameters. Methods 20 COPD patients were analyzed. Each volunteer was connected to a pneumotachometer and an EIT device. They performed three respiratory exercises, and pulmonary function parameters for each volunteer were acquired. The three impedance signals were convolved to simulate the behavior of the thorax as a black box with a single output signal. The convolved impedance signal was analyzed using FFT spectra. Subsequently, it was divided into seven frequency ranges, estimating the area under the curve and quartiles at 25%, 50%, and 75%. Each segment of the FFT spectrum was correlated with each pulmonary function test parameter. Results A significant correlation of over 60% between pulmonary function test parameters and the determinations from the FFT spectrum within seven distinct frequency ranges was observed. However, the determination coefficient ( R 2 ) ranged from approximately 10–66% due to data points that did not fit well, particularly in patients with severe pulmonary dysfunction. Conclusion To address the dispersion of data and enhance the correlation between determinations, it is imperative to adjust impedance determinations using anthropometric parameters or employ a mathematical equation that facilitates the characterization of limitations in lung airflow.