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Assessing Pulmonary Function Parameters Non-invasively by Electrical Bioimpedance Tomography

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Abstract

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 (R2) 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.

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Data Availability

Data will be made available upon reasonable request.

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Funding

The authors thank IDEA-Guanajuato and Direccion de Apoyo a la Investigación y al Posgrado (DAIP) for their financial support (codes CIIC 2023, 213/2023 and IDEAGTO/CONV/036/2022, respectively).

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Authors and Affiliations

  1. Physics Engineering Department, Science and Engineering Division, University of Guanajuato, León, Guanajuato, México

    F. M. Vargas-Luna, M. I. Delgadillo-Cano, S. Kashina & J. M. Balleza-Ordaz

  2. Electronics and Biomedical Instrumentation Department, Universitat Politècnica de Catalunya, Barcelona, Spain

    J. P. Riu-Costa

Authors
  1. F. M. Vargas-Luna
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  2. M. I. Delgadillo-Cano
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  3. J. P. Riu-Costa
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  4. S. Kashina
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  5. J. M. Balleza-Ordaz
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Contributions

FMVL: Data acquisition, data analysis and interpretation, the first draft of the manuscript. MIDC: Data analysis and interpretation, review of the manuscript. JPRC: Study design, data analysis and interpretation, review of the manuscript. SK: Study design, data acquisition, data analysis and interpretation, review of the manuscript, funding acquisition. JMBO: Study design, data acquisition, data analysis and interpretation, review of the manuscript, funding acquisition. All authors approved the version to be published and agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Corresponding authors

Correspondence to S. Kashina or J. M. Balleza-Ordaz.

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Competing interest

The authors have no relevant financial or non-financial interests to disclose.

Ethics Approval

All patients voluntarily consented to participate in the study, which had been previously approved by the institutional ethics committee.

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All patients voluntarily consented to participate in the study by signing informed consent form.

Consent to Publish

The manuscript does not contain data that discloses confidential information or reveals the identity of the participants. Participants agreed that the obtained data would be published as simple data set.

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Vargas-Luna, F.M., Delgadillo-Cano, M.I., Riu-Costa, J.P. et al. Assessing Pulmonary Function Parameters Non-invasively by Electrical Bioimpedance Tomography. J. Med. Biol. Eng. 44, 67–78 (2024). https://doi.org/10.1007/s40846-023-00842-8

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  • DOI: https://doi.org/10.1007/s40846-023-00842-8

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