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International Work-Conference on Bioinformatics and Biomedical Engineering

IWBBIO 2019: Bioinformatics and Biomedical Engineering pp 561–572Cite as

Estimation of Lung Properties Using ANN-Based Inverse Modeling of Spirometric Data

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Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 11466))

Abstract

Spirometry is the most commonly used test of lung function because the forced expiratory flow-volume (FV) curve is effort-independent and simultaneously sensitive to pathological processes in the lungs. Despite this, a method for the estimation of respiratory system parameters, based on this association, has not been yet proposed. The aim of this work was to explore a feedforward neural network (FFNN) approximating the inverse mapping between the FV curve and respiratory parameters. To this end, the sensitivity analysis of the reduced model for forced expiration has been carried out, showing its local identifiability and the importance of particular parameters. This forward model was then applied to simulate spirometric data (8000 elements), used for training, validating, optimizing and testing the FFNN. The suboptimal FFNN structure had 52 input neurons (for spirometric data), two hidden nonlinear layers with 30 and 20 neurons respectively, and 10 output neurons (for parameter estimates). The total relative error of estimation of individual parameters was between 11 and 28%. Parameter estimates yielded by this inverse FFNN will be used as starting points for a more precise local estimation algorithm.

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Acknowledgments

This work was supported by the grant no. 2016/21/B/ST7/02233 from the National Science Centre, Poland.

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Authors and Affiliations

  1. Wrocław University of Science and Technology, B. Prusa Str. 53/55, 50-317, Wrocław, Poland

    Adam G. Polak, Dariusz Wysoczański & Janusz Mroczka

Authors
  1. Adam G. Polak
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  2. Dariusz Wysoczański
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  3. Janusz Mroczka
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Corresponding author

Correspondence to Adam G. Polak .

Editor information

Editors and Affiliations

  1. Department of Computer Architecture and Computer Technology Higher Technical School of Information Technology and Telecommunications Engineering, CITIC-UGR, Granada, Spain

    Ignacio Rojas

  2. ETSIIT, University of Granada, Granada, Spain

    Olga Valenzuela

  3. CITIC-UGR, University of Granada, Granada, Spain

    Fernando Rojas

  4. Fundacion Progreso y Salud, Granada, Spain

    Francisco Ortuño

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Polak, A.G., Wysoczański, D., Mroczka, J. (2019). Estimation of Lung Properties Using ANN-Based Inverse Modeling of Spirometric Data. In: Rojas, I., Valenzuela, O., Rojas, F., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2019. Lecture Notes in Computer Science(), vol 11466. Springer, Cham. https://doi.org/10.1007/978-3-030-17935-9_50

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  • DOI: https://doi.org/10.1007/978-3-030-17935-9_50

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-17934-2

  • Online ISBN: 978-3-030-17935-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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