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Vision, Sensing and Analytics: Integrative Approaches pp 307–325Cite as

Electrical Impedance Tomography Based Lung Disease Monitoring

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Part of the Intelligent Systems Reference Library book series (ISRL,volume 207)

Abstract

Electrical impedance measurements can detect many diseases and disorders in the human body. Electrical Impedance Tomography (EIT) is a fast-developing medical imaging technique. In this chapter, we present some applications of EIT in lung disease detection. Existing literature in this subject has been investigated, including original research work on EIT based lung imaging, carried out in clinical settings for particular respiratory diseases: Acute respiratory distress syndrome (ARDS), Chronic obstructive pulmonary disease (COPD), Cystic fibrosis, Pneumonia, and Pleural effusion. Information about the purpose of the tests, studied subjects, the procedure followed, and results of analysis have been included as well as limitations of the studies. EIT is a promising technique in the direction of non-invasive diagnostic medicine because this can perform imaging of the human body, without posing any visible risk.

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  • DOI: 10.1007/978-3-030-75490-7_11
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Fig. 11.1
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Authors and Affiliations

  1. Department of Electrical and Electronic Engineering, University of Dhaka, Dhaka, Bangladesh

    Aniqa Tabassum & Md Atiqur Rahman Ahad

  2. Department of Intelligent Media, Osaka University, Suita, Japan

    Md Atiqur Rahman Ahad

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  1. Aniqa Tabassum
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Correspondence to Md Atiqur Rahman Ahad .

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  1. Professor, Department of Electrical and Electronic Engineering, University of Dhaka, Dhaka, Bangladesh

    Md Atiqur Rahman Ahad

  2. Solutions Architect for Greenfield and Startups, Amazon Web Services, USA, Visiting Professor, Graduate School of Regional Innovation, Mie University, Tsu, Japan

    Prof. Atsushi Inoue

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Tabassum, A., Ahad, M.A.R. (2021). Electrical Impedance Tomography Based Lung Disease Monitoring. In: Ahad, M.A.R., Inoue, A. (eds) Vision, Sensing and Analytics: Integrative Approaches. Intelligent Systems Reference Library, vol 207. Springer, Cham. https://doi.org/10.1007/978-3-030-75490-7_11

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