Energy Efficiency in Measurement and Image Reconstruction Processes in Electrical Impedance Tomography

Barbara Stefaniak, Tomasz Rymarczyk (), Dariusz Wójcik, Marta Cholewa-Wiktor, Tomasz Cieplak, Zbigniew Orzeł, Janusz Gudowski, Ewa Golec, Michał Oleszek and Marcin Kowalski
Additional contact information
Barbara Stefaniak: Research & Development Centre Netrix S.A., 20-704 Lublin, Poland
Tomasz Rymarczyk: Research & Development Centre Netrix S.A., 20-704 Lublin, Poland
Dariusz Wójcik: Research & Development Centre Netrix S.A., 20-704 Lublin, Poland
Marta Cholewa-Wiktor: Department of Management, Lublin University of Technology, 20-618 Lublin, Poland
Tomasz Cieplak: Department of Management, Lublin University of Technology, 20-618 Lublin, Poland
Zbigniew Orzeł: Institute of Computer Science and Innovative Technologies, WSEI University, 20-209 Lublin, Poland
Janusz Gudowski: Institute of Computer Science and Innovative Technologies, WSEI University, 20-209 Lublin, Poland
Ewa Golec: Institute of Computer Science and Innovative Technologies, WSEI University, 20-209 Lublin, Poland
Michał Oleszek: Research & Development Centre Netrix S.A., 20-704 Lublin, Poland
Marcin Kowalski: Institute of Computer Science and Innovative Technologies, WSEI University, 20-209 Lublin, Poland

Energies, 2024, vol. 17, issue 23, 1-15

Abstract: This paper presents an energy optimization approach to applying electrical impedance tomography (EIT) for medical diagnostics, particularly in detecting lung diseases. The designed Lung Electrical Tomography System (LETS) incorporates 102 electrodes and advanced image reconstruction algorithms. Energy efficiency is achieved through the use of modern electronic components and high-efficiency DC/DC converters that reduce the size and weight of the device without the need for additional cooling. Special attention is given to minimizing energy consumption during electromagnetic measurements and data processing, significantly improving the system’s overall performance. Research studies confirm the device’s high energy efficiency while maintaining the accuracy of the classification of lung disease using the LightGBM algorithm. This solution enables long-term patient monitoring and precise diagnosis with reduced energy consumption, marking a key step towards sustainable medical diagnostics based on EIT technology.

Keywords: electrical impedance tomography; energy efficiency; machine learning; data visualization; discover patterns (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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