The Trade-off in Machine Learning Application for Electrical Impedance Tomography


  • Marlin Ramadhan Baidillah Research Center for Electronics, National Research and Innovation Agency (BRIN), Kawasan PUSPIPTEK, Tangerang Selatan, 15314, Indonesia
  • Pratondo Busono Research Center for Electronics, National Research and Innovation Agency (BRIN), Kawasan PUSPIPTEK, Tangerang Selatan, 15314, Indonesia



[1] Brown, B.H., 2003. Electrical impedance tomography (EIT): A review. Journal of Medical Engineering & Technology. 27(3), 97-108. DOI:

[2] Khambampati, A.K., Rahman, S.A., Sharma, S.K., et al., 2022. Nonlinear difference imaging to image local conductivity of single-layer graphene using electrical impedance tomography. IEEE Transactions on Instrumentation and Measurement. DOI:

[3] Yao, J., Takei, M., 2017. Application of Process Tomography to Multiphase Flow Measurement in Industrial and Biomedical Fields - A Review. IEEE Sensors Journal. pp. 1. DOI:

[4] Hahn, G., Just, A., Dittmar, J., et al., 2008. Systematic errors of EIT systems determined by easily-scalable resistive phantoms. Physiological Measurement. 29(6), S163-172. DOI:

[5] Cook, R.D., Saulnier, G.J., Gisser, D.G., et al., 1994. ACT3: a high-speed, high-precision electrical impedance tomograph. IEEE Transactions on Biomedical Engineering. 41(8), 713-722. DOI:

[6] Baidillah, M.R., Iman, A.A.S., Sun, Y., et al., 2017. Electrical Impedance Spectro-Tomography based on Dielectric Relaxation Model. IEEE Sensors Journal. 17(24), 8251-8262. DOI:

[7] Hoyle, B.S., Nahvi, M., 2008. Spectro-tomography - an electrical sensing method for integrated estimation of component identification and distribution mapping in industrial processes. 2008 IEEE Sensors. pp. 807-810. DOI:

[8] Ogawa, R., Baidillah, M.R., Akita, S., et al., 2020. Investigation of physiological swelling on conductivity distribution in lower leg subcutaneous tissue by electrical impedance tomography. Journal of Electrical Bioimpedance. 11(1), 19-25. DOI:

[9] Marashdeh, Q., Warsito, W., Fan, L.S., et al., 2006. An Impedance Tomography system based on ECT sensor. IEEE Sensors Journal. 1(11), 1-7.

[10] Wang, Q., Wang, M., Wei, K., et al., 2017. Visualization of Gas-Oil-Water Flow in Horizontal Pipeline Using Dual-Modality Electrical Tomographic Systems. IEEE Sensors Journal. 17(24), 8146-8156. DOI:

[11] Gürsoy, D., Mamatjan, Y., Adler, A., et al., 2011. Enhancing impedance imaging through multimodal tomography. IEEE Transactions on Biomedical Engineering. 58(11), 3215-3224. DOI:

[12] Choridah, L., Kurniadi, D., Ain, K., et al., 2021. Comparison of Electrical Impedance Tomography and Ultrasonography for Determination of Solid and Cystic Lesion Resembling Breast Tumor Embedded in Chicken Phantom. Journal of Electrical Bioimpedance. 12(1), 63. DOI:

[13] Dy, J.G., Brodley, C.E., Kak, A., et al., 2003. Unsupervised feature selection applied to content-based retrieval of lung images. IEEE Transactions on Pattern Analysis and Machine Intelligence. 25(3), 373-378. DOI:

[14] Rymarczyk, T., Kłosowski, G., Hoła, A., et al., 2022. Optimising the use of Machine learning algorithms in electrical tomography of building Walls: Pixel oriented ensemble approach. Measurement. 188, 110581. DOI:

[15] Coxson, A., Mihov, I., Wang, Z., et al., 2022. Machine learning enhanced electrical impedance tomography for 2D materials. Inverse Problems. 38(8), 085007.

[16] Tanaka, K., Prayitno, Y.A.K., Sejati, P.A., et al., 2022. Void fraction estimation in vertical gas-liquid flow by plural long short-term memory with sparse model implemented in multiple current-voltage system. Multiphase Science and Technology. 34(2).

[17] Wu, Y., Jiang, D., Liu, X., et al., 2018. A Human-Machine Interface Using Electrical Impedance Tomography for Hand Prosthesis Control. IEEE Transactions on Biomedical Circuits and Systems. 12(6), 1322-1333. DOI:

[18] Valueva, M.V., Nagornov, N.N., Lyakhov, P.A., et al., 2020. Application of the residue number system to reduce hardware costs of the convolutional neural network implementation. Mathematics and Computers in Simulation. 177, 232-243. DOI:

[19] Hochreiter, S., Schmidhuber, J., 1997. Long Short-Term Memory. Neural Computation. 9(8), 1735-1780.

[20] Lee, H., Huang, C., Yune, S., et al., 2019. Machine Friendly Machine Learning: Interpretation of Computed Tomography Without Image Reconstruction. Scientific Reports. 9(1), 1-9. DOI:




How to Cite

Baidillah, M. R., & Busono, P. (2022). The Trade-off in Machine Learning Application for Electrical Impedance Tomography. Electrical Science & Engineering, 4(2), 8–10.





Download data is not yet available.