Cardiac Imaging with Electrical Impedance Tomography (EIT) using Multilayer Perceptron Network
Amelia Putri Ristyawardani (1*), Marlin Ramadhan Baidillah (2), Yudi Adityawarman (3), Pratondo Busono (4), Mochamad Adityo Rachmadi (5), Meta Yantidewi (6), Endah Rahmawati (7) (1) - Indonesia
(2) - Indonesia
(3) - Indonesia
(4) - Indonesia
(5) - Indonesia
(6) - Indonesia
(7) - Indonesia
(*) Corresponding Author
Received: November 22, 2024; Revised: February 04, 2025
Accepted: April 25, 2025; Published: August 31, 2025
Abstract
This research explores the enhancement of Electrical Impedance Tomography (EIT) for cardiac imaging using Multilayer Perceptron (MLP) networks, focusing on supervised and semi-supervised learning approaches. Using synthetic thoracic datasets simulating dynamic cardiac and respiratory conditions, the study demonstrates that supervised learning achieves lower mean squared error (MSE) values (minimum 4.76) and more stable predictions compared to semi-supervised learning (minimum MSE 5.08). However, semi-supervised learning excels in edge accuracy and noise reduction, particularly in regions with sharp conductivity gradients, making it viable for scenarios with limited labeled data. Dropout regularization at 0.3 provided optimal balance, enhancing model generalization and robustness. While supervised learning outperformed semi-supervised methods in overall accuracy, the latter showed potential for cost-effective and scalable applications in EIT-based cardiac imaging. These findings suggest that integrating advanced machine learning with EIT can improve diagnostic accuracy and enable efficient use of sparse labeled data, paving the way for future optimizations and clinical applications.
http://dx.doi.org/10.55981/jet.705 Keywords
References
H. Tran, Hoang Van and Kim, Min Hyuck and Le, Thong Chi and Yoon, High accurate and efficient electrical impedance tomography for fast brain imaging. 2023. doi: 10.1117/12.2659172.
A. Z. Hayat, A. T. Nugroho, and N. Priyantari, “Prototype Portable Electrical Resistance Tomography,” IPTEK J. Technol. Sci., vol. 32, no. 3, p. 136, 2022, doi: 10.12962/j20882033.v32i3.8843.
G. Y. Jang et al., “Noninvasive, simultaneous, and continuous measurements of stroke volume and tidal volume using EIT: feasibility study of animal experiments,” Sci. Rep., vol. 10, no. 1, pp. 1–12, 2020, doi: 10.1038/s41598-020-68139-3.
A. Nguyen, Duc and Qian, Pierre and Barry, Michael and Mcewan, “Self-weighted NOSER-prior electrical impedance tomography using internal electrodes in cardiac radiofrequency ablationle,” Physiol. Meas., vol. 40, p. 065006, 2019, doi: 10.1088/1361-6579/ab1937.
C. Putensen, B. Hentze, S. Muenster, and T. Muders, “Electrical impedance tomography for cardio-pulmonary monitoring,” J. Clin. Med., vol. 8, no. 8, 2019, doi: 10.3390/jcm8081176.
A. Santini, E. Spinelli, T. Langer, S. Spadaro, G. Grasselli, and T. Mauri, “Thoracic electrical impedance tomography: an adaptive monitor for dynamic organs,” J. Emerg. Crit. Care Med., vol. 2, no. September, pp. 71–71, 2018, doi: 10.21037/jeccm.2018.08.08.
J. Caruana, Lawrence & Paratz, Jennifer & Chang, A & Barnett, Adrian & Fraser, “The Time Taken for the Regional Distribution of Ventilation to Stabilise: An Investigation Using Electrical Impedance Tomography.,” Anaesth. Intensive Care, vol. 43, pp. 88–91, 2015, doi: 10.1177/0310057X1504300113.
S. Kumar and R. Kumar, “Analysis and Validation of medical Application through Electrical Impedance based System,” 2018. doi: 10.18201/ijisae.2018637925.
S. J. Hamilton and A. Hauptmann, “Deep D-Bar: Real-Time Electrical Impedance Tomography Imaging With Deep Neural Networks,” IEEE Trans. Med. Imaging, vol. 37, no. 10, pp. 2367–2377, 2018, doi: 10.1109/TMI.2018.2828303.
F. Song, X., Xu, Y., & Dong, “A spatially adaptive total variation regularization method for electrical resistance tomography,” Meas. Sci. Technol., vol. 26, no. 12, p. 125401, 2015, doi: 10.1088/0957-0233/26/12/125401.
L. Karrach and E. Pivarčiová, “Using Different Types of Artificial Neural Networks to Classify 2D Matrix Codes and Their Rotations—A Comparative Study,” J. Imaging, vol. 9, no. 9, pp. 1–16, 2023, doi: 10.3390/jimaging9090188.
J. E. van Engelen and H. H. Hoos, “A survey on semi-supervised learning,” Mach. Learn., vol. 109, no. 2, pp. 373–440, 2020, doi: 10.1007/s10994-019-05855-6.
C. S. Perone and J. Cohen-Adad, “Deep semi-supervised segmentation with weight-averaged consistency targets,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 11045 LNCS, pp. 12–19, 2018, doi: 10.1007/978-3-030-00889-5_2.
E. Endarko and A. B. Sanjaya Umbu, “Design of low-cost and simple reconstruction method for Three Dimensional Electrical Impedance Tomography (3D-EIT) Imaging,” J. Penelit. Fis. dan Apl., vol. 10, no. 2, p. 125, 2020, doi: 10.26740/jpfa.v10n2.p125-136.
S. Liu, J. Jia, Y. D. Zhang, and Y. Yang, “Image Reconstruction in Electrical Impedance Tomography Based on Structure-Aware Sparse Bayesian Learning,” IEEE Trans. Med. Imaging, vol. 37, no. 9, pp. 2090–2102, 2018, doi: 10.1109/TMI.2018.2816739.
A. Zhang, Ke and Li, Maokun and Liang, Haiqing and Wang, Juan and Xu, Shenheng and Abubakar, “Deep feature-domain matching for cardiac-related component separation from image series of chest electrical impedance tomography: proof-of-concept study,” Physiol. Meas., vol. 43, 2022, doi: 10.1088/1361-6579/ac9c44.
T. Rymarczyk, G. Kłosowski, E. Kozłowski, J. Sikora, and P. Adamkiewicz, “Improving the tomographic image by enhancing the machine learning algorithm,” J. Phys. Conf. Ser., vol. 2408, no. 1, 2022, doi: 10.1088/1742-6596/2408/1/012020.
T. Rymarczyk, G. Klosowski, E. Kozlowski, and P. Tchórzewski, “Comparison of selected machine learning algorithms for industrial electrical tomography,” Sensors (Switzerland), vol. 19, no. 7, 2019, doi: 10.3390/s19071521.
T. Huuhtanen and A. Jung, “Anomaly location detection with electrical impedance tomography using multilayer perceptrons,” IEEE Int. Work. Mach. Learn. Signal Process. MLSP, vol. 2020-September, 2020, doi: 10.1109/MLSP49062.2020.9231818.
B. Grychtol, B. Müller, and A. Adler, “3D EIT image reconstruction with GREIT,” Physiol. Meas., vol. 37, no. 6, pp. 785–800, 2016, doi: 10.1088/0967-3334/37/6/785.
F. Thürk et al., “Effects of individualized electrical impedance tomography and image reconstruction settings upon the assessment of regional ventilation distribution: Comparison to 4-dimensional computed tomography in a porcine model,” PLoS One, vol. 12, no. 8, pp. 1–16, 2017, doi: 10.1371/journal.pone.0182215.
Z. Han and S. Yin, “Research on Semi-supervised Classification with an Ensemble Strategy,” vol. 136, no. Icsma, pp. 681–684, 2016, doi: 10.2991/icsma-16.2016.119.
K. Hornik, M. Stinchcombe, and H. White, “Multilayer feedforward networks are universal approximators,” Neural Networks, vol. 2, no. 5, pp. 359–366, 1989, doi: 10.1016/0893-6080(89)90020-8.
T. Miyato, S. I. Maeda, M. Koyama, and S. Ishii, “Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 41, no. 8, pp. 1979–1993, 2019, doi: 10.1109/TPAMI.2018.2858821.
B. Liu, B. Yang, C. Xu, J. Xia, M. Dai, Z. Ji, F. You, X. Dong, X. Shi, and F. Fu, “pyEIT: A python based framework for Electrical Impedance Tomography,” SoftwareX, vol. 7, pp. 304–308, 2018.
F. Chollet, “Keras: the Python deep learning API,” 2015, https://keras.io/.
Article Metrics
Metrics powered by PLOS ALM
Refbacks
- There are currently no refbacks.
Copyright (c) 2025 National Research and Innovation Agency
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
JET is published by National Research and Innovation Agency (BRIN) - formerly LIPI
Copyright of BRIN :: p-ISSN:1411-8289, e-ISSN:2527-9955 
Email this article 
