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All Journal Jurnal Elektronika dan Telekomunikasi
Baidillah, Marlin Ramadhan
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Electrical & Electronics Engineering Engineering

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Computational Analysis of Electrical Impedance Spectroscopy for Margin Tissue Detection in Laparoscopic Liver Resection Sulistia, Sulistia; Riyanto, Riyanto; Busono, Pratondo; Kurniawan, Affandi Faisal; Saefan, Joko; Kurniawan, Wawan; Baidillah, Marlin Ramadhan
Jurnal Elektronika dan Telekomunikasi Vol 24, No 1 (2024)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jet.630

Abstract

Margin tissue detection during intraoperative laparoscopic liver resection (LLR) is required to prevent tumor recurrence and reduce the likelihood of further surgery. This study proposes an electrical impedance spectroscopy (EIS) method for margin tissue detection in LLR to determine the boundary interface of normal and cancerous tissue. The proposed method of this study has three objectives: (1) designing the electrode array configuration to collect multiple EIS impedance measurements, (2) implementing the Feedforward Neural Network (FNN) to classify the orientation of margin tissue relative to the electrode array by using time-difference impedance indexes, and (4) governing the inflection point method based on impedance indexes to detect the margin tissue location. The proposed method is evaluated by a 3D numerical simulation of liver tissue composed of cancerous lumps with Iac = 1 mA alternating injection current  at frequencies: lf = 1 kHz and hf = 100 kHz. The electrode array is composed of 16 electrode pairs each for injection current and voltage measurements. The variation of margin tissue orientation relative to the electrode array direction was considered to occur in unidirectional, perpendicular, and diagonal direction with noise variations (Signal-to-Noise-Ratio: 50 to 90 dB). The FNN trained on 2,400 data points achieves True Positive Rate (TPR) value as 90.2%, 99.4%, and 96.6% for diagonal, perpendicular, and unidirectional respectively in margin tissue orientation classification, while the inflection point method detects margin tissue location with 75% location at the unidirectional orientation (y-axis).
Cardiac Imaging with Electrical Impedance Tomography (EIT) using Multilayer Perceptron Network Ristyawardani, Amelia Putri; Baidillah, Marlin Ramadhan; Adityawarman, Yudi; Busono, Pratondo; Rachmadi, Mochamad Adityo; Yantidewi, Meta; Rahmawati, Endah
Jurnal Elektronika dan Telekomunikasi Vol 25, No 1 (2025)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jet.705

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.
Co-Authors Adityawarman, Yudi Affandi Faisal Kurniawan Endah Rahmawati Joko Saefan Pratondo Busono Rachmadi, Mochamad Adityo Ristyawardani, Amelia Putri Riyanto Riyanto SULISTIA, SULISTIA Wawan Kurniawan Yantidewi, Meta