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Journal of Electronics, Electromedical Engineering, and Medical Informatics
Published by Polilteknik Kesehatan Kemenkes Surabaya
ISSN : -     EISSN : 26568632     DOI : https://doi.org/10.35882/jeeemi
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering
The Journal of Electronics, Electromedical Engineering, and Medical Informatics (JEEEMI) is a peer-reviewed open-access journal. The journal invites scientists and engineers throughout the world to exchange and disseminate theoretical and practice-oriented topics which covers three (3) majors areas of research that includes 1) Electronics, 2) Biomedical Engineering, and 3)Medical Informatics (emphasize on hardware and software design). Submitted papers must be written in English for an initial review stage by editors and further review process by a minimum of two reviewers.
Arjuna Subject : Teknik (semua kategori) - Teknik Biomedis
Articles 287 Documents
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Combination of Image Enhancement and Double U-Net Architecture for Liver Segmentation in CT-Scan Images Fitri Brianna, Dwi; Indra Kesuma, Lucky; Geovani, Dite; Sari, Puspa
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 1 (2025): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i1.582

Abstract

Liver cancer can be identified using CT-Scan liver image segmentation. Liver segmentation can be performed using CNN architecture like U-Net. However, the segmentation results using U-Net architecture are affected by image quality. Low image quality can affect the accuracy of segmentation results. This study proposes a combination of image enhancement and segmentation stages on CT-Scan liver images. Image enhancement is achieved by using a combination of CLAHE to enhance contrast and Bilateral Filter to reduce noise. The segmentation architecture proposed in this study is Double U-Net which is a development of U-Net architecture by adding a second U-Net block with the same structure as a single U-Net. The first U-Net is used to extract simple features, while the second U-Net is used to extract more complex features and enhance the segmentation results of the first U-Net. PSNR and SSIM measure the results of image enhancement. The PSNR is more than 40dB and the SSIM result is close to 1. These results show that the proposed image enhancement method can enhance the quality of original images. The segmentation results were measured by calculating accuracy, sensitivity, specificity, dice score, and IoU. The result of liver segmentation obtained 99% for accuracy, 98% for sensitivity, 99% for specificity, 98% for dice score, and 90% for IoU. This shows that liver segmentation using Double U-Net obtained good segmentation. Results of image enhancement and image segmentation show that the proposed method is very good for enhancing image quality and performing liver segmentation accurately.
Modelling of Human Cerebral Blood Vessels for Improved Surgical Training: Image Processing and 3D Printing Jacinda, Reica Diva; Yossy, Nebrisca Patriana; Menik Dwi Kurniatie; Hawar, Ihtifazhuddin; Setiawan, Andreas Wilson; Adidharma, Peter; Prasetya, Mustaqim; Desem, Muhammad Ibrahim; Asmaria, Talitha
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 1 (2025): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i1.583

Abstract

Human cerebral blood vessels are highly intricate and significantly contribute to brain function support. In the surgical process of these vessels, the neurosurgeons will basically employ magnetic resonance imaging (MRI) as an imaging media to understand the location of the disorder, the anatomical position of vessels, and a guide in the surgical process. However, the usage of MRI data remains a challenge for surgeons in understanding anatomical structures in greater detail, as well as the limitations of training in handling difficult cases. This study aims to provide further technology, combining three-dimensional (3D) image models and 3D printing to accommodate the lack of visualization and pre-operative simulation using MRI data. First, the MRI data would be exported to a software 3D slicer that has the ability to process images with a threshold method to segment the required body parts and generate 3D models. Then, the 3D model of blood vessels would be imprinted using the SLA method to provide the complex anatomical structures of blood vessels. The results from both 3D image modeling and 3D printing have been validated and have dimensions similar to those of the MRI data, indicating that this work is highly accurate. This work significantly helps the surgeons to have a better plan for the surgery steps, identify potential issues before the procedure begins, and develop more precise approaches.
Improved Edge Detection using Morphological Operation to Segmentation of Fingernail Images Kurniastuti, Ima; Herlambang, Teguh; Wulan, Tri Deviasari; Magfira, Dike Bayu; Meutia, Nur Shabrina; Saputro, Hendik Eko; Soraya, Sabrina Ifahdini
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 1 (2025): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i1.589

Abstract

Accurate segmentation of fingernail images is essential for biomedical applications like dermatological diagnostics and nail disease assessments. This study compares traditional methods (Sobel and Canny edge detectors) with an improved method using adaptive thresholding and morphological closing for fingernail image segmentation. The methodology includes data collection, preprocessing, edge detection, segmentation, and evaluation. A dataset of 500 fingernail images (free of nail polish) was captured using a digital camera. Preprocessing involves grayscale conversion to simplify analysis and Gaussian smoothing to reduce noise while preserving key features. For segmentation, thresholding and K-means clustering isolate the fingernail from the background. Evaluation combines qualitative and quantitative analyses. Qualitative results demonstrate that the improved method consistently outperforms traditional techniques under diverse conditions. Quantitative evaluation, based on accuracy, recall, F1 score, and Intersection over Union (IoU), further supports these findings. The Sobel method achieves 0.80 accuracy, 0.77 recall, 0.87 F1 score, and 0.77 IoU. The Canny method achieves 0.82 accuracy, 0.78 recall, 0.88 F1 score, and 0.78 IoU. In contrast, the improved method achieves 0.97 accuracy, 0.98 recall, 0.99 F1 score, and 0.98 IoU. The results clearly show that the improved method, using adaptive thresholding and morphological closing, provides superior segmentation performance. Additionally, the approach remains computationally efficient, making it suitable for real-time applications in medical diagnostics.
Single Channel Electrogastrogram Frequency Domain Analysis and Correspondence to Brain Activity in a Resting State Condition Sahroni, Alvin; Miladiyah, Isnatin; Adinandra, Sisdarmanto; Sofyan, Pramudya Rakhmadyansyah; Anora, Levina; Hanafi, Mhd.
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 1 (2025): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i1.590

Abstract

An electrogastrogram (EGG) is a well-known method to record gastric myoelectrical activity. However, some researchers believe that EGG measures the gastric slow wave and can be used as a surrogate for gastric motility, whereas others claim that EGG is flawed. Our proposed study broadens the scope of EGG research, particularly by offering the opportunity to observe gut-brain signaling pathways, which can enhance our understanding of brain properties and behavior in response to psychological changes. This study focuses on how to confirm single-channel EGG's setup with public datasets and previous studies and how to observe the relationship of gut-brain axis pathways. We gathered four subjects utilizing a 250 Hz bioamp to monitor brain wave activity on the head and scalp including gastric activity, and used Zenodo's EGG dataset for the confirmation phase. We placed single-channel electrodes around the stomach to investigate gastric myoelectrical activity and extracted the EGG's power spectrum using a specific band-pass filter (0.03 - 0.07 Hz). We extracted the EGG's power spectrum and dominant frequency as our main features. Regarding brain electricity activities, we applied the FIR filter to obtain each brain wave's properties. We found that each subject had different responses during pre- and postprandial, both from primary and secondary resources. We found that the increase in EGG activity caused a change in EEG properties, particularly in the alpha band (8-12 Hz). Additionally, the EEG P3 site in the parietal lobe followed the power change rates of the EGG between 0 to 0.015 of relative power. We conclude that P3 and slow-wave gastric movement from EGG correspond to each other and reflect gut-brain axis pathways. However, future studies with larger samples must strengthen our findings according to the gut-brain axis pathways in the P3 site and EGG
Refining Diabetes Diagnosis Models: The Impact of SMOTE on SVM, Logistic Regression, and Naïve Bayes Wibowo, Arief; Masruriyah, Anis Fitri Nur; Rahmawati, Selly
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 1 (2025): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i1.596

Abstract

Accurate diabetes classification is a significant challenge in medical diagnostics, especially in imbalanced datasets. This study addresses this issue by introducing A New Modified Weighted SMOTE (ANMWS), integrated with Priority of Attribute by Expert Judgement (PAEJ) framework, to enhance the performance of machine learning models for imbalanced data. PAEJ categorizes attributes into three levels—high, medium and low priority—based on expert knowledge, while ANMWS applies weighted oversampling using these priority levels to generate synthetic data more representative of real-world cases. The proposed method was evaluated using three algorithms: Support Vector Machine (SVM), Logistic Regression, and Naïve Bayes. Results indicate that applying ANMWS algorithm with PAEJ framework significantly improved predictive performance, with AUC values increasing to 0.995 for SVM, 0.993 for Logistic Regression, and 0.990 for Naïve Bayes, compared to 0.980, 0.978, and 0.975, respectively, using standard SMOTE. Additionally, precision and recall for SVM improved by 5% and 7%, respectively. These findings demonstrate the critical role of ANMWS algorithm and PAEJ framework in addressing class imbalance, providing a reliable method for early diabetes diagnosis and informed clinical decision-making.
Collaborative Healthcare Data Management Framework using Parallel Computing and the Internet of Things D, Shamia; M, Ephin; Yalagi, Pratibha C. Kaladeep; Chowdhury , Rini; Prashant Kumar; R, Prabhu
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 1 (2025): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i1.611

Abstract

Healthcare data management has become a critical research area, primarily driven by the widespread adoption of personal health monitoring systems and applications. These systems generate an immense volume of data, necessitating efficient and reliable management solutions for lossless sharing. This article introduces a Collaborative Data Management Framework (CDMF) that leverages the combined strengths of parallel computing and federated learning. The proposed CDMF is designed to achieve two primary objectives: reducing computational complexity in data handling and ensuring high sharing accuracy, regardless of the data generation rate. The framework employs parallel computing to streamline the scheduling and processing of data acquired at various intervals. This approach minimizes processing delays by operating on a less complex scheduling algorithm, making it suitable for handling high-frequency data generation. Federated learning, on the other hand, plays a pivotal role in verifying data distribution and maintaining sharing accuracy. By enabling decentralized learning, federated learning ensures that data remains on local devices while sharing only the necessary model updates. This approach enhances privacy and security, a critical consideration in healthcare data management. It ensures that data distribution and sharing are verified based on appropriate requests while avoiding latency issues. By decentralizing the learning process, federated learning enhances privacy and security, as raw data does not leave the local systems. This cooperative interaction between parallel computing and federated learning operates in a cyclic manner, allowing the framework to adapt dynamically to increasing monitoring intervals and varying data rates. The performance of the CDMF is validated through improvements in two key metrics. First, the framework achieves a 15.08% enhancement in sharing accuracy, which is vital for maintaining data integrity and reliability during transfers. Second, it reduces computation complexity by 9.48%, even when handling maximum data rates. These results highlight the framework’s potential to revolutionize healthcare data management by addressing the dual challenges of scalability and accuracy.
De-identification of Protected Health Information in Clinical Document Images using Deep Learning and Pattern Matching Sriram, Ravichandra; Sathya S, Siva; Lourdumarie Sophie S
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 1 (2025): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i1.616

Abstract

Clinical documents that include lab results, discharge summaries, and radiology reports of patients are generally used by doctors for diagnosis and treatment. However, with the popularization of AI in healthcare, clinical documents are also widely used by researchers for disease diagnosis, prediction, and developing schemes for quality healthcare delivery. Though huge volumes of clinical documents are produced in various hospitals every day, they are not shared with researchers for study purposes due to the sensitive nature of health records. Before sharing these documents, they must be de-identified, or the protected health information (PHI) should be removed for the purpose of preserving the patient's privacy. If the documents are stored digitally, this PHI can be easily identified and removed, but finding and extracting PHI from old clinical documents that are scanned and stored as images or other formats is quite a daunting task for which machine learning models have to be trained with a large number of such images. This work introduces a novel combination of deep learning and pattern matching algorithms for the efficient de-identification of scanned clinical documents, distinguishing it from previous methods, which can primarily work only on text documents and not on scanned clinical documents or images. Thus, a comprehensive de-identification technique for automatically extracting protected health information (PHI) from scanned images of clinical documents is proposed. For experimental purposes, we created a synthetic dataset of 700 clinical document images obtained from various patients across multiple hospitals. The de-identification framework comprises two phases: (1) Training of YoloV3- Document Layout Analysis (Yolo V3-DLA) which is a Deep learning model to segment the various regions in the clinical document. (2) Identifying regions containing PHI through pattern-matching techniques and deleting or anonymizing the information in those regions. The proposed method was implemented to identify regions based on content structure, facilitating the de-identification of PHI regions and achieving an F1 score of 0.97. This system can be readily adapted to accommodate any form of clinical document.
Robust Fault Detection Of A Hybrid Control System Using Derivative Free Estimator And Reinforcement Learning Method Chatterjee, Sayanti; Deka, Bhupesh; Debnath, Dipanwita; Tripathy, Sasmita; Agarwalla, Bindu
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 1 (2025): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i1.644

Abstract

Fault detection in hybrid control systems (HCS) poses significant challenges due to dynamic variations in system dynamics caused by event-based inputs and the existence of unknown large process noise. A novel scheme for optimized robust fault detection of HCS has been proposed and projected here that can effectively handle dynamic system changes and process noise along with the fault detection while achieving high accuracy and reliability. The challenge with the HCS is the presence of a large process noises due to changing of state equations drastically with dynamical input making the fault detection a complex task. The derivative-free estimator minimizes process noise and provides reliable state estimation, while the Markov Decision Process (MDP) framework is employed to optimize fault detection. MDP has been chosen here due to its mathematical introspection for dynamic system's decision-making process when the results are random or under the control of a decision maker. The data generated by the derivative-free estimator is used to train this deep learning model. Simulation studies were conducted to evaluate the scheme’s performance, and additional tests for convergence, optimization, and robustness were performed using MDP infused with adaptive estimators. The efficacy of the proposed estimators has been confirmed on a benchmark problems, namely the liquid level control system for an chemical stirred tank reactor (CSTR) model. Simulation studies has been employed to prove the efficacy of the proposed method. The proposed method achieved 98.6% fault detection accuracy and a 12% mean error reduction compared to existing techniques. It demonstrated robustness under varying noise levels, dynamic conditions and presence of external disturbances . The results confirm the method's effectiveness for robust and optimized fault detection in HCS, offering a scalable, accurate, and noise-resilient solution for real-world industrial systems.
Automated Detection of Porcine Gelatin Using Deep Learning-Based E-Nose to Support Halal Authentication Mahmudah, Kunti R.; Biddinika, Muhammad K.; Hakika, Dhias C.; Tresna, Wildan P.; Sugiarto, Iyon T.; Syafarina, Inna
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 1 (2025): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i1.654

Abstract

Authenticating gelatin sources is essential for consumers, particularly those with dietary restrictions or religious concerns regarding pork-derived ingredients. Porcine gelatin, widely used in food and pharmaceutical products, poses considerable challenges for authentication due to its prevalence and the difficulty of detecting it, especially in processed products. In this study, we developed and evaluated an integrated electronic nose (e-nose) system with a Recurrent Neural Network (RNN) to detect and classify gelatin type based on their sources. The e-nose system utilized an array of gas sensors to capture the unique volatile organic compounds (VOCs) associated with each gelatin type, which was subsequently classified by the RNN. The classification performance of the integrated 7-module e-nose system showed promising results based on time points after sample preparation, with accuracy, sensitivity, and AUC of 96.3%, 96.6%, and 98.2% at the 0-hour point, respectively, rising to 99.1% for all three metrics at 2-hour point. The sensitivity of the system also showed an increase over time for single gelatin samples, from 100%, 97.8%, and 91.9% to 98.6%, 99.3%, and 99.3% for pig-derived, cow-derived, and fish gelatin, respectively. For mixed gelatin samples, the system maintained high accuracy, sensitivity, and AUC at 98.2%, 97.9%, and 98.1%, respectively. In conclusion, the integrated e-nose system demonstrates the potential for robust performance in gelatin authentication, paving the way for more efficient and reliable methods of halal food authentication.
Swarm Intelligence-Based Functional Link Fuzzy Neural Estimator for Software Development Effort Estimation Benala, Tirimula Rao; Kaushik, Anupama; Dehuri, Satchidananda
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 2 (2025): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i2.492

Abstract

Accurate Software Development Effort Estimation (SDEE) is pivotal for effective project management, significantly impacting resource allocation and the overall success of software projects. This paper introduces the Swarm Intelligence-Based Functional Link Fuzzy Neural Estimator (SFNE), a novel computational intelligence model designed to enhance estimation accuracy by integrating multiple advanced methodologies. The SFNE framework employs the QUICK algorithm for dataset optimization, effectively minimizing noise and redundancy. A Functional Link Artificial Neural Network (FLANN) captures complex nonlinear relationships within the data, while Interval Type-2 Fuzzy Logic Systems (IT2FLS) address inherent data uncertainties. Additionally, Particle Swarm Optimization (PSO) is applied to fine-tune model parameters, improving prediction precision. Empirical evaluations were conducted using six benchmark datasets from the PROMISE repository. The results demonstrate that the SFNE model significantly outperforms existing models across key metrics, including Mean Magnitude of Relative Error (MMRE), Median Magnitude of Relative Error (MdMRE), and Prediction at 0.25 (PRED(0.25)). Notably, SFNE achieved a predictive accuracy of 99.983% on the DesharnaisL3 dataset and an MMRE of 2.87×10⁻⁵ on the DesharnaisL1 dataset. These findings underscore the robustness and adaptability of SFNE in addressing the limitations of traditional SDEE methods, particularly in managing data scarcity and uncertainty. The proposed SFNE model establishes a new benchmark for SDEE accuracy and demonstrates substantial potential for practical application in real-world software engineering projects. Future research will explore integrating additional computational intelligence techniques, such as deep learning and reinforcement learning, and developing automated tools to advance SDEE practices further. These advancements contribute to more reliable and efficient software project management, facilitating real-time effort estimation and informed decision-making in the software industry.

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