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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 317 Documents
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Optimized Recurrent Neural Network Based on Improved Bacterial Colony Optimization for Predicting Osteoporosis Diseases B, Sivasakthi; K, Preetha; D, Selvanayagi
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 2 (2026): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i2.1410

Abstract

Osteoporosis is a silent disease before significant fragility fractures despite its high prevalence, and its screening rate is low. In predictive healthcare analytics, the Elman recurrent neural network (ERNN) has been widely used as a learning technique. Traditional learning algorithms have some limitations, such as slow convergence rates and local minima that prevent gradient descent from finding the global minimum of the error function. The main goal is to precisely estimate each individual's risk of developing osteoporosis. These forecasts are essential for prompt diagnosis and treatment, which have a significant influence on patient outcomes. Hence, the present research focuses on making a more efficient prediction method based on an optimized Elman recurrent neural network (ERNN) for predicting osteoporosis diseases. An optimized ERNN method, IBCO-ERNN, improved bacterial colony optimization (IBCO) by optimizing the ERNN weights and biases. The IBCO approach uses an iterative local search (ILS) algorithm to enhance convergence rate and avoid the local optima problem of conventional BCO. Subsequently, the IBCO is used to optimize the ERNN's weights and biases, thereby improving convergence speed and detection rate. The effectiveness of IBCO-ERNN is evaluated using four different types of osteoporosis datasets: Femoral neck, Lumbar spine, Femoral and Spine, and BMD datasets. The proposed IBCO-ERNN produced higher accuracy at 95.61%, 96.26%, 97.26%, and 97.54 % for the Femoral neck, Lumbar spine, Femoral, and Spine datasets, respectively. The experimental findings demonstrated that, compared with other predictors, the proposed IBCO-ERNN achieved respectable accuracy and rapid convergence.
Impact of Optimizer Algorithm on NasNetMobile Model for Eight-class Retinal Disease Classification from OCT Images Selvarajan, Madhumithaa; M, Masoodhu Banu N.
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 2 (2026): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i2.1464

Abstract

Artificial intelligence (AI) is an emerging technology that plays a vital role in various fields, including the medical field. Ophthalmology is the earliest field to adopt AI for diagnosing several retinal diseases. Many imaging techniques are available, but Optical Coherence Tomography (OCT) is particularly useful for early-stage diagnosis. OCT is a non-invasive imaging method that offers high-resolution visualization of the retinal structure, aiding the ophthalmologist in differentiating between normal and abnormal retina. Automated OCT-based retinal disease classification using deep learning (DL) is important for early disease detection. Most DL models achieved high performance, but the influence of the optimizer on model behaviour, convergence, and explainability remains a challenge. To bridge the gap, this study evaluates the performance and convergence of five optimizers, such as RMSprop, AdamW, Adam, Nadam, and SGD, on the NasNetMobile model. The model was trained on the OCT-8 dataset, which comprises seven diseased retinal classes and one normal class of Optical Coherence Tomography (OCT) images. The seven diseases are Age-related Macular Degeneration (AMD), choroidal neovascularization (CNV), Central Serous retinopathy (CSR), diabetic macular edema (DME), diabetic retinopathy (DR), DRUSEN, and Macular Hole (MH). The study also analyzes convergence behaviour and explainability through early stopping regularization technique and GradCAM XAI, respectively. The model achieved 71%, 93%, 96%, 97%, and 97% of accuracy, respectively. Compared with other optimizers, the SGD optimizer achieved high accuracy in 22 epochs, which indicates better generalization. GradCAM XAI highlights the disease-relevant region across different retinal diseases. This framework emphasizes the significance of selecting an appropriate optimizer for robust retinal disease classification using a DL model trained on OCT images
MK–TripNet: A Deep Learning Framework for Real-Time Multi-Class Lung Sound Classification Erini, Widya Surya; Thomas, Gracia Putri; Badia, Giulia Salzano; Rahadian, Arief; Raharjo, Sofyan Budi; Wulandari, Sari Ayu
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 2 (2026): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i2.1403

Abstract

Respiratory diseases such as asthma, pneumonia, and Chronic Obstructive Pulmonary Disease (COPD) remain major global health challenges, particularly in resource-limited settings where access to pulmonary specialists and early diagnostic tools is limited. Automatic lung sound classifications have emerged as a promising non-invasive screening approach; however, existing methods often rely on single-scale feature extraction, conventional loss functions, and offline analysis, which limit their discriminative capability and real-time applicability. The aim of this study is to develop and evaluate a deep learning framework for real-time multi-class lung sound classifications that improves discriminative representation and temporal sensitivity. To address limitations, this study proposes MK-TripNet, a novel deep learning architecture designed to integrate multi-scale feature extraction, discriminative embedding learning, and real-time inference within a unified framework. The main contribution of this work is the unified integration of a Multi-Kernel convolutional architecture, Triplet Loss-based embedding learning, and Sliding Window segmentation within a single end-to-end framework, enabling accurate segment-level lung sound classifications in real-time scenarios. Unlike prior approaches, the proposed method simultaneously captures fine-grained temporal patterns and broader spectral characteristics while explicitly maximizing inter-class separability in the embedding space. The proposed model was evaluated using a newly constructed dataset comprising 1,409 lung sound segments obtained from primary digital stethoscope recordings and publicly available respiratory sound databases. Experimental results demonstrate that MK-TripNet consistently outperforms several strong baseline models, including CNN-BiGRU, CNN-BiGRU-UMAP, and VGGish-Triplet, achieving an accuracy of 89.1%, an F1-score of 0.89, and a recall of 0.88. Ablation studies further confirm that the combined use of Multi-Kernel convolution, Triplet Loss, and Sliding Window segmentation yields the most robust and generalizable performances. These findings highlight the clinical potential of MK-TripNet for real-time digital auscultation and point-of-care respiratory screening, particularly in resource-limited and telemedicine settings.
Multipoint Wrist Pulse Acquisition and Analysis by Combining HRV with Morphological Timing Features for Quantitative Identification of Ayurvedic Doshas Patel, Devendra; Patel, Mitul
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 2 (2026): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i2.1474

Abstract

Nadi Pariksha, the traditional Ayurvedic method of wrist pulse examination, posits that three adjacent radial artery locations corresponding to Vata, Pitta, and Kapha (V-P-K) reflect distinct physiological states. While recent sensor-based systems have attempted to digitize wrist pulse acquisition, many have emphasized hardware design or classification performance without rigorously validating physiological differences between pulse sites within the same individual. This study presents a quantitative evaluation of the multi-point principle of Nadi Pariksha using synchronized multi-site photoplethysmography (PPG) combined with integrated cardiovascular signal analysis. Pulse waveforms were simultaneously acquired from 39 participants, including 32 healthy individuals and 7 clinically characterized subjects, at the three classical radial artery locations. Morphological timing features and time-domain heart rate variability (HRV) metrics were extracted to characterize vascular dynamics and autonomic regulation. Within-subject statistical analysis demonstrated significant spatial differentiation across the pulse sites. Crest time decreased from 0.204 s at the Kapha site to 0.175 s at the Vata site (14.2% reduction), while systolic width decreased from 0.140 s to 0.109 s (22.1% reduction) (p ≤ 0.004). Non-parametric analysis confirmed significant differences in crest time (H = 9.15, p = 0.010), pulse width (H = 8.43, p = 0.015), systolic amplitude, systolic area, and HRV variability (SDNN: H = 6.33, p = 0.041), with moderate-to-large effect sizes (η² = 0.12–0.20). Clinically characterized cases exhibited deviations from this baseline pattern, including a 62% reduction in crest time gradient and a 72% increase in stiffness index in diabetes, and a 55% reduction in gradient with a 25% decrease in HRV during acute infection. Given the limited clinical sample (n = 7), these findings are interpreted as preliminary. Overall, the results provide quantitative within-subject evidence supporting the physiological distinctiveness of the V-P-K pulse locations and contribute toward the development of standardized, sensor-based Nadi Pariksha
Design and Statistical Evaluation of an AI-Enabled IoT-Based Non-Invasive Biosensing System for Diabetes Risk Screening Kamble, Prachi C.; Ragha, Lakshamappa; Pingle, Yogesh
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 2 (2026): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i2.1541

Abstract

Early identification of diabetes risk remains a significant challenge due to the invasive nature, recurring cost, and limited accessibility of conventional biochemical diagnostic tests. These limitations restrict continuous monitoring and hinder large-scale population screening, particularly in remote and resource-limited settings. The aim of this study is to design and statistically evaluate an AI-enabled IoT-based non-invasive biosensing system for diabetes risk screening, focusing on system-level engineering design, data integration, and performance validation rather than clinical diagnosis. In this study, the term “non-invasive” refers exclusively to externally measurable surface-level physiological and breath-based signals that do not require skin penetration, blood sampling, or subdermal sensor implantation. The main contributions of this work include the development of a wearable IoT-based non-invasive biosensing framework, integration of multi-modal physiological and breath-based biomarkers for risk assessment, implementation of an ensemble machine learning model for diabetes risk classification, and comprehensive statistical validation using agreement, reliability, and calibration metrics. The proposed DiaAssist system acquires physiological parameters such as heart rate, blood pressure, oxygen saturation, body temperature, physical activity indicators, and breath volatile organic compound acetone through a wearable IoT platform with edge-level preprocessing. Fused physiological and demographic features are processed using an ensemble learning framework to generate individualized diabetes risk scores. Performance evaluation was conducted on a single-center observational dataset comprising 625 records using paired statistical tests, agreement analysis, and calibration assessment. The optimized model achieved an accuracy of 99.7%, an area under the receiver operating characteristic curve of 1.000, a Cohen’s Kappa coefficient of 0.993, a Matthews correlation coefficient of 0.993, and a Brier score of 0.045, demonstrating strong classification reliability and probabilistic calibration. The results confirm that combining IoT-based non-invasive biosensing with ensemble machine learning enables accurate and reliable screening for diabetes risk. The proposed system provides a scalable, cost-effective, and engineering-oriented solution suitable for remote monitoring and preventive healthcare applications
A Hybrid Deep Ensemble Model for Precise Liver and Tumor Segmentation Using U-Net and W-Net Architectures B. Sravani; M. sunil Kumar
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 2 (2026): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i2.1089

Abstract

The identification of the liver with the hepatic tumors on the computed tomography (CT) scans is a major compulsion to the earliest diagnosis, treatment planning, and surgery in the case of hepatocellular carcinoma. However, automated segmentation is not an easy job due to the non-homogeneous appearance of tumors, blurry boundaries, small size of annotated datasets, and high inter-slice variability. Existing single deep learning models are known to suffer from prediction variance and low generalization in complex clinical conditions. The primary goal of the study is to develop an effective, highly accurate segmentation model that improves the accuracy, consistency, and explanability of liver and tumor borders in CT images. In this paper, an original hybrid deep ensemble model is proposed that leverages the advantages of U-Net and W-Net. This is the primary contribution; one can combine the strong spatial localization ability of U-Net and the reconstruction-driven unsupervised learning ability of W-Net in minimizing the variance and maximizing the generalization. In addition, soft probability fusion, uncertainty modelling, and entropy-based confidence estimation are also introduced to improve reliability and clinical interpretation. The preprocessing of CT images is performed mathematically by normalizing and resizing to 256x256. U-Net and W-Net are trained separately using the pixel-wise probability maps, which are soft-averaged and thresholded. Benchmark liver CT datasets are tested with the ensemble using the Dice coefficient, accuracy, precision, recall, F1-score, Intersection over Union (IoU), ROC-AUC, and statistical significance tests. The results of the experiment show that the suggested ensemble performs better with an accuracy of 95.4, a precision of 94.3, a recall of 93.9, an F1-score of 94.1, IoU of 89.8, and an average ROC-AUC of 0.9615 than the models of the U-Net and W-Net, which differ in a huge number. Statistical confirmation that the improvements are relevant (p < 0.01) will be provided. In summary, the proposed deep ensemble segmentation can accurately, reliably, and effectively segment the liver and tumor, showing strong potential for clinical use and subsequent extension to multi-organ and multi-modal medical imaging.
Ensemble Voting Method to Enhance the Performance of a Dental Caries Detection System using Convolutional Neural Network Putri Rizkiah; Maulisa Oktiana; Khairun Saddami; Maya Fitria; Fitri Arnia; Hubbul Walidainy; Yunida Yunida
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 2 (2026): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i2.1343

Abstract

Individual classification models for caries detection still face significant challenges, including limited accuracy and unstable predictions, which can hinder diagnosis, delay clinical decisions, and increase the risks associated with patient care. To overcome these limitations, this study proposes an ensemble voting method that combines five deep learning models, such as ResNet-152, MobileNetV2, InceptionV3, NASNetMobile, and EfficientNet-B5. This approach aims to enhance the accuracy and stability of caries detection by leveraging the complementary strengths of the individual models while mitigating their weaknesses. Each model was trained and tested on the same dataset of dental images, categorized into caries and regular classes. Their predictions were aggregated using hard and soft voting techniques. The ensemble's performance was evaluated using accuracy, precision, recall, and F1-score. The ensemble voting demonstrates a notable improvement in classification performance over individual models. Hard and soft voting have excellent classification performance and consistently outperform the best individual models. The accuracy increased from EfficientNetB5 0.8485 to 0.8864 and 0.8712, representing increases of 4.46% and 2.68%, respectively. The precision increased from MobileNetV2 0.8182 to 0.8493 and 0.8551, representing increases of 3.81% and 4.52%. For recall, EfficientNetB5 ranked highest among individual models with a score of 0.9242. Hard voting increased 1.64% to 0.9394, and soft voting decreased slightly by 3.28% to 0.8939. The F1 score of EfficientNetB5 is 0.8592. Hard and soft voting increased 3.83% and 1.73% to 0.8921 and 0.8741. The proposed ensemble improves the F1-score by 3.83 percentage points compared to the best individual model. The ensemble voting method effectively leverages the complementary strengths of each deep learning model to improve the stability and accuracy of fast, reliable dental caries early detection prediction.
HST-Net: Hierarchical Spectrum-Tokenization with Progressive Refinement for Cardiac MRI Segmentation Naga Chandrika Gogulamudi; Shamia D; V Kavithamani; Amitha Ida Chandran; K Venu; Kunchanapalli Rama Krishna
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 2 (2026): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i2.1485

Abstract

The accurate segmentation of cardiac structures from Magnetic Resonance Imaging (MRI) plays a vital role in quantitative ventricular assessment, functional analysis, and the clinical diagnosis of cardiovascular diseases. Precise delineation of cardiac components, such as the left ventricle, right ventricle, and myocardial wall, is essential for evaluating cardiac morphology and function. In recent years, transformer-based architectures, including TransUNet and Swin-UNet, have demonstrated strong capabilities in modeling long-range dependencies and capturing global contextual information. However, despite these advantages, they often struggle to preserve smooth anatomical geometry and achieve high-precision boundary delineation, particularly in the presence of large shape deformations and significant inter-subject variability commonly observed in cardiac MRI data. To overcome these limitations, a Hierarchical Spectrum-Tokenization Network (HST-Net) is proposed. The core idea of HST-Net is to represent cardiac anatomy at multiple levels of granularity, enabling a more robust structural understanding across varying spatial scales. The proposed architecture incorporates a novel approach called Spectrum Tokenization. This approach divides the latent representations into two parts, one containing low-frequency global tokens that capture context information, and another containing high-frequency boundary-aware tokens that capture the contours. By progressively enhancing boundary details, PSR significantly improves contour accuracy, especially for complex and thin structures. Experimental evaluations conducted on a cardiac MRI dataset demonstrate the effectiveness of the proposed approach. HST-Net achieves an average Dice coefficient of 91.6% and a pixel-wise segmentation accuracy of 94.8%. Compared to nnU-Net and Swin-UNet, it shows consistent performance gains, yielding improvements of 2.1–3.4% in Dice score and 1.9–2.6% in segmentation accuracy across different cardiac structures.
Topographic EEG Power Mapping and Machine Learning-Based Seizure Detection Using Real and Synthetic SSIM-MSE Features Ghansyamkumar Rathod; Hardik Modi
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 2 (2026): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i2.1515

Abstract

The neural activities of the brain can show abnormalities and misfiring due to seizures. The ionic activity of the brain can be converted into electrical activity, which can be observed on the human scalp using electroencephalography (EEG). The spatial patterns of brain activity can be analyzed using topographic maps generated from EEG signals. In this study, topographic power maps with seizure and normal states of the brain were generated, and the features of the image were named structural similarity index (SSIM) and mean square error (MSE). The data utilized in this study were obtained from a publicly available dataset from the Children's Hospital Boston (CHB) in association with the Massachusetts Institute of Technology (MIT). Topographic images of the bipolar montages showed a clear difference between seizure and non-seizure brain states, along with the affected areas of the brain regions. Synthetic Features were generated to mimic real data for training the ML models. The major tested machine learning models, gradient boosting, decision tree, and k-nearest neighbors, provided the highest accuracy of 99.34% and an F-score of 0.996 when evaluated using real and generated data. The generalizability of the model was confirmed using 5-fold cross-validation. Overall, this study provides an EEG power-based topographic power image generation along with reliable feature extraction to train ML models for detecting epileptic seizures. The proposed methodology not only enhances the interpretability of EEG spatial patterns but also offers potential for integration into biomedical wearable devices for real-time seizure monitoring and intervention, along with the identification of the type of seizure.
Dynamic Uncertainty-Aware Adaptive Subspace Fusion Network for Robust Multimodal Medical Image Classification Krishnakumar B; Thanga Parvathi; K. Nithya; M. Pyingkodi; Kunchanapalli Rama Krishna; Jeevitha R
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 2 (2026): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i2.1529

Abstract

Multimodal medical image classification leverages complementary information from multiple imaging modalities to improve diagnostic accuracy and clinical decision-making. However, most existing multimodal fusion approaches rely on deterministic low-rank constraints and assume equal importance across all modalities. Such assumptions significantly limit flexibility, robustness, and interpretability, particularly in real-world clinical scenarios where modality data may be noisy, incomplete, or partially missing. To address these challenges, this work proposes a Dynamic Uncertainty-Aware Adaptive Subspace Fusion Network (DUA-SFNet) for robust multimodal medical image classification. The core of the proposed framework is a rank-learning adaptive-rank tensor decomposition module that dynamically adjusts subspace dimensionality according to the intrinsic complexity of the input data. This adaptive mechanism effectively reduces feature redundancy while preserving the highly discriminative information essential for accurate classification. In addition, DUA-SFNet incorporates a modality uncertainty estimation scheme to explicitly quantify the reliability and trustworthiness of each modality. By assigning uncertainty-aware weights during the fusion process, the framework can suppress unreliable or noisy modalities while emphasizing more informative ones, thereby improving resilience under adverse data conditions. Furthermore, a hierarchical adaptive attention strategy is employed to jointly model intra-subspace feature interactions and inter-modality dependencies. This design enhances feature representation capability while offering improved clinical interpretability by revealing how different modalities and subspaces contribute to the final decision. Extensive experiments conducted on multiple public and self-organized multimodal medical image datasets demonstrate that DUA-SFNet consistently outperforms state-of-the-art methods, achieving classification accuracy improvements of 3.8–6.2% and F1-score gains of 4.1–7.5%. Overall, DUA-SFNet provides an interpretable, uncertainty-aware, and adaptive solution for next-generation multimodal medical image analysis.

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