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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 368 Documents
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QoS-Aware Secure Federated Intelligence for Biomedical Electronics and Medical Informatics in Wireless Health Monitoring Networks Kaliappan A; Senthil Kumar P; Tamilselvi K; Ravi A T; Santhoshkumar S P; Asha V
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 4 (2026): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i4.1566

Abstract

Wireless Health Monitoring Networks (WHMNs) that utilize Biomedical Electronics (BMEs) are rapidly changing how medical information is gathered and used in contemporary Medical Informatics through providing ongoing, real-time, and remote assessment and monitoring of essential physiological parameters; including electrocardiogram (ECG), body temperature, blood oxygen saturation, and heart rate. A number of constraints can limit the practical implementation of WHMNs including: stringent Quality of Service (QoS) requirements; limited energy availability; network scalability limitations; and significant risks associated with the transfer of sensitive medical information via open wireless networks. While conventional centralized routing and learning based approaches provide the ability to route packets and learn network behavior they require high levels of communication, make the network vulnerable to cyber threats, and do little to protect the privacy of patients' data. This paper describes a QoS aware secure federated intelligence framework designed specifically for BME driven WHMNs. The proposed framework will include: (1) Decentralized Federated Learning to enable distributed intelligence among edge nodes; (2) Trust based QoS Aware Routing to enable reliable and energy efficient communication throughout the network; and (3) a Lightweight Blockchain-Assisted Mechanism to provide authentication, integrity, and tamper resistance to the network. In contrast to traditional federated learning where edge node models are aggregated into a central server model for processing, the proposed framework enables edge nodes to train their own local models on their own data and to share only encrypted model updates rather than raw biomedical data; thereby reducing communication costs and increasing patient data privacy protection. Simulation results show that the proposed framework outperform other state of the art WHMN protocols in terms of Packet Delivery Ratio (PDR); End-To-End Delay (E2ED); Residual Energy per Node (REPN); and Overall Network Lifetime (ONL). Additionally, the security analysis provided in this paper demonstrates the proposed framework's robustness against Insider Attacks, Data Manipulation Attacks, and Unauthorized Access Attacks. These findings indicate the strong feasibility of the proposed framework for next generation Intelligent Healthcare and Medical Informatics Applications.
RadDenseNet: An Explainable AI Model for Detection of Hepatic Steatosis from Abdominal CT Images Rahul LR; Satya Prasad Gaddamedi
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 4 (2026): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i4.1688

Abstract

Hepatic steatosis affects approximately 30.2% of the global population and, if undetected, can silently progress to non-alcoholic steatohepatitis, cirrhosis, and hepatocellular carcinoma. Manual CT interpretation for fatty liver assessment is time-consuming and prone to inter-observer variability, motivating research into automated screening tools. We propose RadDenseNet, a DenseNet121 model initialized with RadImageNet weights pretrained on 1.35 million medical images spanning CT, MRI, and ultrasound, and evaluate it as a research prototype. The first 397 of 427 backbone layers were frozen, and the remaining 30 layers were fine-tuned alongside a classification head comprising global average pooling, a dense layer of 256 units, batch normalization, and dropout of 0.5. The model was trained on the Kaggle Liver CT Image Dataset - 3,557 abdominal CT images across Healthy and Hepatic Steatosis classes - using a stratified 70/20/10 split with 5-fold cross-validation. RadDenseNet was compared against six baseline models spanning ImageNet and RadImageNet pretraining across ResNet50, InceptionV3, and DenseNet121 architectures. The decision threshold was optimized on the validation set, yielding τ = 0.48. On the held-out test set, RadDenseNet achieved 96.35% accuracy, AUC of 0.9966, sensitivity of 0.9538, specificity of 0.9752, precision of 0.9789, and F1-score of 0.9662, with 5-fold cross-validation confirming stable performance (mean accuracy 96.46% ± 0.68%, mean AUC 0.9962 ± 0.0014). RadImageNet pretraining consistently outperformed ImageNet pretraining across all architectures, with accuracy improvements of 2.81 to 5.90 percentage points. Grad-CAM analysis covering all four prediction categories revealed that false positives arose from extra-hepatic attention and false negatives from weak parenchymal activation - clinically meaningful failure modes. External validation on multi-center datasets, extension to multi-class steatosis grading, and liver ROI segmentation as preprocessing are recommended as future directions
Machine Learning Models Using Gut Microbiome Data for Obesity-Relevant Classification, Dietary Response Prediction, and Personalised Nutrition: A PRISMA-Guided Systematic Review Poppy Fransisca Amelia; Fitra Adi Prayogo; Isy Royhanaty; Heni Wijayanti; Egy Sunanda Putra
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 4 (2026): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i4.1722

Abstract

Obesity is a major non-communicable disease burden, yet population-level dietary guidance does not capture interindividual metabolic variability related to gut microbiome composition. Machine learning (ML) may support microbiome-informed precision nutrition, but evidence remains heterogeneous across classification, response-prediction, and intervention-adjacent studies. This systematic review evaluated ML models that use gut microbiome data for obesity-relevant classification, dietary response prediction, and personalised nutrition outcomes. A PRISMA 2020-guided search of PubMed/MEDLINE, Scopus, Web of Science, and IEEE Xplore identified English-language peer-reviewed articles published from January 2015 to February 2026. Eligible studies included adult cohorts with overweight/obesity participants, BMI-stratified microbiome data, or dietary-response outcomes relevant to obesity-related precision nutrition. Risk of bias was assessed using PROBAST, Newcastle-Ottawa Scale, and RoB 2.0 as appropriate. Twelve studies met the inclusion criteria and were assigned to dominant evidence families: seven classification or biomarker-modelling studies, two dietary or postprandial-response prediction studies, one small, blinded, personalised-diet validation within a PPGR cohort, one prospective, controlled-feeding cohort, and one longitudinal weight-loss prediction study. XGBoost-based classifiers showed the strongest comparative support for static obesity/BMI classification (peak internal AUC 0.90), with SHAP improving interpretability in the best-performing study. Random Forest/MetAML performance was variable, ranging from cross-study AUCs around 0.60 to internal or robust-signature AUCs of 0.83-0.85. The main translational finding was a generalisation envelope from a peak internal AUC of 0.90 to approximately 0.60 across cross-study/meta-analytic benchmarks. Specificity was not reported in any classification-capable study, and calibration metrics were absent across all included studies. Microbiome-based ML shows early promise for precision-nutrition CDSS, but clinical translation requires external validation, compositional-data-aware preprocessing, complete metric reporting, and prospective dietary trials
A Multilevel Feature-based Emotion Classification Technique using Electroencephalogram Signals Kabita Devi; Bhabesh Deka; Debarun Chakraborty
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 4 (2026): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i4.1774

Abstract

Emotion dysfunction can be an indication of mental health conditions like depression, stress, anxiety, etc. Therefore, appropriate recognition of human emotion is crucial for early monitoring of mental well-being. This study aims to develop a robust multilevel feature concatenation framework that effectively integrates handcrafted and deep learning (DL) features to improve electroencephalogram (EEG)-based emotion classification. The proposed framework is evaluated on two publicly available benchmark EEG datasets: DEAP and DREAMER. Initially, handcrafted features extracted from the statistical, time, frequency, and nonlinear domains are categorized into low-level features (LLF) and mid-level features (MLF). To select the most relevant handcrafted features, statistical analysis, minimum Redundancy Maximum Relevance (mRMR) and the Pearson correlation coefficient (PCC) are employed. In parallel, a two-dimensional convolutional neural network (2D-CNN) is used to learn hierarchical deep features, which are grouped into LLF, MLF, and high-level features (HLF). The handcrafted LLF and MLF are then concatenated with the corresponding DL-based LLF, MLF, and HLF to construct a discriminative multilevel feature representation. The resulting feature vector is classified using Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and XGBoost classifiers under a 10-fold and leave-one-subject-out (LOSO) cross-validation protocol. Performance is evaluated using accuracy, precision, recall, F1-score, and balanced accuracy. Experimental results demonstrate that the proposed framework consistently outperforms existing state-of-the-art methods. Among the evaluated classifiers, SVM achieves the highest performance, yielding accuracies of 93.07% and 98.70% for 4-class and 2-class emotion classification, respectively, on the DEAP dataset, and 93.85% and 98.78% for 4-class and 2-class emotion classification, on the DREAMER dataset. These results demonstrate that concatenation of handcrafted with DL-based features at different abstraction level significantly enhances feature discriminability and classification robustness. The proposed framework provides an effective solution for EEG-based emotion recognition, with promising applications in affective computing, human–computer interaction, and intelligent mental health monitoring.
Embedded Colour- and Electrodermal-Based Pressure Injury Staging and Objective Pain Assessment System Powered by a Proton Battery Wahmisari Priharti; Husneni Mukhtar; Dien Rahmawati; Nur Hidayah Ahmad
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 4 (2026): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i4.1828

Abstract

Pressure injury is a serious health concern among the elderly and individuals with restricted mobility and is often associated with pain that reduces quality of life. Conventional assessment relies on clinician visual inspection and subjective pain scales, which are difficult to apply to patients with limited communication abilities. Therefore, early identification, accurate stage classification and objective pain assessment are essential for effective clinical intervention. This study develops an embedded pressure injury stage classification and pain assessment system integrating a colour sensor and an electrodermal activity (EDA) sensor with a Rule-Based Decision Tree to classify pressure injuries into four clinical stages, powered by a six-cell 1.5 V proton battery pack as an alternative to conventional lithium-based power sources. The system achieved an overall accuracy, precision, recall, and F1-score of 0.88, 0.89, 0.88, and 0.88, respectively. Skin Conductance Level (SCL) increased progressively from Stage 1 to Stage 3 and decreased at Stage 4, indicating a preliminary physiological pattern associated with injury severity. The battery pack maintained a stable output voltage of 9.1 V at an average current consumption of 279.9 mA, demonstrating its feasibility as an alternative power source. Because the thresholds were derived from 70 annotated Medetec images and validated on only two subjects with four injury sites (40 region-level measurements), the reported metrics should be interpreted as preliminary proof-of-concept rather than definitive clinical validation, and larger studies are needed for clinical confirmation.
Med-ConvNeXT: A Multi-Class Edge-Compatible Image Classification Model for Respiratory Disease Parvez Belim; Nirav Bhatt; Amit Lathigara; Vikas Kumar Sabata
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 4 (2026): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i4.1840

Abstract

Respiratory abnormality detection in chest X-ray images can be assisted and automated via machine learning models. Many of those models are very effective in detecting abnormalities, but they require large computational and memory resources. Running these models on edge devices often leads to delays in inference or crashes, as most of the devices have memory constraints. Therefore, a more edge-aware image classification model called Med-ConvNeXT is developed by keeping both memory and performance in mind. The proposed model introduces a kernel reparameterization technique called receptive kernel fusion, which combines useful information from kernels ( ) to calculate a receptive kernel. The kernel calculated by the above method can extract both lower- and higher-level features without increasing FLOPs or the network's parameter count. To further reduce the number of parameters and FLOPs, a novel channel pruning method, called Memory-based Channel Pruning, is introduced. This method compares the classification performance of different kernels and ranks them to eliminate the least important kernels. This reduced the total channel size from 96 to 64 in the first stage. Combining the proposed kernel reparameterization and channel pruning technique reduced the parameter count of the ConvNeXT model by up to 54.7% while achieving a classification accuracy of 96.53% for X-ray images. The proposed model achieved 93.7% sensitivity and 95.9% specificity with a parameter count of 12.96 million. The proposed model was implemented on Raspberry Pi 5 hardware and got an inference latency of 985ms using a 118 MB memory footprint. This performance is achieved with an input image dimension of a 512×512 X-ray image. The proposed model balances both image classification performance and edge implementation efficiency. The proposed model can achieve better performance on edge devices without the need for expensive server and cloud infrastructure
Comparative Analysis of Optuna-Optimized 1D and 2D CNNs for Protein Secondary Structure Prediction Annisa Rizqiana; Triando Hamonangan Saragih; Muhammad Itqan Mazdadi; Irwan Budiman; Adryan Maulana Saputra
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 4 (2026): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i4.1878

Abstract

Protein secondary structure prediction (PSSP) is a fundamental task in bioinformatics, yet traditional experimental methods remain costly and time-consuming. While Convolutional Neural Networks (CNNs) offer efficient computational alternatives, systematic comparisons between 1D and 2D CNN topologies remain limited. Furthermore, conventional hyperparameter tuning in existing models relies on manual trial-and-error, introducing human bias and trapping networks in suboptimal performance thresholds. This study aims to evaluate the effectiveness of 1D and 2D CNN architectures for PSSP and systematically investigate the impact of automated hyperparameter optimization on both model performance and training efficiency. The proposed framework integrates standalone 1D and 2D CNN topologies with an automated hyperparameter optimization pipeline using Optuna based on the Tree-structured Parzen Estimator. Models are trained on the benchmark CullPDB dataset and rigorously evaluated via cross-dataset testing on the independent, non-redundant CB513 benchmark. The primary contributions include a standalone comparative analysis between 1D and 2D CNN topologies, the elimination of human bias via automated tuning, and a comprehensive feature efficiency analysis highlighting the trade-offs between computational representation and predictive accuracy. Experimental results show that all configurations achieve high overall performance with accuracies exceeding 81% and AUC values above 94%. Specifically, the CNN 1D + Optuna configuration yields the highest precision of 0.4409 and an F1-score of 0.4454, effectively capturing local sequential motifs. Conversely, the CNN 2D + Optuna configuration excels in extracting spatial inter-residue relationships, delivering the highest AUC of 0.9493 and the lowest cross-entropy loss of 0.0114. Additionally, Optuna dramatically accelerates training efficiency, reducing convergence iterations by up to 66% (from epoch 24 down to 8 for the 2D model). In conclusion, automated optimization eliminates manual bias and enhances training efficiency, revealing that the choice between 1D and 2D topologies should be strategically guided by the precise needs of downstream biological applications.
DLSTM-121 with Principal Component Analysis for Diabetic Retinopathy Classification Using Segmented Retinal Blood Vessel Images Muhammad Arhami; Marwan Ramli; Melinda Melinda; Anita Desiani; Musta’inul Abdi; Husaini Husaini
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 4 (2026): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i4.1889

Abstract

Diabetic retinopathy classification using fundus images increasingly relies on deep learning, particularly Convolutional Neural Networks (CNNs). However, the use of high-dimensional retinal images in the processing pipeline may increase computational requirements. This study proposes a DLSTM-121-based approach for diabetic retinopathy classification by applying Principal Component Analysis (PCA) to augmented retinal images before U-Net-based segmentation. The main methodological contribution is the placement of PCA before the segmentation stage, allowing dimensionality reduction to be performed before the images are processed by the subsequent segmentation and classification models. DLSTM-121 combines DenseNet-121 and Long Short-Term Memory (LSTM) to extract spatial features and model dependencies among the extracted features. The proposed pipeline consists of data augmentation, PCA, U-Net-based retinal image segmentation, and DLSTM-121 classification. Four PCA configurations using 25, 50, 75, and 100 principal components were evaluated. The results showed that 75 principal components retained approximately 99% of the image information and achieved the best classification performance, with 96.92% accuracy, 100% sensitivity, 86.67% specificity, 98.18% F1-score, and 99% AUC. This configuration required 1,435.59 s of training time and 261.76 MB of peak GPU memory, compared with 1,456.18 s and 232.41 MB for the configuration without PCA. The inference times were 177.22 and 173.20 ms/image, respectively, while both configurations required 12.713 GFLOPs. These results demonstrate that PCA can reduce the dimensionality of the retinal image representation while maintaining high classification performance. The study used 129 retinal images from a single dataset, which limits the generalizability of the findings. Future research should evaluate the proposed approach using larger and more diverse retinal datasets.
Subject-Level Aggregated Wavelet Packet Energy-Variance Features for EEG-Based Autism Spectrum Disorder Classification Zaky Naufal; Melinda Melinda; Siti Rusdiana; Yunidar Yunidar; Aufa Rafiki; Lola Vivita
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 4 (2026): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i4.1908

Abstract

This study addresses a key clinical challenge in EEG-based autism spectrum disorder (ASD) diagnosis: most prior work reports accuracy at the window level, whereas the clinical diagnostic unit is the individual subject, a mismatch that can inflate performance estimates. We develop and validate a subject-level EEG feature aggregation framework, combining Wavelet Packet Transform (WPT) energy-variance features, fold-safe ANOVA feature selection, and per-subject aggregation, evaluated under leakage-safe nested leave-one-subject-out (LOSO) cross-validation. Sixteen-channel EEG was recorded from 16 participants (8 ASD, 8 neurotypical) in Banda Aceh, Indonesia, using a single OpenBCI Cyton board with a Daisy module for native simultaneous 16-channel acquisition, preprocessed with Infomax ICA artifact removal, and classified using Logistic Regression. The framework achieved a nested LOSO balanced accuracy of 93.75% (F1 = 0.9412); because this estimate derives from only 16 subjects, the repeated subject-pair evaluation (balanced accuracy 0.9525 ± 0.0529, AUC 0.9897 ± 0.0163, permutation p = 0.0196) is treated as the primary robustness indicator. A learning-curve analysis indicated that performance approaches, but has not fully reached, a plateau at this cohort size. Sensitivity analyses across nine window/overlap configurations and six wavelets, selected within the nested cross-validation, confirmed the result was not selection-dependent. A controlled ablation indicated Infomax ICA outperformed MSICA-DWT (Wilcoxon p < 0.001), though a participant-level bootstrap interval was wide and included zero, reflecting the limited power of the 16-subject cohort. Overall, these results demonstrate the methodological feasibility of subject-level WPT energy-variance aggregation for leakage-safe ASD screening research rather than diagnostic readiness, and external multicenter validation on larger, demographically characterized cohorts is required before any clinical use.
Influence of Minkowski Distance Order on Deep Embedding-Based Cross-Spectral Cross-Distance Face Recognition Ernita Dewi Meutia; Nasaruddin Nasaruddin; Rusdha Muharar; Fitri Arnia
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 4 (2026): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i4.1932

Abstract

Cross-spectral cross-distance face recognition (CSCD-FR) is a challenging biometric task involving visible (VIS) and near-infrared (NIR) facial images acquired at varying stand-off distances. Beyond the spectral discrepancy between VIS and NIR images, increasing acquisition distance degrades resolution, blurs images, and reduces facial detail, making recognition considerably more difficult. Although deep convolutional neural networks (CNNs) have demonstrated strong capabilities for learning discriminative facial embeddings, existing studies have predominantly focused on network architectures, feature representations, and learning strategies, while the influence of distance metric selection remains relatively underexplored. Since recognition decisions are ultimately based on distances between embedding vectors, the choice of similarity metric may affect recognition performance. This study therefore investigates the influence of generalized Minkowski distance order on CSCD-FR using deep embeddings extracted from MobileNetV3-Small, EfficientNet-B2, and DenseNet121. Using the Long-Distance Heterogeneous Face Database (LDHF-DB) at stand-off distances of 1, 60, and 100 m, both ImageNet-pretrained and domain-adapted models were evaluated using -normalized embeddings. Domain adaptation optimized the embedding layer using triplet margin loss while the CNN backbones remained frozen. Minkowski orders from to were evaluated using closed-set identification and verification protocols. ImageNet-pretrained models yielded Rank-1 accuracies below 11%, whereas domain adaptation substantially improved recognition performance. DenseNet121 achieved the highest observed performance, with 93.19% Rank-1 accuracy and 2.16% EER at . The Rank-1-optimal Minkowski distance order varied across architectures, with EfficientNet-B2 and MobileNetV3-Small achieving their highest observed Rank-1 accuracies at and , respectively. These findings show that selecting a similarity metric can provide an additional matching-stage optimization without modifying the CNN backbone. Because the same retained identities were used for domain adaptation and evaluation, the results represent a dataset-specific assessment rather than recognition performance on previously unseen subjects.

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