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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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Structured Nursing Handover Report Generation from Clinical Speech using Fine-Tuned XLSR-53 and T5: A Benchmarking Study Sasikala D; Siva Sathya S; Niranjan Kumar D; Vignesh S
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.v8i4.1778

Abstract

Accurate nursing handovers are critical for patient safety, as miscommunication during shift transitions leads to irreversible clinical errors. This work proposed an end-to-end pipeline that converts unstructured clinical nursing speech into standardized handover reports using a fine-tuned XLSR-53 acoustic model and T5-base text-to-text transformer. An Australian English clinical corpus of 200 synthetic nursing handover recordings from the CSIRO data access portal was utilised in this work. This benchmarking study was conducted within the CSIRO synthetic Australian English nursing handover corpus and does not represent a broad cross-domain clinical ASR benchmark. A domain-specific benchmarking study across seven state-of-the-art ASR architectures (Whisper Tiny/Base/Small, Wav2Vec2 Base/Large, HuBERT Large, XLSR-53) was conducted using this corpus. The experimental results further revealed XLSR-53 as the optimal architecture for clinical nursing speech recognition. A partial layer-freeze strategy was adopted in XLSR-53 by freezing the first 12 of 24 encoder layers, empirically validated through an ablation study with five freeze configurations (L=0, 6, 12, 18, 24). XLSR-53 preserves cross-lingual phonetic representations while enabling clinical vocabulary adaptation. A clinically motivated evaluation framework using curated medical vocabulary terms computes Medical Precision, Recall, and F1-Score along with standard WER, CER, and PER to assess reliability in clinical term recognition. Benchmarking against Google Health AI's MedASR zero-shot revealed that the proposed system XLSR-53 (L=12) achieved 17.15% WER against MedASR's 28.87% (p<0.001) with a Medical F1-Score of 0.98 and ROUGE-L of 0.92. Although results were obtained on synthetic Australian English speech, performance under real clinical conditions with background noise, overlapping speakers, and spontaneous interruptions requires further validation.
A Multimodal Graph Neural Network for Multiclass ADHD and ASD Classification with Leakage-Aware Evaluation Chofifatul Hidayah; Wiharto Wiharto; Esti Suryani
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.v8i4.1801

Abstract

Neurodevelopmental disorders such as attention deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) share overlapping clinical symptoms, complicating diagnosis and motivating objective, data-driven approaches using neuroimaging and machine learning. Graph neural networks (GNNs) have shown strong performance in this domain, yet many existing studies rely on single-modality data, transductive learning, and feature selection procedures that may introduce information leakage and inflate reported accuracy. This study proposes a multimodal graph learning framework that integrates resting-state fMRI (rs-fMRI), structural MRI (sMRI), and demographic data for classifying ADHD, ASD, and healthy controls (HC). The framework integrates temporal stability-based functional connectivity, hybrid feature selection, and adaptive multi-graph learning to exploit complementary information across these modalities. Using the ADHD-200 and ABIDE datasets, the framework is evaluated under three protocols that progressively tighten control over information leakage: transductive learning with global feature selection, inductive learning with global feature selection, and inductive learning with fold-wise feature selection. Results show that classification performance is highest under the transductive, globally-selected setting (85.5% accuracy for HC vs ADHD vs ASD, 92.5% for HC vs ASD, and 90.4% for HC vs ADHD), but decreases under the strictest leakage-aware protocol (70.9%, 81.7%, and 79.3%, respectively). This performance gap indicates that conventional evaluation protocols can substantially overestimate real-world generalization. Importantly, the proposed framework still achieves reasonable accuracy under the strictest setting, suggesting genuine discriminative capability beyond evaluation artifacts. These findings emphasize that leakage-aware evaluation, although yielding lower numbers, provides a more realistic and trustworthy estimate of model performance, highlighting its importance for developing reliable neuroimaging-based GNN models
Electrooculography-Based Voluntary Eye-Blink Detection for Arabic Assistive Communication Afrah Thamer; Mussab Alaziz
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.v8i4.1848

Abstract

People with profound neuromotor disabilities commonly experience significant speech disability and lack of voluntary control over their limbs, retaining, however, the ability to perform deliberate eyelid blinks. Such retained capability presents an opportunity to be used as assistive communication. Nevertheless, reliable detection of voluntary blinks and decoding of communication patterns based on them is challenged by variation in the waveforms, amplitudes, durations, and inter-blink intervals of the voluntary eye blinks. In this work, a cost-efficient vertical electrooculography (EOG) approach for detecting voluntary eye blinks and decoding predefined assistive messages in Arabic was presented and assessed. The pipeline consists of signal conditioning, root-mean-square envelope calculation, adaptive hysteresis-based thresholding, temporal segmentation, and rule-based interpretation of single- and double-blink patterns. Quiet-background threshold updating and locking it during the communication window period were used to increase the robustness of the pattern detection process, while autocorrelation analysis and temporal constraints were utilized to interpret patterns. A single blink is associated with binary 0, and a double blink with binary 1; four consecutive pattern positions form a 4-bit frame that represents one of sixteen predefined Arabic messages. Unclear patterns are excluded from processing and not mapped into any valid message. The proposed approach was evaluated offline on 4,000 word-level recordings from 25 healthy participants, with verification using videos. The F1-score of 95.69%, macro-F1-score of 98.30%, and message-decoding accuracy of 92.98% were obtained. These findings demonstrate the possibility of implementing an interpretable and cost-efficient vertical-EOG approach for message-level Arabic assistive communication without morse-code-type encoding and letter-by-letter spelling. Direct mapping of patterns into messages can help reduce communication effort and time. Nevertheless, since the present evaluation was conducted offline and included only healthy subjects, further validation with target users and real-time implementation should be conducted before deployment.
Adaptive Optimizers for Neural Network Sperm Motility Classification I Gede Susrama Mas Diyasa; Vaizal Asy’ari; Ani Dijah Rahajoe; Sayyidah Humairah; Deshinta Arrova Dewi; Muhammad Nashif Farid; Faikul Umam
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.v8i4.1498

Abstract

Infertility remains a major challenge in global reproductive healthcare, with abnormal sperm motility being one of the primary contributing factors to male infertility. Conventional sperm motility analysis relies heavily on manual microscopic observation, which is subjective, time-consuming, and prone to inter-operator variability. Although Computer-Aided Sperm Analysis (CASA) systems provide automated solutions, their high cost and limited accessibility restrict widespread clinical adoption. Therefore, there is a need for a lightweight and cost-effective computational approach that can automatically classify sperm motility abnormalities with high accuracy. This study proposes a machine learning framework based on a Multilayer Perceptron (MLP) neural network optimized using adaptive optimization algorithms to classify sperm motility trajectories. The model utilizes two kinematic features extracted from microscopic video tracking: mean velocity and trajectory linearity. A dataset consisting of 276 unique sperm trajectories was obtained from microscopic recordings and processed using the Trackpy library to generate motion trajectories. The dataset was divided using an 80:20 train–test split strategy to evaluate the generalization capability of the model. Four adaptive optimization algorithms were systematically evaluated, including Adagrad, Adadelta, FTRL, and Adamax. The neural network was trained for 50 epochs using binary cross-entropy as the loss function and evaluated using accuracy, per-class precision, recall, F1-score, specificity, balanced accuracy, and ROC-AUC. Experimental results demonstrate that the Adamax optimizer achieved the best performance with an accuracy of 96.43% and a perfect ROC-AUC of 1.00, outperforming Adagrad (92.86%), FTRL (69.64%), and Adadelta (67.86%). The results indicate that Adamax provides more stable convergence and better optimization behavior for dense kinematic feature spaces. These findings highlight the effectiveness of adaptive optimization techniques in improving neural network performance for automated sperm motility classification. The proposed approach offers a computationally efficient and accessible alternative for supporting clinical sperm analysis and reducing subjectivity in reproductive diagnostics
NeuroQFormer-Net: A Quaternion-Guided Hybrid Attention Transformer Fusion Network for Accurate Brain Tumor Segmentation Krishnakumar B; Thanga Parvathi B; Abbinayaa M; Kunchanapalli Rama Krishna; Suganthi Rajamanickam; Satheeswaran Venkatesan
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.v8i4.1756

Abstract

Brain tumor segmentation is a critical task in medical image analysis, as accurate identification and delineation of tumor regions support timely diagnosis, treatment planning, therapeutic decision-making, and disease monitoring. Magnetic Resonance Imaging (MRI) provides multiple complementary modalities that offer valuable information about tumor structure and tissue characteristics. However, accurate segmentation remains challenging due to substantial variations in tumor size, shape, texture, and location, along with irregular and overlapping tumor boundaries, low-contrast regions, and considerable differences in the information captured by different MRI modalities. To address these challenges, this study proposes NeuroQFormer-Net (Quaternion-Guided Hybrid Attention Transformer Feature Fusion Network), a novel deep learning framework for multi-modal brain tumor segmentation. The proposed framework employs quaternion-based inter-channel correlation learning to effectively capture and integrate complementary information across multiple MRI modalities while preserving structural dependencies and enhancing cross-modal feature representations. In addition, a hybrid attention mechanism integrated with transformer-based learning is utilized to simultaneously capture fine-grained local structural characteristics and long-range contextual dependencies. To further improve segmentation accuracy, an adaptive co-learning feature fusion strategy is introduced to effectively integrate multi-scale representations obtained from different network levels. Furthermore, a boundary-aware refinement module enhances tumor boundary localization and reduces segmentation errors, particularly in low-contrast, irregular, and complex tumor regions. Experimental evaluation demonstrates the effectiveness of NeuroQFormer-Net, achieving an average Dice Similarity Coefficient (DSC) of 96.45%, Intersection over Union (IoU) of 93.26%, Precision of 96.17%, Recall of 96.08%, and Hausdorff Distance (HD) of 2.31 mm. These results indicate that the proposed framework provides accurate and robust multi-modal brain tumor segmentation
Adaptive Swarm-Driven Skin Lesion Segmentation and Classification Using Boundary-Aware U-Net and Hybrid Vision Transformer Thilagavathi Rajamanickam; Anuradha Konidena; Mahesh N; Balakrishnan M; N.K. Priyadharsini; Suganthi Rajamanickam
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.v8i4.1758

Abstract

Skin cancer is one of the most prevalent dermatological diseases worldwide, and timely and accurate diagnosis is essential for effective treatment and improved patient outcomes. However, automated skin lesion analysis remains challenging due to irregular lesion boundaries, low contrast between lesions and surrounding skin, imaging artifacts, variations in lesion appearance, and significant intra-class variability. To address these challenges, this research proposes an Adaptive Swarm-Driven Skin Lesion Segmentation and Classification Approach that integrates an Adaptive Boundary-Aware U-Net (ABAU-Net), a Butterfly Optimization Algorithm–Elephant Herding Optimization (BOA-EHO) strategy, and a Hybrid Vision Transformer with Dynamic Feature Fusion (HViT-DFF). The proposed ABAU-Net is designed to enhance segmentation accuracy by emphasizing boundary information and preserving fine-grained lesion structures, particularly in cases involving poorly defined or irregular borders. The BOA-EHO optimization strategy combines global exploration and local exploitation mechanisms to improve network parameter optimization, accelerate convergence, and reduce the possibility of premature convergence. Following segmentation, the HViT-DFF model integrates CNN-based local spatial features with transformer-based global contextual representations through dynamic feature fusion, enabling robust discrimination among multiple skin lesion classes. Extensive experiments conducted on the ISIC skin lesion dataset demonstrate the effectiveness of the proposed framework, achieving a Dice coefficient of 98.2%, Jaccard index of 97.5%, classification accuracy of 98.8%, and AUC of 0.992. These results outperform recent CNN-, Transformer-, and hybrid-based approaches. Overall, the proposed framework demonstrates strong potential for accurate, reliable, and automated skin lesion segmentation and multi-class classification, supporting computer-aided dermatological diagnosis and potentially facilitating earlier clinical intervention.
Machine Learning Approach to ATPG in VLSI Circuits Using Random Forest Regressor Sima Gonsai; Usha Mehta
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.v8i4.1842

Abstract

As the need for highly advanced chip technology and high-performance computing grows, semiconductor technology must develop rapidly. Also, as VLSI circuits become more complex, it is more difficult and time-consuming to test circuits using traditional test methods. Hence, Chip design, verification, and testing become extremely complex and critical processes. To confirm the chip as fault-free, testing examines its functionality, timing, and connectivity. Various automatic test pattern generation (ATPG) tools have been available for a long time for fault detection. For large combinational circuits, a lot of computing resources and more time are required to use such tools. To tackle this challenge, machine learning (ML) techniques have recently been introduced as a promising alternative to improve the efficiency of test generation. Machine learning has advanced dramatically over the last few years, and it currently plays an important role in enhancing automation, efficiency, and decision-making in a variety of domains. The main purpose of this work is to explore the possibility of using machine learning to predict test vectors in digital combinational circuits and achieve comparable fault coverage. The Random Forest Regressor method is utilized for generating test vectors. Experimental results showcase that this method performs well compared to traditional tools like ATALANTA on most ISCAS85 benchmark circuits. This approach reduced test pattern generation time to 0.0146 sec, while the standard tool requires 0.02 sec for test set generation, retaining almost similar fault coverage. This research demonstrated the highest fault coverage of 96.523 for the c5315_13 circuit and the lowest coverage of 60.404 for c1908_23, compared to the ATALANTA Tool. The results demonstrate that machine learning is a promising complementary testing method for conventional ATPG tools in VLSI testing.
A Disease-Memory Guided Framework for Radiology Report Generation from Multi-View Chest X-rays Prajakta Dhamanskar; Chintan Thacker; Saurabh Shah
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.v8i4.1882

Abstract

Chest X-rays are widely used, low-cost diagnostic tools. Writing chest X-ray reports manually is a time-consuming and tedious task. The increasing number of X-rays burdens radiologists, creating a need for a clinically accurate, fast and automated report generation system. To solve this challenge, we propose a new disease-memory-guided vision-language framework for radiology report generation. Frontal and lateral chest X-rays (multi-view) are encoded using a Swin-Transformer. These encoded visual features are then passed through a multi-label classification head that predicts disease probabilities, from which the top three labels are converted into learnable disease-memory embeddings. These are concatenated with projected visual tokens and fixed task instruction embeddings to form a multimodal prompt which is fed into a frozen decoder. A LLaMA-2-7B model adapted to the radiology domain using LoRA fine-tuning on the train-only MIMIC-CXR subset is used as an autoregressive decoder. The disease-memory embeddings serve as disease guidance during report generation. An extensive experimental analysis of different experimental variants is conducted on the benchmark Indiana University Chest X-ray dataset using three aspects: 1) statistical n-gram overlap, 2) semantic similarity, and 3) clinical label-based metrics. Experimental results of the proposed system show improvements in semantic evaluation metrics, with BERTScore precision, recall, and F1 values of 0.898, 0.891 and 0.894, respectively, and ROUGE-L and METEOR scores of 0.327 and 0.281, indicating improved contextual consistency between generated and reference reports. However, performance on statistical metrics (e.g., BLEU4=0.068) and clinical evaluation metrics remains limited, with Micro-F1, Macro-F1, Precision and Recall values of 0.4211, 0.0640, 0.4272, and 0.4151, respectively, indicating only partial abnormality description. This framework improves abnormality grounding while maintaining computational efficiency and can be used as a supportive tool for preliminary report drafting in research or resource-constrained settings.
ProtoSurv-X: Explainable Brain Tumor Survival Prediction Neeshu Chaudhary; Saurabh Shah; Chintan Thacker
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.v8i4.1899

Abstract

Accurate survival prediction for patients with high-grade glioma is important for prognostic assessment and personalized treatment planning; however, substantial intratumoral heterogeneity, complex multimodal MRI patterns, and limited interpretability challenge existing deep learning approaches. This study aims to develop ProtoSurv-X, an explainable and uncertainty-aware framework for MRI-based glioma survival prediction that integrates probabilistic tumor habitat modeling, prototype-guided learning, evidential prediction, and evidence-grounded clinical explanations. A unified cohort was constructed from the BraTS 2019 and BraTS 2020 datasets by removing duplicate subjects, resulting in 369 unique subjects, including 118 gross total resection patients with complete survival annotations. The proposed framework uses diffusion-enhanced SwinUNETR for tumor segmentation, probabilistic habitat construction, and fusion of radiomic, deep imaging, habitat, and age-related features. Prototype learning and survival-aware contrastive learning generate prognostic representations, while Evidential Deep Learning estimates risk and predictive uncertainty alongside continuous survival regression. For explanation generation, seven open-source large language models were benchmarked using structured model evidence, and a Meta-Llama-3.1-8B-Instruct model was fine-tuned using QLoRA. In five-fold cross-validation on the unified survival cohort, ProtoSurv-X achieved a mean absolute error of 145.2 ± 6.5 days, an RMSE of 189.4 ± 8.3 days, a C-index of 0.745 ± 0.008, and an integrated Brier score of 0.124. The segmentation module achieved Dice scores of 91.24%, 87.38%, and 81.76% for whole tumor, tumor core, and enhancing tumor, respectively, on BraTS 2019. The fine-tuned explanation model obtained a BERTScore of 0.915 and a hallucination rate of 2.6%. These findings indicate that ProtoSurv-X offers a unified computational framework for accurate, uncertainty-aware, and evidence-grounded glioma prognostic modeling, while further clinical and multicenter validation remains necessary.
Automated Retinal Disease Classification from OCT Images Using a Lightweight CNN–SE Architecture Parth Dave; Nikunj Domadiya; Jaiwal Patel
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.v8i4.1991

Abstract

Automated classification of retinal diseases from Optical Coherence Tomography (OCT) images can support timely and consistent identification of retinal abnormalities; however, many existing approaches rely on computationally intensive architectures or complex preprocessing procedures. This study proposes a lightweight Convolutional Neural Network with Squeeze-and-Excitation blocks (CNN–SE) for automated multi-class retinal disease classification while maintaining a compact computational structure. The proposed architecture consists of convolutional layers with SE-based channel recalibration, global average pooling, a fully connected layer, and dropout. Unlike approaches that depend on pretrained backbones or multi-stage segmentation, the proposed framework integrates channel-wise feature recalibration directly into a compact CNN, allowing the model to learn discriminative retinal representations with limited architectural complexity. Data augmentation was applied only to the training images, and the network was optimized using the Adam optimizer. The model was evaluated on the eight-class OCT-C8 dataset and the four-class OCT2017 dataset using accuracy, precision, recall, F1-score, and macro-ROC-AUC. The proposed model achieved 97.00% accuracy on OCT-C8 and 99.00% on OCT2017. To assess domain generalization, the model was trained exclusively on OCT-C8 and directly evaluated on the independent OCT2017 test set without retraining or fine-tuning. Across five random seeds, the cross-dataset evaluation achieved 98.74 ± 0.11% accuracy, with consistently high precision, recall, F1-score, and macro-ROC-AUC. The proposed network maintains a compact computational footprint and enables efficient image inference compared with the evaluated lightweight architectures. These results demonstrate that the proposed CNN–SE architecture provides strong classification performance and cross-dataset generalization while maintaining computational efficiency, making it a promising lightweight framework for automated retinal OCT image analysis.

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