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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 338 Documents
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Efficient VGA-Net Modification Using ConvNeXt-Tiny and GATv2 for Retinal Vessel Segmentation Billie Zandra Widiyanto; Wiharto Wiharto; Shaifudin Zuhdi
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 3 (2026): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i3.1747

Abstract

Retinal blood vessel segmentation plays a crucial role in the early detection of ocular diseases such as diabetic retinopathy, glaucoma, and macular degeneration. Existing hybrid architectures, such as VGA-Net, suffer from high computational complexity due to the VGG-16 backbone and limited attention expressiveness due to its static GAT module, yet no prior work has examined replacing both components within a patch-based graph architecture in which backbone feature quality directly conditions graph attention effectiveness. This study aims to improve the computational efficiency and topological modeling of VGA-Net by replacing VGG-16 with ConvNeXt-Tiny and substituting GAT with GATv2. The primary contribution is a 55% parameter reduction through the ConvNeXt-Tiny backbone substitution and improved vessel topology modeling through GATv2's dynamic attention mechanism, which produces fully dynamic attention coefficients per query node. Experiments were conducted on the DRIVE and STARE datasets using a consistent preprocessing pipeline, one-factor-at-a-time hyperparameter tuning, and a unified evaluation protocol across all compared methods. The proposed model achieves the lowest parameter count (5.3M) and GFLOPs (3.2443), with a competitive inference time of 61.00 ms per image, among all compared methods, while achieving competitive performance in sensitivity and topological continuity. On the DRIVE dataset, the model achieved the highest sensitivity of 0.8718 and the highest clDice of 0.8446. On the STARE dataset, the model achieved the highest sensitivity of 0.9383 and the highest clDice of 0.9055. These results demonstrate that the proposed model achieves a favorable efficiency-performance trade-off, leading to sensitivity and topological continuity at the lowest computational cost among all compared methods, at the expense of lower specificity, accuracy, Dice, and MCC relative to certain compared methods.
Skin Cancer Classification by Applying Different Models of Artificial Intelligence Wajid Dawood Alwan; Osama Qasim Jumah Al_Thahab; Hanaa Mohsin Ali Al Abboodi
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 3 (2026): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i3.1757

Abstract

Accurate multiclass classification of dermoscopic skin lesions remains challenging because of high inter-class visual similarity, substantial intra-class variability, and frequent acquisition artifacts (black borders, hair occlusions, noise). We propose a unified, reproducible framework that systematically coordinates four stages: (i) artifact-aware preprocessing (field-of-view circular cropping, hair removal, CLAHE, bilateral filtering); (ii) lesion-focused segmentation via GrabCut-refined fusion and a U-Net with EfficientNet-B3 encoder; (iii) compact deep-feature extraction (EfficientNet-B7) refined by principal component analysis and Neural Spline Flow density calibration; and (iv) robust machine-learning classification. The HAM10000 dataset (n = 10,015, seven diagnostic classes) was partitioned once by stratified random sampling into training (70 %, n = 7010), validation (15 %, n = 1502), and test (15 %, n = 1503) subsets under a strictly sequential anti-leakage protocol with patient-level isolation; the test set was sequestered until terminal evaluation. External generalization was assessed on an independent ISIC 2019 subset (n = 350, 50 per class) without retraining. On the held-out HAM10000 test set, XGBoost achieved the highest accuracy of 99.47 % with an F1-score of 98.99 %, followed by LightGBM (98.20 %) and MLP (97.67 %). Ablation analysis confirmed incremental gains of +2.55 % (preprocessing), +1.75 % (segmentation), and +1.32 % (Neural Spline Flow refinement). On the external ISIC 2019 data, MLP attained the best cross-domain accuracy of 95.43 %, demonstrating that the feature backbone generalizes beyond the training distribution. The demonstrated synergy of artifact suppression, lesion-centered segmentation, and density-calibrated feature learning yields highly discriminative and generalizable representations, providing a robust foundation for reliable computer-aided dermatologic screening
Deep Learning Based Pain Recognition via Facial Expression Using Feature Fusion of Visual and Thermal Images Raihan Islamadina; Fitri Arnia; Taufik Fuadi Abidin; Rusdha Muharar; Aulia Syarif Aziz; Khairun Saddami
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 3 (2026): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i3.1766

Abstract

Objective pain assessment in non-verbal patients remains a significant clinical challenge. While automated facial expression analysis offers a promising solution, existing methods often rely on simple cross-modality concatenation or shallow attention mechanisms, which typically fail to fully capture complex, interdependent cross-modal dynamics. To address this limitation, this study proposes a deep learning-based multimodal feature fusion framework that combines visual and thermal images to significantly enhance pain detection accuracy. The proposed framework integrates pretrained convolutional neural network backbones, specifically VGGFace, ResNet50, and DEYOLO, with two novel attention modules: Dual Semantic Enhancing Channel Weight Assignment (DECA), and Dual Spatial Enhancing Pixel Weight Assignment (DEPA) to adaptively optimize joint feature representations. For comparison, a hybrid baseline model that processes visual and thermal images separately was also developed, allowing a comparative analysis between fusion-based and non-fusion-based approaches. The model performance was systematically evaluated on the MIntPain dataset, which comprises 20 healthy subjects experiencing five distinct levels of pain intensity. To ensure data independence and prevent identity leakage across the training and testing phases, a strict subject-wise data split protocol was implemented with 15 subjects allocated for training and 5 subjects for testing. Experimental results demonstrate that the proposed multimodal fusion framework achieves superior performance, attaining a peak F1 score of 0.938 using the VGGFace backbone. Furthermore, external validation on the UNBC McMaster Shoulder Pain dataset yields a classification accuracy of 0.872, confirming the strong generalization capability and stability of the framework across unseen subjects. These findings highlight the effectiveness of visual-thermal synergy and the efficacy of the proposed DECA and DEPA modules, showcasing high potential for robust, non-invasive clinical pain-monitoring and assessment applications
Design and Mechanical Evaluation of a Polymer Keel SACH Foot Using Finite Element Analysis and Experimental Validation Agus Setyo Nugroho; Kazuhiko Sasaki; Muhammad Nouman; Rifky Ismail
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 3 (2026): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i3.1807

Abstract

Conventional Solid Ankle Cushion Heel (SACH) foot commonly uses wooden keels, which may exhibit variability in mechanical properties and limited long-term durability. This study aimed to develop and evaluate a polymer-based keel SACH foot as a low-cost alternative with improved mechanical performance. An integrated methodology combining finite element analysis (FEA) and experimental validation was employed. Three polymer keel SACH foot configurations were designed and analyzed under loading conditions that represent heel strike, mid-stance, and terminal stance, in accordance with ISO 10328. The optimal design was subsequently fabricated and tested under static loading conditions to validate the numerical model. The models utilized ABS for the keel, HDPE for the footplate, and vulcanized rubber for the foot body. Mechanical performance was assessed through total deformation, von Mises stress, strain, and safety factor analysis. Among the three configurations, Model A demonstrated the best mechanical performance, with the lowest average deformation (20.12 mm), the lowest stress concentration (12.67 MPa), and the highest safety factor (1.59). The selected design was subsequently fabricated and validated experimentally under static loading conditions up to 1176.78 N. Experimental validation showed strong agreement with FEA predictions, with deviations below 5% across all gait phases, confirming the accuracy of the FEA model. Comparative testing against a conventional wooden-keel SACH foot revealed significantly lower deformation values for the polymer-based keel SACH foot (p < 0.05), indicating improved structural stiffness and more efficient load distribution during loading process. These findings suggest that replacing conventional wooden keels with polymer-based structures can enhance mechanical consistency and structural reliability, while maintaining manufacturability and cost-effectiveness. The proposed design offers a promising approach to developing an affordable and durable prosthetic foot, particularly for use in low- and middle-income countries
Clustering Analysis for Anemia Risk Profiling in Hospital Patients Using K-Prototypes Approach Oki Setiono; Zaenal Sugiyanto; Dyah Ernawati; Arif Kurniadi; Ika Pantiawati; Mohamad Nazri Husin
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 3 (2026): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i3.1723

Abstract

Anemia in inpatients represent a complex clinical condition often associated with systemic responses such as inflammation and infection. However, conventional univariate approaches based solely on hemoglobin (Hb) levels frequently fail to capture the multidimensional nature of anemia risk, particularly in mixed-type clinical datasets. This study aims to develop an anemia risk stratification framework by integrating hematological and demographic variables using the K-Prototypes clustering algorithm. The dataset consisted of 587 patient records collected from multiple hospitals in Central Java, Indonesia, including hemoglobin, leukocytes, platelets, age, and sex. Data preprocessing involved cleaning, standardization, and mixed-data transformation prior to clustering analysis. Multiple cluster configurations ( = 3, = 4, and = 5) were evaluated using the Elbow Method and clustering validation metrics, including Silhouette Score, Davies–Bouldin Index, and Calinski–Harabasz Index. The results identified = 4 as the optimal clustering configuration, providing the best balance between cluster separation and interpretability. Subsequently, the identified clusters were clinically interpreted into three risk categories, including High Risk, Moderate Risk, and Normal Risk. The High-Risk group exhibited the lowest mean Hb level (9.27 g/dL), while the Moderate Risk group was characterized by elevated leukocyte counts (19.2k/µL), suggesting distinct hematological patterns. The Normal Risk group demonstrated relatively stable hematological profiles and higher mean age. Statistical testing confirmed significant differences among risk profiles for age, hemoglobin, leukocytes, platelets, and gender ( < 0.001). These findings demonstrate that anemia-related risk patterns are influenced by multidimensional interactions among hematological and demographic factors. The proposed framework provides clinically meaningful patient stratification and has potential applications in electronic medical record systems and clinical decision support environments.
STFT-Based Multiclass Heart Sound Classification Using BiLSTM and CNN-BiLSTM Models Noor S.; Ehab Abdulrazzaq Hussein; Laith Ali Abdul-Rahaim
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.v8i4.1724

Abstract

Cardiovascular diseases require early and reliable screening because manual auscultation may be affected by noise, subjective interpretation, and inter-observer variability. This study aimed to develop an STFT-based deep learning framework for multiclass phonocardiogram (PCG) classification. The proposed framework was designed to provide a reproducible evaluation procedure by combining standardized preprocessing, time–frequency feature extraction, and deep learning-based classification under the same experimental conditions. Unlike approaches that may evaluate segmented signals without clearly preserving recording-level separation, this study emphasizes a leakage-free splitting strategy to reduce the risk of overestimated performance and to provide a more reliable assessment of model generalization. The Yaseen PCG dataset, consisting of 1000 recordings from five classes (AS, MR, MS, MVP, and Normal), was divided using a leakage-free recording-level split before segmentation and spectrogram generation. After preprocessing, 2-second PCG segments with 50% overlap were converted into 128 × 128 STFT spectrograms and classified using BiLSTM and CNN-BiLSTM models. Both models were trained and tested using the same dataset split, preprocessing pipeline, and evaluation metrics, including accuracy, precision, recall, F1-score, specificity, and confusion matrices. The BiLSTM model achieved 92.36% accuracy in the final independent test run, while the CNN-BiLSTM model achieved 95.83%. Across three repeated runs, BiLSTM achieved 93.85% ± 1.47%, whereas CNN-BiLSTM achieved 95.94% ± 0.71%. These results show that CNN-BiLSTM provides higher and more stable classification performance for five-class PCG classification, while BiLSTM remains a simpler alternative for lightweight implementation. Overall, the proposed STFT-based framework provides a reliable approach for automated heart sound classification and may support future computer-aided cardiac screening applications.
Hybrid Time-Frequency ECG Arrhythmia Classification with Feature-Model Compatibility and Ensemble Decision Fusion J Prayoga; Melinda Melinda; Teuku Yuliar Arif; Herlina Dimiati
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 3 (2026): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i3.1730

Abstract

Cardiac arrhythmia is a critical cardiovascular disorder associated with high global mortality, while electrocardiogram (ECG)-based diagnosis remains time-consuming and susceptible to inter-observer variability. Although recent artificial intelligence approaches have improved automated ECG analysis, many existing studies rely on single time-frequency representations and uniform classification strategies, limiting their ability to capture complementary ECG characteristics. This study proposed a hybrid time-frequency ECG arrhythmia classification framework incorporating feature–model compatibility and ensemble decision fusion to improve classification robustness and reliability. ECG signals from the MIT-BIH Arrhythmia Database were preprocessed using a fourth-order Butterworth bandpass filter and segmented using overlapping windows. To prevent data leakage, patient-wise cross-validation was employed, ensuring that ECG segments originating from the same patient were assigned exclusively to either training or testing folds. Three complementary time-frequency representations, namely Mel-Spectrogram, Short-Time Fourier Transform (STFT), and Discrete Wavelet Transform (DWT), were paired with their most compatible classifiers: Convolutional Neural Network (CNN); Random Forest (RF); and Support Vector Machine (SVM); respectively. Decision fusion was performed using stacking, hard voting, and soft voting strategies. Experimental results showed that the DWT-SVM model with stacking achieved the best overall performance, attaining 94.82% accuracy and a 94.59% F1-score, while STFT-RF with hard voting achieved comparable performance with 94.75% accuracy and a 94.50% F1-score. In contrast, Mel-Spectrogram-CNN produced substantially lower performance with 81.45% accuracy, indicating limited suitability of Mel-scale representations for ECG morphology analysis. Statistical analysis confirmed significant performance differences among models (p < 0.001). The findings demonstrate that integrating hybrid time-frequency representations with feature–model compatibility and model-dependent decision fusion provides a robust framework for automated ECG arrhythmia classification with strong potential for clinical decision support and real-time cardiac monitoring applications
Temporal Deep Learning with Multiscale Principal Component Features for Autism Classification from Electroencephalographic Signals Muliyadi Muliyadi; Melinda; Yuwaldi Away; Syahrul Gazali; Aufa Rafiki; W.K Wong
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 3 (2026): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i3.1735

Abstract

Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental condition whose early identification remains challenging because clinical assessment still relies heavily on behavioral observation and expert judgment. Electroencephalography (EEG) offers a noninvasive approach for capturing neural dynamics. Still, EEG-based ASD classification remains difficult because of signal nonstationarity, limited sample sizes, and the risk of subject-identity leakage. This study proposes a comparative temporal deep learning framework for EEG-based ASD classification by evaluating principal component analysis (PCA) and multiscale principal component analysis (MS-PCA) as feature representations combined with a recurrent neural network with bidirectional long short-term memory (RNN-BiLSTM) and a temporal convolutional network with self-attention (TCN-SA). Resting-state eyes-open EEG signals were acquired from only 10 participants, consisting of 5 individuals with ASD and 5 typically developing controls, using a 16-channel acquisition system. The signals were filtered using a fourth-order Butterworth band-pass filter, transformed into PCA or MS-PCA representations, segmented into 4 s windows with 50% overlap, and evaluated using subject-wise 5-fold cross-validation to reduce subject-identity leakage. The results showed that MS-PCA produced higher descriptive performance than PCA in both temporal architectures, with the strongest descriptive result obtained by the MS-PCA + TCN-SA scheme, which achieved a mean accuracy of 97.96 ± 2.37% and balanced precision, recall, F1-score, and specificity. However, the inferential comparison between PCA and MS-PCA did not reach statistical significance at the 0.05 level, and the cohort size was limited to 10 participants. Therefore, these findings should be interpreted as preliminary descriptive evidence within the present cohort rather than evidence of diagnostic readiness or robust clinical applicability. Larger, independent, and demographically diverse EEG datasets with richer clinical characterization are required to confirm the observed trend and evaluate the generalizability of the proposed framework.

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