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All Journal Journal of Electronics, Electromedical Engineering, and Medical Informatics Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Rafiki, Aufa
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Health Professions Materials Science & Nanotechnology

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Implementation of Vision Transformer for Early Detection of Autism Based on EEG Signal Heatmap Visualization Rafiki, Aufa; Melinda, Melinda; Oktiana, Maulisa; Dewi Meutia, Ernita; Afnan, Afnan; Mulyadi, Mulyadi; Zakaria, Lailatul Qadri
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 1 (2025): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/40n05b64

Abstract

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder characterized by difficulties in social interaction, communication, and repetitive behavioral patterns. Early detection of ASD is crucial for improving the quality of life of affected individuals and alleviating the burden on their families. This study proposes a computer-aided diagnostic system for ASD by applying a pre-trained Vision Transformer (ViT-B/16) architecture to EEG signal data obtained from King Abdul Aziz University. The dataset comprises EEG recordings from 16 subjects (8 normal and 8 ASD) that have undergone preprocessing—including filtering using the Discrete Wavelet Transform (DWT), segmentation (windowing), and conversion into heatmap representations—and were subsequently partitioned into training, validation, and testing subsets. The ViT model was trained for 100 epochs with a batch size of 16, using the AdamW optimizer and the CrossEntropy loss function, while two learning rate configurations (0.0001 and 0.00001) were evaluated; the best-performing weights were selected based on the lowest validation loss. Test results indicate that the model trained with a learning rate of 0.00001 achieved a testing accuracy of 99.53%, accompanied by excellent precision, specificity, recall, and f1-score, thereby demonstrating strong generalization capabilities and minimal overfitting. Future research is recommended to incorporate locally sourced datasets and to further customize the ViT architecture through comprehensive hyperparameter tuning, with the aim of developing a mobile application to support clinical ASD diagnosis.
MEWT-Enhanced EEGNet for ASD EEG Classification: Performance Evaluation with k-Fold Cross-Validation Fathur Rahman, Imam; Melinda, Melinda; Yunidar, Yunidar; Basir, Nurlida; Rafiki, Aufa
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 8 No. 1 (2026): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v8i1.313

Abstract

Accurate and reliable classification of autism spectrum disorder (ASD) from electroencephalography (EEG) signals remains challenging due to the inherently nonstationary, nonlinear, and multichannel nature of EEG data. These properties complicate the extraction of discriminative features that are both stable and computationally efficient. To address this challenge, this study proposes a compact deep-learning pipeline that integrates the Multivariate Empirical Wavelet Transform (MEWT) with EEGNet for ASD–EEG classification. MEWT decomposes multichannel EEG signals into spectrally aligned subbands while preserving inter-channel relationships. The resulting MEWT-based features are then processed by EEGNet, a lightweight convolutional neural network specifically designed for EEG-based learning tasks. Performance was evaluated using 5-fold cross-validation. The proposed MEWT with the the EEGNet model achieved a mean test accuracy of 98.35%, with consistently high precision (98.23%), recall (98.45%), F1-score (98.34%), and specificity (98.24%) across all folds. Confusion-matrix results indicated very few and well-balanced false positives and false negatives, supporting stable discrimination between ASD and control EEG segments. A one-sample one-tailed t-test against a 50% chance level confirmed that all evaluated metrics were significantly above chance (p < 0.0001). When benchmarked against previously reported results on the same dataset, the proposed approach slightly improved upon EMD with EEGNet (97.99%) and clearly outperformed EWT with EEGNet (95.08%), suggesting that MEWT-derived multichannel features are well matched to compact convolutional architectures for ASD–EEG analysis. Despite these strong results, the study is limited by a small, single-site cohort and the use of a single deep-learning model. Future work will focus on standardized retraining across multiple feature families and validation on larger and more diverse populations to further assess robustness and generalizability
Heavy–Light Soft-Vote Fusion of EEG Heatmaps for Autism Spectrum Disorder Detection Melinda, Melinda; Gazali, Syahrul; Away, Yuwaldi; Rafiki, Aufa; Wong, W.K; Muliyadi, Muliyadi; Rusdiana, Siti
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 1 (2026): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i1.1377

Abstract

Autism spectrum disorder is a neurodevelopmental condition that affects social communication and behaviour, and diagnosis still relies on subjective behavioural assessment. Electroencephalography provides a noninvasive view of brain activity but is noisy and often analysed with handcrafted features or evaluation schemes that risk data leakage. This study proposes a deep learning pipeline that combines wavelet denoising, EEG-to-image encoding, and heavy-light decision fusion for autism detection from EEG. Sixteen-channel EEG from children and adolescents with autism and typically developing peers in the KAU dataset is denoised using discrete wavelet transform shrinkage, segmented into fixed 4 second windows, and rendered as pseudo colour heatmaps. These images are used to fine-tune five ImageNet pretrained architectures under a unified training protocol with 5-fold cross-validation. Heavy-light fusion combines one heavyweight backbone and one lightweight backbone through weighted soft voting on class posterior probabilities. The strongest single model, ConvNeXt Tiny, attains about 97.25 percent accuracy and 97.10 percent F1 score at the window level. The best heavy light pair, ConvNeXt plus ShuffleNet, reaches about 99.56 percent accuracy and 99.53 percent F1, with sensitivity and specificity in the 99 percent range. Fusion mainly reduces missed ASD windows without increasing false alarms, indicating complementary error patterns between heavy and light models. These findings show that the proposed denoise encode classify pipeline with heavy light fusion yields more robust autism EEG classification than individual backbones and can support EEG-based decision support in autism screening.
CNN-Based Facial Image Analysis for Pediatric Down Syndrome Classification Yunidar, Yunidar; Harahap, Inda Mariana; Melinda, Melinda; Rosmawinda, Rosmawinda; Basir, Nurlida; Rafiki, Aufa; Rahman, Imam Fathur
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.v8i2.1523

Abstract

Down syndrome (trisomy 21) is a genetic disorder caused by an extra copy of chromosome 21, resulting in distinctive developmental facial characteristics and intellectual delays. Early detection is crucial to enable timely medical intervention. However, conventional diagnostic procedures still rely on clinical observation and genetic testing, which can be invasive and expensive. This study proposes a facial image–based classification system for detecting Down syndrome using a Convolutional Neural Network (CNN) approach. Seven CNN architectures were evaluated, namely EfficientNetB0, MobileNetV2, ResNet34, ShuffleNetV2, AlexNet, VGG19, and InceptionV3, under two training scenarios: with and without early stopping. The dataset consisted of 1,000 facial images of children with and without Down syndrome, split into training, validation, and test sets at 60:20:20. Face detection was performed using the Haar Cascade Classifier, followed by data augmentation techniques including rotation, zoom, translation, horizontal flipping, and Gaussian noise to improve model generalization and reduce overfitting. Experimental results show that the VGG19 architecture achieved the best performance, with an accuracy of 94.5%, precision of 91.59%, recall of 98%, and an F1-score of 94.69%. A one-way ANOVA test yielded an F-value of 0.003 and a p-value of 0.955 (> 0.05), indicating no statistically significant difference between models trained with and without early stopping. Grad-CAM visualization highlighted key facial regions, namely the eyes, nose, and mouth, as the primary contributors to classification, while analysis using 68 facial landmark points revealed distinctive morphological patterns associated with Down syndrome. The integration of CNN models, Grad-CAM visualization, and facial landmark analysis demonstrates a promising, interpretable, and non-invasive approach to supporting early Down syndrome screening using facial images
Co-Authors Afnan, Afnan Basir, Nurlida Ernita Dewi Meutia Fathur Rahman, Imam Gazali, Syahrul Inda Mariana Harahap Lailatul Qadri Zakaria Melinda Melinda Muliyadi Muliyadi Mulyadi Mulyadi Oktiana, Maulisa Rahman, Imam Fathur Rosmawinda, Rosmawinda Siti Rusdiana Wong, W.K Yunidar Yuwaldi Away