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All Journal Journal of Electronics, Electromedical Engineering, and Medical Informatics
Ghansyamkumar Rathod
Department of Electronics and Communication, Chandubhai S Patel Institute of Technology, Charotar University of Science and Technology, Gujarat, India
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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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.
Graph-Theoretic Analysis of Electroencephalography Functional Connectivity Using Phase Lag Index for Detection of Ictal States Ghansyamkumar Rathod; Hardik Modi
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.v8i1.1230

Abstract

Epileptic disorders are characterized by the misfiring of neurons and affect 50 million people worldwide, who have to live with physical challenges in their normal lives. The ionic activity of the brain can be detected as an electrical activity from the scalp using a non-invasive bio-potential measurement technique known as electroencephalography (EEG). Manual interpretation of brainwaves is a time-consuming, expert-intensive task. In recent years, AI has achieved remarkable results, but at the cost of large datasets and high processing power. We used publicly available online datasets from the Children’s Hospital Boston (CHB) in collaboration with the Massachusetts Institute of Technology (MIT). The datasets consisted of 23 bipolar channels that included pre-processed epochs of both normal and pre-labeled seizure (ictal) states. Using the Phase Lag Index (PLI), the functional connectivity of the network was built to record consistent phase synchronization while minimizing artifacts from volume conduction. Graph-theory-based features were used to detect the brain's seizure state. A significant increase in the values of graph theoretical features, such as degree centrality and clustering coefficient, was observed, along with the formation of hyper-connected hubs and disrupted brain communication in the ictal state. Statistical tests (T-tests, ANOVA, Mann-Whitney U) across multiple PLI thresholds confirmed consistent significant differences (p-value < 0.05) between normal and ictal conditions. This study aims to provide a method based on graph theory, which is computationally efficient, interpretable, and suitable for real-time seizure detection. Considering the efficiency of clustering coefficient and degree of centrality, we can say that they are useful biomarkers for biomedical applications.
Co-Authors Hardik Modi