Topographic EEG Power Mapping and Machine Learning-Based Seizure Detection Using Real and Synthetic SSIM-MSE Features
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.
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