International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Epileptic seizure detection is one of the critical challenges in biomedical signal processing, with inherent non- linearity and noise in EEG recordings. While traditional machine learning and deep learning approaches have achieved promising accuracy, generalizability, class imbalance, and interpretability remain concerning issues. The objectives of this work are to develop an ensemble-based approach by combining RF with BTC to improve the robustness and sensitivity of seizure detection. The pre-processing steps have been done using normalization and label encoding on raw EEG data obtained from the UCI Epileptic Seizure Recognition dataset. The hybrid model combines the merits of both RF, which reduces variance with the large randomness provided by the features, and BTC, with its reduced overfitting via bootstrap aggregation. These experimental results demonstrate that the proposed hybrid method performs superiorly when compared to individual ML models in several performance metrics: accuracy, precision, recall, and F1-score. The proposed work is a computationally efficient, interpretable, and reliable seizure detection framework for real-world and portable EEG monitoring systems.Keywords : Epileptic seizure detection, EEG signals, hybrid machine learning, Random Forest, and Bagged Tree Classifier.

Downloads: | DOI: 10.17148/IJARCCE.2026.15229