Abstract: Epileptic seizures are sudden neurological events that require timely detection and intervention. Electroencephalogram (EEG) signals, due to their non-invasive nature, are widely used for seizure analysis. This paper presents a complete automated seizure detection framework using the Children’s Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) Scalp EEG Database. The system integrates preprocessing, feature extraction, data balancing, data augmentation, and classification into a robust pipeline. Key features were derived using power spectral density, wavelet decomposition, entropy, and statistical measures. To overcome class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) was employed. Multiple machine learning classifiers, including Support Vector Machine (SVM), Random Forest (RF), Extra Trees (ET), Logistic Regression (LR), XG Boost (XGB), and Light GBM (LGBM) were evaluated. Performance was assessed using 5-fold stratified cross-validation, demonstrating that the integration of spectral and nonlinear EEG features with advanced machine learning methods significantly improves seizure detection accuracy.

Keywords: Epileptic seizure detection; Electroencephalogram (EEG); Machine learning, Feature extraction; Children’s Hospital Boston- Massachusetts Institute of Technology (CHB-MIT).

Download: | DOI: 10.17148/IMRJR.2026.030603