Robust EEG Classification Using Enhanced Ensemble Methods for Seizure and Rhythmic Pattern Detection

Electroencephalography (EEG) is an essential tool in helping diagnose and track neurological conditions, especially epilepsy. The present study introduces an efficient EEG classification framework through an enriched ensemble learning technique to enhance the detection of seizure and rhythmic pattern. We propose a hybrid model that combines CNNs, LSTM networks, and RF classifiers, leveraging the strengths of each for improved accuracy and robustness. The input data undergo various preprocessing steps, such as normalization, artifact elimination, and wavelet transform feature extraction, before being passed to the model. For temporal dynamics capturing, we divide all EEG signals into epochs, we also validate the epochs by using a k-fold crossvalidation that reduces overfitting and increases generalization. Monitoring is done using principal metrics, including accuracy, precision, recall, F1-score, and Area under Curve (AUC). Experimental results on the CHB-MIT Scalp EEG Database show that the classification accuracy of the proposed model exceeds that from traditional methods. The results demonstrate the promising capability of ensemble-based EEG classification systems for the development of real-time, automated seizure detection, which can support clinical decision making and ultimately lead to better outcomes for patients. In the future, optimization for Real-time deployment and computational efficiency in healthcare contexts are desired.