Deep Learning-Driven Detection of Temporal Lobe Seizures Using EEG Signals

Epileptic seizure detection using electroencephalogram (EEG) signals has received considerable attention because of the potential of automated systems to support continuous neurological monitoring and clinical decision-making. Among focal epilepsies, temporal lobe epilepsy is particularly important because the temporal lobe is a common site of focal seizure activity. However, many existing EEG-based approaches process all available channels simultaneously, potentially introducing redundant information and reducing the interpretability of the resulting models. This paper investigates a region-specific deep learning approach for automated seizure detection using EEG signals obtained from the temporal lobe. The study is derived from a broader brain-region-based seizure detection framework in which EEG channels are grouped according to anatomical regions and informative channels are selected using the Lion-Mapped Chaotic Whale Optimization (LMC-WWO) algorithm. The selected EEG signals are normalized, filtered, artifact-reduced, segmented, and transformed using Gramian Angular Summation Field (GASF) and Continuous Wavelet Transform (CWT) representations. Four deep learning architectures, namely Long Short-Term Memory (LSTM), One-Dimensional Convolutional Neural Network (CNN1D), Temporal Convolutional Network-1 (TCN1), and Temporal Convolutional Network-2 (TCN2), are evaluated for temporal-lobe seizure classification. A weighted ensemble of CNN1D and TCN2 is also investigated. Experimental results show that the LSTM model achieves the highest temporal-lobe classification accuracy of 97.62%, outperforming CNN1D (93.45%), TCN1 (91.67%), TCN2 (72.02%), and the weighted ensemble (78.57%). The findings indicate that temporal-lobe EEG signals contain sequential characteristics that can be effectively captured by recurrent temporal modeling. The study demonstrates the potential of region-specific EEG analysis for developing accurate and clinically interpretable seizure detection systems