Enhancing EEG signals classification using LSTM‐CNN architecture

Epilepsy is a disorder that interferes with regular brain activity and can occasionally cause seizures, odd sensations, and momentary unconsciousness. Epilepsy is frequently diagnosed using electroencephalograph (EEG) records, although conventional analysis is subjective and prone to error. The dynamic and non‐stationary nature of EEG structure restricted the performance of Deep Learning (DL) approaches used in earlier work to improve EEG classification. Our multi‐channel EEG classification model, dubbed LConvNet in this paper, combines Convolutional Neural Networks (CNN) for extracting spatial features and Long Short‐Term Memory (LSTM) for identifying temporal dependencies. To discriminate between epileptic and healthy EEG signals, the model is trained using open‐source secondary EEG data from Temple University Hospital (TUH). Our model outperformed other EEG classification models employed in comparable tasks, such as EEGNet, DeepConvNet, and ShallowConvNet, which had accuracy rates of 86%, 96%, and 78%, respectively. Our model attained an amazing accuracy rate of 97%. During additional testing, our model also displayed excellent performance in trainability, scalability, and parameter efficiency. In this study, we propose LConvNet, a multi‐channel EEG classification model that combines CNN for spatial feature extraction and LSTM for capturing temporal dependencies. By training the model using secondary EEG data from Temple University Hospital, we achieved an impressive accuracy of 97%, outperforming existing models such as EEGNet, DeepConvNet, and ShallowConvNet. Our model also demonstrated excellent performance in terms of trainability, scalability, and parameter efficiency, addressing the limitations of previous deep learning techniques in handling the dynamic and non‐stationary nature of EEG structures.