Enhanced epileptic seizure detection using CNNs with convolutional block attention and short-term memory networks

The CNN_CBAM_LSTM model achieved an accuracy of 98.80% in detecting epileptic seizures from EEG signals.

• A novel method for detecting epileptic seizures combines Convolutional Neural Networks and Long-Short-Term Memory Networks.
• The model utilizes a convolutional block attention module to enhance focus on critical information in EEG signals.
• Parameter optimization and ablation experiments were conducted to evaluate the model's performance on the Bonn University dataset.
• This approach may significantly improve the early detection and intervention of seizures in epilepsy patients.

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