A Novel for Seizure Prediction Using Artificial Intelligent and Electroencephalography

Seizure prediction is critical in effectively managing epilepsy, a chronic neurological disorder characterized by sudden abnormal brain activity. This study proposes a Long Short-Term Memory (LSTM) neural network deep learning model for seizure prediction using non-invasive scalp Electroencephalography (EEG) recordings. The LSTM model is well-suited for time series analysis, making it an ideal candidate for detecting patterns in EEG data. The model is designed to distinguish between ictal and interictal states, accurately predicting the onset of seizures with high sensitivity and minimal false alarms. The results demonstrate the effectiveness of the proposed model, achieving accuracy rates ranging from 99.07 to 99.95% and a sensitivity of 1, indicating that all seizure states are correctly predicted. The model's low false positive and false negative rates validate its potential for early and accurate seizure prediction. Future works are presented at the end of this paper to enhance the model's performance and translate it into practical clinical applications. The proposed LSTM model holds promising prospects for timely seizure warnings and improving the quality of life for patients with epilepsy.