Application of Complex Network Modeling in Neuroimaging for EEG Signal Analysis and Epileptic Seizure Detection : A Multi-Model Algorithms

Mosleh Hmoud Al-Adhaileh¹Sultan Ahmad²Alhasan A. Alharbi³MOHAMMED ALARFAJ¹Mukta Dhopeshwarkar³Theyazn H.H Aldhyani^1*

• ¹King Faisal University, Al-Ahsa, Saudi Arabia
• ²Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
• ³Department of Computer Science, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad 431004, India, Aurangabad, India

Affecting millions of individuals worldwide, epilepsy is a neurological condition marked by repeated convulsions. Monitoring brain activity and identifying seizures depends much on electroencephalography (EEG). An essential step that may help clinicians identify and treat epileptic seizures is the differentiation between epileptic and non-epileptic signals by use of epileptic seizure detection categorization. In this work, we investigated Machine learning algorithms including Random Forest, Gradient Boosting, and K-Nearest Neighbors, alongside advanced DL architectures such as Long Short-Term Memory networks and Long-term Recurrent Convolutional Networks for detecting epileptic seizures in terms of difficulties and procedures evolved depending on EEG data.The EEG data classification by applying ML and DL framework to improve the accuracy of seizure detection. The EEG dataset consisted of 102 patients (55 seizure and 47 non-seizure cases), and the data underwent comprehensive preprocessing, including noise removal, frequency band extraction, and data balancing using SMOTE to address class imbalance. Key features, including delta, theta, alpha, beta, and gamma bands, as well as spectral entropy, were extracted to aid in the classification process. A comparative analysis was conducted, resulting in high classification accuracy, with the Random Forest model achieving the best results at 99.9% accuracy. The study demonstrates the potential of EEG data for reliable seizure detection while emphasizing the need for further development of more practical and non-invasive monitoring systems for real-world applications.