Generalized Tonic Clonic Seizure Classification Based on Optimized Feature Extraction from Surface Electromyography

Epilepsy is one of the most common neurological disorders and characterized by unpredictable recurrent seizures that affect people of different ages. Certain seizures can result in severe, uncontrollable bodily motions that injure patients or cause Sudden Unexpected Death in Epilepsy (SUDEP). Help is desperately needed because the majority of these symptoms are associated with generalized tonic-clonic seizures (G TCS). Surface electromyography (sEMG) has been clinically verified for automated GTCS identification and can be utilized for non-invasive G TCS detection. However, classification accuracy and computing efficiency are challenged by the high-dimensional and complicated nature of EMG data. In order to improve GTCS's classification performance with EMG signals, this research inves-tigates feature optimization strategies. Various feature extraction methods are applied in the signal-specific and signal independent domain, followed by ANOVA technique. After extracting 192 features in total for both types, we ranked the top 10 features in both types that performed optimally and could be crucial in GTCS classification. The effectiveness of these optimized features is evaluated using machine learning models. We classified the dataset by using the Random Forest model (95%), K-nearest Neighbor Algorithm (90%), Extreme Gradient Boost (95%), Decision Tree (93 %), Artificial Neural Network (97 %) and Long-short Term Memory (96%) and compared the results with one another for the G TCS classification. Experimental results demon-strate that optimized feature selection significantly improves classification accuracy while reducing computational complexity. This study highlights the importance of feature optimization in EMG signal for GTCS classification and understanding the severity of the seizures in patients.