Epileptic spasm recognition: EEG classification using time-frequency features and machine learning

Epileptic spasm (ES), characterized by sudden muscle contractions and loss of consciousness, poses significant challenges in early diagnosis and treatment, especially in infants and young children. Despite advances in EEG-based seizure detection, the automatic classification of ES remains a complex task due to the variability of seizure patterns. In this study, we propose an approach for classifying ES EEG based on clinically collected data, using time-frequency domain features derived from EEG signals and machine learning models. A total of 54 time-frequency features were extracted, and three machine learning algorithms-Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM)-were employed to classify the seizures. The results show that the RF model achieved the highest classification accuracy of 81.18% when fewer features were used, whereas KNN has increased performances with larger feature sets. This work highlights the potential of combining time-frequency features with machine learning for accurate seizure classification, offering a promising tool for automated monitoring and diagnosis of ES. Further research is needed to refine feature extraction methods and improve model robustness for clinical applications.