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

ABSTRACT: 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 hig

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC12729157 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature