Epilepsy Classification Based on EEG Spectral Analysis Combined with Machine Learning and Deep Learning
Epilepsy is a common chronic neurological disorder characterized by recurrent abnormal brain electrical activity. Due to the wide range of epilepsy types and varied clinical manifestations, diagnosis traditionally depends on the interpretation of EEG (electroencephalography) signals by experienced physicians—a process that is often time-consuming and subjective. In recent years, the need for objective and automated diagnostic methods has driven increasing interest in applying artificial intelligence to epilepsy classification.
This study utilizes machine learning and deep learning techniques to develop an epilepsy classification model. By collecting and preprocessing large-scale EEG data, and applying feature extraction and algorithm training, we explore the potential of various approaches in the classification of epileptic conditions. The aim is to support clinical diagnosis, enhance efficiency and objectivity, and contribute to the advancement of intelligent healthcare systems.
