Review of Hybrid Classification Technique for Automated Detection and Severity Analysis of Epilepsy Using EEG Signals

Abstract

Epilepsy affects an estimated 70 million people worldwide and is defined by the realization of two or more unprovoked seizures. Introduction Electroencephalography (EEG) has long been a cornerstone in the diagnosis and management of epilepsy, but it remains challenging for interpretation due to its vast complexities. Hybrid classification methods provide the encouraging results considerably through automatic detection. These methods are combining Machine learning, Signal processing and Statistical techniques which will lead to better accuracy in detecting epilepsy and measure its severity. There are other hybrid approaches, with these having the capacity to adapt and normalize EEG signal variability amidst noise which make results more reliable and less prone to fluctuation; some examples of this being Support Vector Machines (SVM) used in combination with Convolutional Neural Networks for example. They also provide possibilities for continuous and real-time monitoring as required in clinical settings, to enable timely interventions. Nonetheless, problems like data diversity, real-time processing of it to generate predictions for each patient and model interpretability still remain. These rely on a mixture of named algorithms and human-chosen algorithmic choices, reflect the types biases that flow through every system in science but optimization for real world applications will require extensive evaluation of each other these issues. A significant upshot demands these algorithms to be more generalizable, real-time and clinically integrated in the future research work so that automation improves epilepsy management efficiently.