Integrative Approach for Epileptic Seizure Detection: A Comparative Analysis

Epilepsy, a condition affecting the central nervous system (CNS). An epileptic seizure (ES) is brought on by an abrupt aberrant electrical impulse that takes place in the cerebral networks and typically lasts little than a few minutes in duration. Different types of seizures can cause different symptoms, such as jerking, losing consciousness, or increased confusion. The electroencephalogram (EEG), which captures neural electrical reactions, is a crucial test for diagnosing epilepsy. The EEG displays the patterns of normal or abnormal electrical impulses in the brain. It is possible to distinguish between the various stages of an epileptic seizure using characteristics found in EEG data collected both before and during the seizure. Conversely though, manual interpretation of EEG data is a costly and time consuming process that necessitates highly qualified professionals for analysis. These limitations have encouraged research on automated methods to assist neurologists in differentiating between brain signals from seizures that are non-epileptic and those that are epileptic. Consequently, we developed a model capable of detecting epileptic seizures while identifying between various seizure states from unprocessed EEG data.