Computational Review on Cognitive Analysis for Epilepsy Detection Using ML and DL Applications

Individuals with epilepsy face many challenges due to the recurrent epilepsy of this complex neurological disorder. Symptoms of this epilepsy include panic behaviour, strange feelings and, in severe cases, delusions. These seizures manifest as abnormal electrical discharges in the central nervous system. Effective stroke care relies on early detection and diagnosis of stroke, enabling timely interventions to reduce risks and enhance patient outcomes We can say that there has been a remarkable breakthrough in epilepsy research in recent years is mainly due to two factors: the lack of rapidly developing machine learning EGs and the availability of inexpensive EEGs have facilitated the collection of brain activity of information, and to provide opportunities to monitor and evaluate seizure events outside of traditional medical settings Study programs have the ability to analyze EEG data and diagnose seizures, and thus patients with epilepsy no ability with enhanced control and educated decision making This review presents a current overview of existing approaches to the treatment of multiple sclerosis. Feature extraction methods and classification algorithms receive important considerations. Details of commonly used EEG data types and their availability are provided. Tested methods include a range of techniques, including well-performing learning systems are been employed to provide efficient results.