Analysis of Techniques and Methods for Automated EEG signal for Epilepsy Diagnosis: A Review - Goel, Sachin; Agarwal, Rajeev; Jain, Parag (International Journal of Computer Sciences and Engineering, 2025)
Most of the recent research has explored the possibility of predicting & analyzing epileptic seizures by using different techniques & methods. Epilepsy is the second most common neurological disorder which affects people of all ages i.e. about 1-2% of the world’s population affected by this major chronic disorder. The Electroencephalogram (EEG) signal is used as a useful tool for the early detection of epileptic seizures in several applications of epilepsy diagnosis. Many techniques have been developed for differentiate the features of seizures present in EEGs. This article reviews the seizure detection techniques & methods reported in last decade/years. However, there are various techniques like Empirical mode decomposition (EMD), wavelet transform, tensors, entropy, chaos theory, and dynamic analysis which are used in the area of epilepsy diagnosis. For better treatment of the patients it is important that the seizures are detected correctly in time. Although efforts have been made for better prediction of the seizures, the translation of current analysis & results to clinical applications is still not possible. We have reviewed a framework of reliable algorithmic seizure prediction studies, discussing each component of the whole block diagram. We have also explored all the processes, from signal acquisition to adequate performance evaluation that should be opted in the designing of an efficient seizure advisory/intervention system. The present review has established that there is a potential for improvement and optimization in the seizure prediction framework.