Seizure classification feature extraction feature selection mutual information. Frontal alpha asymmetry, a measure used as a proxy for feelings of approach or avoidance, is typically used to provide an assessment of how appealing or repellent a stimulus is. This is, in turn, necessitates the identification of pattern recognition technique to effectively distinguish EEG epileptic data from a various condition of EEG data. Results based on the analysis of separate testing data (360 h of scalp EEG, including 69 seizures in 16 patients) initially show a sensitivity of 77.9, a false detection rate of 0.86/h and a median detection delay of 9.8 s.Results after use of the tuning mechanism show a sensitivity of 76.0, a false detection rate of 0.34/h and a median detection delay of 10 s. EEG data analysis can admittedly be a complex process, which is why iMotions has several features designed to reduce the burden of this step. Three types of EEG signals (EEG signal recorded from healthy volunteer with eye open, epilepsy patients in the epileptogenic zone during a seizure-free interval, and epilepsy patients during epileptic seizures) were classified. This is mainly due to the fact that EEG detected under different conditions has different characteristics. For seizure event detection, Bonn University EEG database has been used. Further, the literature survey shows that the pattern recognition required to detect epileptic seizure varies with different conditions of EEG datasets. More than 100 research papers have been discussed to discern the techniques for detecting the epileptic seizure. Therefore, in this paper, an attempt has been made to review the detection of an epileptic seizure. It is difficult to present a detailed review of all these literature.
In this respect, an enormous number of research papers is published for identification of epileptic seizure. Deep learning does not require the manual design of the corresponding feature extractor for each classification problem.
Over many decades, research is being attempted for the detection of epileptic seizure to support for automatic diagnosis system to help clinicians from burdensome work. The use of long - term EEG recordings ( usually 24 to 72 hours ) can help clinicians detect a seizure focus in some patients.