Epilepsy is a psychiatric condition that has serious consequences for the human brain. The Electroencephalogram (EEG) may reveal a pattern that tells physicians whether an epileptic seizure is likely to occur again. EEG testing may also help the physician exclude other conditions that mimic epilepsy as a reason for the seizure. Now-a-days the researchers are showing much interest in these seizure detection because of its significance in epileptic detection. This paper is addressing an efficient soft computing framework for seizure detection from the EEG signal. The proposed pipeline of work is having the state-of-art as the possibility of achieving the maximum accuracy. The spectral features extracted from the Intrinsic mode functions (IMF) of EEG samples and it is directing the proposed flow towards the efficient detection of seizure and also the random forest algorithm based a convulsion classification is reliable for because of its learning behavior from the huge number of known dataset. The feature selection algorithm in this proposed work is stimulating the overall work towards the maximum true positive rate. This work is implemented on MATLAB platform and dataset were downloaded from the universal database such as Bonn university database. The results obtained from the proposed approach is showing the truthfulness of the approach introduced here.
An Improved Optimization Algorithm for Epileptic Seizure Detection in EEG Signals Using Random Forest Classifier
