Epileptic Seizures Diagnosis Using Amalgamated Extremely Focused EEG Signals and Brain MRI

There exists various neurological disorder based diseases like tumor, sleep disorder, headache, dementia and Epilepsy. Among these, epilepsy is the most common illness in humans, comparable to stroke. Epilepsy a severe chronic that can be discovered through analysis of signals generated by brain neurons Magnetic resonance imaging (MRI). Neurons are intricately coupled order communicate generate from human organs. Due complex nature electroencephalogram (EEG) MRI’s epileptic seizures detection related problems diagnosis becomes challenging task. Computer techniques machine learning models continuously giving their contributions diagnose all such better way than normal process diagnosis. Their performance may sometime degrade due missing information, selection poor classification model unavailability quality data used train for prediction. This research work an attempt using multi focus dataset on EEG MRI. The key steps this are: feature extraction having two different streams i.e., wavelet transformation along with SVD-Entropy, MRI convolutional neural network (CNN), after extracting features both streams, fusion applied vector support (SVM) seizures. From experimental evaluation results comparison current state-of-the-art techniques, it has been concluded proposed scheme existing models.