Epileptic seizure attack is caused by abnormal brain activity of human subjects. Certain cases will lead to death. The detection and diagnosis is therefore an important task. It can be performed either by direct patient activity during seizure or by electroencephalogram (EEG) signal analysis by neurologists. EEG signal processing and detection of seizures using machine learning techniques make this task easier than manual detection. To overcome this problem related to a neurological disorder, we have proposed the ensemble learning technique for improved detection of epilepsy seizures from EEG signals. In the first stage, EEG signal decomposition is done by utilizing empirical wavelet transform (EWT) for smooth analysis in terms of sub-bands. Further, features are extracted from each sub. Time and frequency domain features are the two categories used to extract the statistical features. These features are used in a stacked ensemble of deep neural network (DNN) model along with multilayer Perceptron (MLP) for the detection and classification of ictal, inter-ictal, and pre-ictal (normal) signals. The proposed method is verified using two publicly available datasets provided by the University of Bonn (UoB dataset) and Neurology and Sleep Center - New Delhi (NSC-ND dataset). The proposed algorithm resulted in 98.93 % and 98 % accuracy for the UoB and NSC-ND datasets, respectively.

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Epilepsy is the maximum common brain disease which has been spreading largely around the arena. It takes place because of excessive or synchronous strange activities in human brain. To degree the electric seizure pastime of mind many technology and techniques had been developed. Nearly 80% of people with epilepsy stay in low-and center-earnings countries. It is estimated that as much as 70% of people residing with epilepsy should stay seizure-free if well diagnosed and handled. The chance of premature demise in humans with epilepsy becomes three times more than for the overall population. Three quarters of people with epilepsy living in low-profits nations do now not get the remedy they want. In many parts of the arena, human beings with epilepsy be afflicted by stigma and discrimination. Epilepsy is characterized by means of surprising bursts of extra energy in the mind, manifesting as recurrent seizures. Epilepsy continues to be now not properly understood whilst compared with other neurological disorders. This paper provides an overview of detection and classification era for the reason of EEG seizure. Time frequency transforms and system learning plays an important function in extracting meaningful facts. The overall performance of detection and classification techniques of the researchers is the primary focus of the paper.

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