Grey Wolf Optimization with Deep Recurrent Neural Network for Epileptic Seizure Detection in EEG signals

In the world fifty million peoples were affected by epileptic seizure disease. The brain activity of people is monitored by using Electroencephalography (EEG) Signals, which is utilized to identify the epileptic seizure diseases. In clinical field human master is important to measure and investigate the EEG to recognize normal signal and epileptic seizure signal. This technique is burns-through time, expensive, and the signal is additionally high in unpredictability. It is very crucial for accurately detect the seizure for antiepileptic drug treatment to prevent the future risk in the patients. In this paper Grey Wolf Optimization (GWO) based deep Recurrent Neural Network (RNN) approach is employed for accurate discovery of epileptic seizure in brain waves. The proposed seizure detection method has categorized into three steps. In the primary level the brain waves are decomposed into subbands by manipulating discrete wavelet transform. In the subsequent step, features namely line length, autocovariance and autocorrelation and periodogram, are extracted from the signal. In the following step GWO based deep recurrent neural network is applied to categorize seizure brain waves and normal brain waves from the selected features. The analysis of this method is performed using real time database and yields precision of 93.4%.The empirical scores exhibit that the existence of epileptic seizures in brain signals is effectively detected by this method.