HEURISTIC-ASSISTED ADAPTIVE HYBRID DEEP LEARNING MODEL WITH FEATURE SELECTION FOR EPILEPSY DETECTION USING EEG SIGNALS

The word epilepsy is related to a neurological disease occurred by abnormalities of brain neurons. Timely detection of epilepsy is helpful for patients to decrease the mortality rate. To detect seizures, the Encephalogram (EEG) signals are analyzed based on monitoring the conditions of patients, and seizures can be detected from the EEG signal at appropriate times. The manual detection from the EEG signal requires more time for detecting the seizures and also it needs domain knowledge. The miss detection is eliminated by improving the classification performance in automatic epilepsy detection. Nowadays, deep learning models have not been greatly harnessed in the detection of epileptic seizures due to inappropriate descriptions of time-domain signals and sub-optimal classifier design. The aforementioned issues are combated by the novel Adaptive Hybrid Deep Learning (AHDL) approaches for epilepsy detection using EEG signals. Initially, the required EEG signal is collected from benchmark datasets. The collected signals are subjected to a signal decomposition phase that is accomplished by five levels of decomposition using Dual-Tree Complex Wavelet Transform (DTCWT), where the parameters are tuned by Improved Probability-based Coyote Optimization Algorithm (IP-COA). Further, the decomposed signal is given for feature extraction, where it divides the signal into two phases. In the first phase, the first feature set is obtained by using One-Dimensional Convolutional Neural Network (1DCNN), whereas in the second phase, the proposed model utilizes Auto Encoder (AE) to provide the second feature set. These resultant features are getting fused and the optimal feature selection process is found, where the features are obtained optimally by the IP-COA. Finally, epilepsy detection is accomplished with the aid of proposed AHDL with both Radial Basis-Recurrent Neural Networks (RB-RNN), where the hyperparameters are optimized using IP-COA. Thus, the experimental results illustrate that the suggested model enhances the detection and classification rate.