Modified binary salp swarm algorithm in EEG signal classification for epilepsy seizure detection

Epilepsy is a brain disorder characterized by sudden seizures, periodic abnormal and inappropriate behaviour, and an altered state of consciousness. The visual diagnosis of epilepsy using electroencephalogram (EEG) signals is challenging, which led to the development of machine learning methods to automate this task. With the help of machine learning techniques optimized for epilepsy, this study aims to diagnose epilepsy disorders and related seizures with high accuracy. In the first step, the proposed multilevel method applies Discrete Wavelet Transform (DWT) to decompose the EEG signal into sub-band frequency levels. Next, the algorithm uses the Modified Binary Salp Swarm Algorithm (MBSSA), a population-based strategy, to extract time-domain features. The proposed method uses the Levenberg-Marquardt (LM) backpropagation classification model as a Feed-Forward Neural Network (FFNN). The MBSSA also optimally determine the type of WT (DWT or Double Density DWT (DDT)), the number of decomposed levels in WT, the best-fitted mother wavelet and the number of neurons in the hidden layer of FFNN, preventing manual and time-consuming calculation. Evaluation procedures compare the proposed method to other state-of-the-art methods and verify its superiority by achieving the highest and average accuracy of %99.45 and %98.46, respectively.