On the Use of Wavelet Neural Networks in the Task of Epileptic Seizure Detection from Electroencephalography Signals

This paper investigates the feasibility and effectiveness of wavelet neural networks (WNNs) in the task of epileptic seizure detection. The electroencephalography (EEG) signals were first pre-processed using discrete wavelet transforms (DWTs). This was followed by the feature selection stage, where two sets of four representative summary statistics were computed. The features obtained were fed into the input layer of WNNs. Three different activation functions were used in the hidden nodes of WNNs – Gaussian, Mexican Hat, and Morlet wavelets. A 10-fold cross validation was performed and the performance assessment revealed that the proposed classifiers achieved high overall classification accuracy, which showed the prominence of WNNs in this binary classification task. The best combination to be used was the WNNs that employed Morlet wavelet as the activation function, with Daubechies wavelet of order 4 in the feature extraction stage. The cross comparison done showed that the classification accuracy achieved by WNNs was comparable to those of other artificial intelligence-based classifiers. It was also demonstrated that a classifier would perform better if input features with higher dissimilarity index were used.