  Epileptic seizures are one of the most crucial neurological disorders, and
their early diagnosis will help the clinicians to provide accurate treatment
for the patients. The electroencephalogram (EEG) signals are widely used for
epileptic seizures detection, which provides specialists with substantial
information about the functioning of the brain. In this paper, a novel
diagnostic procedure using fuzzy theory and deep learning techniques is
introduced. The proposed method is evaluated on the Bonn University dataset
with six classification combinations and also on the Freiburg dataset. The
tunable-Q wavelet transform (TQWT) is employed to decompose the EEG signals
into different sub-bands. In the feature extraction step, 13 different fuzzy
entropies are calculated from different sub-bands of TQWT, and their
computational complexities are calculated to help researchers choose the best
set for various tasks. In the following, an autoencoder (AE) with six layers is
employed for dimensionality reduction. Finally, the standard adaptive
neuro-fuzzy inference system (ANFIS), and also its variants with grasshopper
optimization algorithm (ANFIS-GOA), particle swarm optimization (ANFIS-PSO),
and breeding swarm optimization (ANFIS-BS) methods are used for classification.
Using our proposed method, ANFIS-BS method has obtained an accuracy of 99.74%
in classifying into two classes and an accuracy of 99.46% in ternary
classification on the Bonn dataset and 99.28% on the Freiburg dataset, reaching
state-of-the-art performances on both of them.
