Extreme learning adaptive neuro-fuzzy inference system model for classifying the epilepsy using Q-Tuned wavelet transform

Abstract

Epilepsy is a nervous disorder that causes arbitrary recurrent seizures within the cerebral cortex region of the encephalon. The early diagnosis of a seizure is important in clinical therapy. An automatic epileptic seizure detection method for electroencephalogram (EEG) signals can significantly enhance the patient’s life in clinical aspect. The proposed paper is principally based on a completely unique approach of epileptic seizure detection using Q-Tuned Wavelet Transform (QTWT) and Approximate entropy (ApEn). This work focuses by utilizing and testing the common sense of Extreme Learning Adaptive Neuro-Fuzzy Inference System Model (EXL-ANFIS) which foresees the elements of the mind states as a trajectory that results in the seizure event. QTWT is used for decomposing EEG signals into sub-band frequency signals. Approximate entropy is carried out to those sub-band signals as a discriminatory function because of its indefinite disordered feature. The solutions obtained by directing towards EXL- ANFIS shows an incredible advancement in the perpetual performance outlay for the classification of an epileptic seizure. The proposed classification method is implemented on publicly available Bonn dataset. The outcome confirms that by combining extreme learning and ANFIS model improves the classification accuracy and decrease the feature dimension with reduced computational complexity. This method achieves 99.72% of classification accuracy over existing models.