Comparative Analysis for the Influence of the Tuning Parameters in the Algorithm for Detection of Epilepsy Based on Fuzzy Neural Networks
Date Issued
2018-09-22
Author(s)
Vesna Ojleska Latkoska
Marjan Stoimchev
Abstract
This study presents a comparative analysis for the
influence of the tuning parameters in our previously published
algorithm for detection of epilepsy [2]. As the algorithm in [2] is
generated using wavelet transform (WT) for feature extraction,
and Adaptive Neuro-Fuzzy Inference System (ANFIS) for
classification, the comparison in this paper is based on the
different data splitting methods, the different input space
partitioning methods in the ANFIS model, the usage of the
different wavelet functions in the WT, the effects of normalization,
as well as the effects of using different membership functions. The
model was evaluated in terms of training performance and
classification accuracies, and it was concluded that different
combinations of input parameters differently classify the EEG
signals.
influence of the tuning parameters in our previously published
algorithm for detection of epilepsy [2]. As the algorithm in [2] is
generated using wavelet transform (WT) for feature extraction,
and Adaptive Neuro-Fuzzy Inference System (ANFIS) for
classification, the comparison in this paper is based on the
different data splitting methods, the different input space
partitioning methods in the ANFIS model, the usage of the
different wavelet functions in the WT, the effects of normalization,
as well as the effects of using different membership functions. The
model was evaluated in terms of training performance and
classification accuracies, and it was concluded that different
combinations of input parameters differently classify the EEG
signals.
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