Epileptic Detection Based On EEG Signal Using Graph Index Complexity & Average Weight Degree
Abstract
EEG signal suffers from some problem which leads to difficult during diagnosis of diseases. This paper
is presented a new approach for diagnosing epilepsy disease through EEG signals are using Weighted
Visibility Graph(WVG) for build complex networks and then extracting two feature weight index
complexity and average weight index of the network which characterized by its ability to detect seizure
cases depending the amount of change in the EEG.
the suggested method has been tested with a publicly available benchmark database. So evaluation of
performance of proposed the method the experimental results with Support Vector Machine SVM
classifier gave us 100% accuracy for classifying healthy VS epileptic seizure signals and also when
testing every feature as separate way average have get 97% accuracy.
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