A Novel approach for Epilepsy Classification using Fuzzy C-means and ANN

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Mohammad Asif A Raibag, Dr. J Vijay Franklin


Detection from the EEG recorded signals to epileptic seizure activity is critical and challenging to locate and classify
epileptic seizures. It is evident from the study that seizure development is essentially a dynamic, non-static process consisting
of multiple frequencies. The traditional methods for extracting useful data out of EEG signals have limited applications. Hence,
in this paper, we present a time frequency analysis (t-f) along with deep learning technique to classify epilepsy. There are four
stages to the analys is: 1) Initially the t-f analysis and calculation of power spectrum density (PSD) of each EEG segment is
performed. 2) Relevant features will be extracted from the specific t-f window. 3) The obtained dataset is then clustered using
the Fuzzy-C Means technique before being fed to a neural network model for better results.4) And finally, the recorded EEG
segment will be classified into different epilepsy classifications using ANN. The study's findings show that the proposed
clustering approach is highly effective in epilepsy classification, with an accuracy of 99% which is significantly higher than
many existing methods.

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