Fuzzy C-Means Clustering and Gaussian Mixture Model for Epilepsy Classification from EEG

  • Harikumar RajaguruEmail author
  • Sunil Kumar Prabhakar
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 30)


Due to various disorders in the functionality of the brain, epileptic seizures occur and it affects the patient’s mental, physical and emotional health to a great extent. The prediction of epileptic seizures before the beginning of the onset is pretty useful for seizure prevention by medication. One of the major causes for epilepsy is molecular mutation which results in irregular behaviour of neurons. Though the exact reasons for epilepsy are not known, early diagnosis is very useful for the treatment of epilepsy. Various computational techniques and machine learning algorithms are utilized to classify epilepsy from Electroencephalography (EEG) signals. In this paper, Fuzzy C-Means (FCM) Clustering algorithm is used as a clustering technique initially and then the features obtained through it is classified with the help of Gaussian Mixture Model (GMM) used as a post-classification technique. Results report that an average classification accuracy of 97.64% along with an average performance index of 95.01% is obtained successfully.


FCM GMM Epilepsy EEG 



The authors are grateful to Dr. Asokan, Neurologist, Ramakrishna Hospital Coimbatore and Dr. B. Rajalakshmi, Diabetologist, Govt. Hospital Dindigul for providing the EEG signals.


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of ECEBannari Amman Institute of TechnologySathyamangalamIndia

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