Fuzzy C-Means Clustering and Gaussian Mixture Model for Epilepsy Classification from EEG
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.
KeywordsFCM 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.
- 2.Rajaguru H, Prabhakar SK (2016) A framework for epilepsy classification using modified sparse representation classifiers and native bayesian classifier from EEG signals. J Med Imaging Health InfGoogle Scholar
- 3.Prabhakar SK, Rajaguru H (2015) Analysis of centre tendency mode chaotic modeling for electroencephalography signals obtained from an epileptic patient. Adv Stud Theor Phys 9(4): 171–177, HIKARI Ltd., http://dx.doi.org/10.12988/astp.2015.5117
- 4.Harikumar R, Kumar PS (2015) Frequency behaviors of electroencephalography signals in epileptic patients from a wavelet thresholding perspective. Appl Math Sci 9(50): 2451–2457, HIKARI Ltd., http://dx.doi.org/10.12988/ams.2015.52135
- 5.Prabhakar SK, Rajaguru H (2016) Classification of epilepsy risk using variable thresholding based feature extraction technique and suitable post classifiers. Int J Simul Syst Sci Technol (IJSSST) 17(33): 28.1–28.8Google Scholar
- 6.Rajaguru H, Prabhakar SK (2017) Analysis of probabilistic neural networks with dimensionality reduction for epilepsy classification from EEG. Int J Mech Eng TechnolGoogle Scholar
- 9.Prabhakar SK, Rajaguru H (2017) Adaboost classifier with dimensionality reduction techniques for epilepsy classification from EEG. In: International conference on biomedical and health informatics. Thessaloniki, Greece, 18–21 November 2017Google Scholar
- 12.Prabhakar SK, Rajaguru H (2017) Conceptual analysis of epilepsy classification using probabilistic mixture models In: 5th IEEE winter international conference on brain-computer interface. South Korea, 9–11 January 2017Google Scholar
- 15.Prabhakar SK, Rajaguru H (2016) Efficient wireless system for telemedicine application with reduced PAPR using QMF based PTS technique for epilepsy classification from EEG signals In: IFBME proceedings (Springer), international conference on advancements of medicine and health care through technology (MEDITECH), Romania, 12–15 October 2016Google Scholar
- 16.Saha I, Maulik U, Bandyopadhyay S (2009) A new differential evolution based fuzzy clustering for automatic cluster evolution. In: Proceedings of the IEEE international advance computing conference (IACC’09), IEEE, Patiala, India, 706–711 March 2009Google Scholar
- 17.Prabhakar SK, Rajaguru H (2016) Performance analysis of GMM classifier for classification of normal and abnormal segments in PPG signals In: 16th international conference on biomedical engineering (ICBME), Singapore, 7–10 December 2016Google Scholar