Sushmitha N, Ramakanth Kumar P., Sudarshan. B. G,



Epilepsy Detection Using EEG Signal and Machine Learning Classifiers: A Survey

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Epilepsy is a critical neurological disorder caused by abnormal function of the neurons resulting in unusual behaviour of the patients sometimes at its worst phase results in recurrent seizers and unconsciousness of the patients. Neurons are connected in the complex way and carry the information across different neurons controlling all the organs of the human body. Using electrical and chemical signals, they help to coordinate all the necessary functions of life. The effective tool used to monitor these brain signal in medical diagnosis to detect any sizers is Electroencephalogram (EEG). These signals are complex, noisy, non-linear, non-stationary and produce a high volume of data. Hence, the detection of seizures and discovery of the brain-related knowledge is a challenging task. Machine learning classifiers are able to classify EEG data and detect seizures along with revealing relevant sensible patterns without compromising performance. As such, various researchers have developed number of approaches to seizure detection using machine learning classifiers and statistical features. The main challenges are selecting appropriate classifiers and features. The aim of this paper is to present an overview of different types of machine learning classifiers used in detection of epilepsy using EEG signals.


Epilepsy, seizure, Electroencephalogram, machine learning, classifiers


Cite this paper

Sushmitha N, Ramakanth Kumar P., Sudarshan. B. G,. (2022) Epilepsy Detection Using EEG Signal and Machine Learning Classifiers: A Survey. International Journal of Education and Learning Systems, 7, 128-131


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