Analysis of EEG signals using Machine Learning for the Detection and Diagnosis of Epilepsy
Anubha Nagar1, Bidushi2, Mimangsha Sarma3, Mithra Anand Kumar4, J.Valarmathi5
1Anubha Nagar*, Department of Electronics and Communication, Vellore Institute of Technology, Tamil Nadu, India.
2Bidushi, Department of Electronics and Communication, Vellore Institute of Technology, Tamil Nadu, India.
3Mimangsha Sarma, Department of Electronics and Communication, Vellore Institute of Technology, Tamil Nadu, India.
4Mithra Anand Kumar, Department of Electronics and Communication, Vellore Institute of Technology, Tamil Nadu, India.
5Dr. Valarmathi J., Professor, Vellore Institute of Technology, Tamil Nadu, India.
Manuscript received on September 02, 2020. | Revised Manuscript received on September 15, 2020. | Manuscript published on October 30, 2020. | PP: 89-93 | Volume-10 Issue-1, October 2020. | Retrieval Number: 100.1/ijeat.A16851010120 | DOI: 10.35940/ijeat.A1685.1010120
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Electroencephalogram (EEG) is one of the most commonly used tools for epilepsy detection. In this paper we have presented two methods for the diagnosis of epilepsy using machine learning techniques. EEG waveforms have five different kinds of frequency bands. Out of which only two namely theta and gamma bands carry epileptic seizure information. Our model determines the statistical features like mean, variance, maximum, minimum, kurtosis, and skewness from the raw data set. This reduces the mathematical complexities and time consumption of the feature extraction method. It then uses a Logistic regression model and decision tree model to classify whether a person is epileptic or not. After the implementation of the machine learning models, parameters like accuracy, sensitivity, and recall have been found. The results for the same are analyzed in detail in this paper. Epileptic seizures cause severe damage to the brain which affects the health of a person. Our key objective from this paper is to help in the early prediction and detection of epilepsy so that preventive interventions can be provided and precautionary measures are taken to prevent the patient from suffering any severe damage.
Keywords: Epilepsy, EEG, Decision Tree model, Logistic regression, seizures.
Scope of the Article: Machine Learning