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Analysis of EEG signals using Machine Learning for the Detection and Diagnosis of Epilepsy

Authors: Anubha Nagar; Bidushi; Mimangsha Sarma; Mithra Anand Kumar; J.Valarmathi;

Analysis of EEG signals using Machine Learning for the Detection and Diagnosis of Epilepsy

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

Subjects by Vocabulary

Microsoft Academic Graph classification: medicine.diagnostic_test Computer science Speech recognition Electroencephalography medicine.disease Epilepsy medicine

Keywords

Environmental Engineering, General Engineering, Computer Science Applications, Epilepsy, EEG, Decision Tree model, Logistic regression, seizures.

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citations
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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