International Journal of Engineering Research in Electronics and Communication Engineering(IJERECE)

Monthly Journal for Electronics and Communication Engineering

Volume3,May 2016,

Topic : Identification of Seizures in EEG Waves using Determinant Analysis

Authors:Sharanya A R || Chethana K S

Abstract:Electroencephalogram (EEG) signals are electrical signals that are recorded from the scalp. They can be used to carry out the analysis and identification of brain disorders. EEG signal are non-stationary. Feature extraction of EEG always remains as a major because of the non stationary behavior along with their accurate classification. Support vector machines are supervised learning models used for classification & regression analysis. LDA classifiers are also used for classification. The main objective of the proposed work is to identify the seizures in the EEG waves. Real time databases are collected and filtered to remove the noise. Each EEG channel is normalized & trained using Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) classifiers to locate the seizures. Their results are compared based on their performance..

Keywords: Electroencephalogram, Seizures, Classification, Support Vector Machine, Linear Discriminant Analysis

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DOI: 01.1617/vol3iss5pid227


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Editor-in-Chief

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Dr.Zhongfu Tan
National Professor,
North China Electric Power University,
CHINA


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IMPACT FACTOR: 3.689

ISSN(Online): 2394-6849

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Thomson Reuters ID : q-6288-2016.
ORCiD Research ID : 0000-0001-9540-6799

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