Feature Extraction and Classification of Epileptic EEG Signals Using Wavelet Transforms and Artificial Neural Networks
Marked by unpredictable seizures, epilepsy is the fourth most prevailing chronic neural disorder. This neurodegenerative disorder can attack individuals belonging to any category or age group. Also, the resulting seizures can be of any type. There is always a possibility of misjudging the symptoms with psychogenic nonepileptic events. Thus, in addition to the common methods like functional magnetic resonance imaging (fMRI) and positron emission tomography (PET), electroencephalography (EEG) is a useful tool to differentiate epilepsy from other neurodegenerative disorders. However, EEG measures brain activity directly unlike the other two techniques that measure changes in blood flow to a certain part of the brain. Hence, EEG is most widely used. The paper focuses on conversion of time domain brain signals into time–frequency domain using wavelet transforms followed by extraction of various statistical and nonlinear features. These features are then fed to the neurons of an artificial neural network (ANN) which indicates the presence of epilepsy in an individual.
KeywordsArtificial neural networks (ANN) Confusion matrix Discrete wavelet transforms (DWT) Epilepsy Electroencephalography (EEG)
Authors would like to thank the Centre of Epilepsy in Bonn, Germany for the data provided.
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