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Electroencephalogram Signals Processing for the Diagnosis of Petit mal and Grand mal Epilepsies Using an Artificial Neural Network

Author(s): M. R. Arab | A. A. Suratgar | V. M. Martínez‐Hernández | A. Rezaei Ashtiani

Journal: Journal of Applied Research and Technology
ISSN 1665-6423

Volume: 8;
Issue: 1;
Start page: 120;
Date: 2010;
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Keywords: Tonic‐clonic epilepsy | petit mal epilepsy | Continuous Wavelet Transform (CWT) | absence epilepsy

In this study, a novel wavelet transform‐neural network method is presented. The presented method is used for theclassification of grand mal (clonic stage) and petit mal (absence) epilepsies into healthy, ictal and interictal (EEGs). Preprocessingis included to remove an artifact occurred by blinking and a wandering baseline (electrodes movement) as well as an eyeballmovement artifact using the Discrete Wavelet Transformation (DWT). Denoising EEG signals from the AC power supplyfrequency with a suitable notch filter is another job of preprocessing. The preprocessing enhanced speed and accuracy of theprocessing stage (wavelet transform and neural network). The EEGs signals are categorized into normal and petit mal and clonicepilepsy by an expert neurologist. The categorization is confirmed by the Fast Fourier Transform (FFT) analysis. The datasetincludes waves such as sharp, spike and spike‐slow wave. Through the Countinous Wavelet Transform (CWT) of EEG records,transient features are accurately captured and separated and used as classifier input. We introduce a two‐stage classifier basedon the Learning Vector Quantization (LVQ) neural network localized in both time and frequency contexts. The particularcoefficients of the Continuous Wavelet Transform (CWT) are networks. The simulation results are very promising and theaccuracy of the proposed method obtained is of about 80%.
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