Detection of seizure activity in EEG by an artificial neural network: a preliminary study
Computers and Biomedical Research
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In this study, topographic brain mapping and wavelet transform-neural network method are used for the classification of grand mal (clonic stage) and petit mal (absence) epilepsies into healthy, ictal and interictal (EEGs). Preprocessing is included to remove artifacts occurred by blinking, wandering baseline (electrodes movement) and eyeball movement using the Discrete Wavelet Transformation (DWT). De-noising EEG signals from the AC power supply frequency with a suitable notch filter is another job of preprocessing. In experimental data, the preprocessing enhanced speed and accuracy of the processing stage (wavelet transform and neural network). The EEGs signals are categorized to normal and petit mal and clonic epilepsy by an expert neurologist. The categorization is confirmed by Fast Fourier Transform (FFT) analysis and brain mapping. The dataset includes waves such as sharp, spike and spike-slow wave. Through the Counties Wavelet Transform (CWT) of EEG records, transient features are accurately captured and separated and used as classifier input. We introduce a two-stage classifier based on the Learning Vector Quantization (LVQ) neural network location in both time and frequency contexts. The brain mapping used for finding the epilepsy locates in the brain. The simulation results are very promising and the accuracy of the proposed classifier in experimental clinical data is ~80%.