HFD and MCFET Based Feature Extraction Technique for Detection of Epilepsy Using ANN Classifier

A neurological disorder called Epilepsy which causes the sudden occurrence of epileptic seizures. The electroencephalogram (EEG) is recorded electrical activities brain to examine patient through EEG pattern for diagnosis. Epileptic seizure one abnormality or in patterns shows large spikes specific time domain area. This work mainly focused on detecting seizures extracted feature like Higuchi Fractal Dimension (HFD) and Masking Check-in based extraction technique (MCFET). Three scaling features HFD viz. fractal dimension, standard deviation dimension factor while twenty masking check-in-based upper lower envelope along with ten Discrete Wavelet Transform (DWT) coefficients (Table 1) from raw signals are required as input Artificial Neural Network (ANN) classifications. overall performance improved terms Accuracy, Sensitivity, Specificity both MCFET features. Further, accuracy using around 98% a bit computational about 1 second by reducing training percent 80% 60%.