DETEKSI EPILEPSI DENGAN PCA - Siswandari Noertjahjani; Aris Kiswanto; Heri Dwi Santosa (PROSIDING SEMINAR NASIONAL & INTERNASIONAL, 2017)
The main purpose of this study is to early detection of symptoms of epilepsy symptoms on the introduction of normal EEG signaling patterns with epilepsy (abnormal) EEG signals. There are 5 characteristics of statistics used are mean, variant, kurtosis, entropy, skweness. Electrodes used in EEGs usually have 19 channels: FP1, FP2, F7, F3, F2, F4, F8, C3, CZ, C4, P3, P4, PZ, O1 and OZ. While in this research only use FP1 electrode with 2 second signal cutting. Extraction of normal wave characteristics and epilepsy using PCA (principle componen analysis). PCA method is very appropriate to use if the existing datahas a large number of variables and has a correlation between variables such as EEG signals.  The calculation of the principal component analysis is based on the calculation of eigenvalues and eigenvectorsexpressing the dissemination of data from a dataset and capable of reducing the high dimension to a low dimension, without losing the information contained in the original data.Keywords-epilepsy, EEG, FP1