Classification of Epileptiform Waves Based on Frequency by Using Backpropagation Neural Network

Epilepsi is an abnormal condition of brain activity that can be recorded by using Elecktroencephalography (EEG). On epilepsy patient, most of the recording is interictal wave that in form of spike wave and sharp wave. This study has goal to classify whether the interictal waves are spike wave or sharp wave. The study was conducted in two stages Identification and Classification. Firstly, The epileptogenik wave were identified by shifting the baseline of each wave to select the best baseline that contain all data of the wave, then doing normalization of it to get the features of frequency, amplitude 1 and amplitude 2. Secondly, Back propagation Neural Network method is applied to classify it. Classification is done by using 200 data consisting of 120 training data and 80 testing data. The results show that classification using binary sigmoid activation function give same recognition rate of 91,25 % for all variation of learning rate.