Classification of Enchepalo Graph (EEG) Signals for Epilepsy Using Discrete Wavelet Transform and K-Nearest Neighbor Methods Klasifikasi Sinyal Enchepalo Graph (EEG) untuk Epilepsi Menggunakan Metode Discrete Wavelet Transform dan K-Nearest Neighbor

Main Article Content

Maulana Angga Pribadi


Epileps is a disorder of the contents of the nervous system of the human brain resulting in the presence of abnormal activity that is the excessive activity of neuron cells in the brain. In Indonesia there are more than 1,400,000 cases of Epilepsy each year with 70,000 additional cases each year. About 4050% occurs in children. A widely used method for assessing brain activity is through a sephalogram (EEG) Electrone signal. The Epilepsy classification system is built with extraction and identifikas stages. Wavelet exctraction is suitable for non-stationary signal analysis such as EEG signals. Wavelet tranformation can extract signal components only at the required frequency. So that it can also reduce the amount of data but without losing meaningful information. But to make it work and can be used on a system needs to be done classification in order to be able to distinguish between commands from each other. So it is used K-Nearest Neighbour (K-NN) classification method so that the signal that has been eliminated buzz can be directly entered into the classification to determine the correct wrongness of a data. In this study obtained the results of data accuracy value that K = 1 has the largest percent of 100% and the smallest percent is found in K = 7 and K = 11 namely 14.2% and 18.2% it is caused by the presence of classes that do not match the test data so as to reduce the percentage of accuracy in the K.


Download data is not yet available.

Article Details

How to Cite
M. A. Pribadi, “Classification of Enchepalo Graph (EEG) Signals for Epilepsy Using Discrete Wavelet Transform and K-Nearest Neighbor Methods”, PELS, vol. 1, no. 1, Mar. 2021.
Electrical Engineering


[1] Eric R. Kandel, Larryi R.Squire. (2000). Neuroscience : Breaking Down Scientific Barries to the study of brain and mind. Journal Science. 290:1113-1120
[2] Campellone, JV (2006). Eeg Brain Wave Test di diakses pada tanggal 1 oktober 2016.
[3] Wikipedia. 2007. Signal di Diakses pada tanggal 23 Oktober 2018.
[4] Kumar, Sandeep. (2017). "EEGi Signali Classification Using PSO Trained RBF Neural Network for Epilepsy Identification.". Journal Informatics in Medicine Unlocked.
[5] Wikipedia. 2018. Electroencephalography di Diakses pada tanggal 23 Oktober 2018 .
[6] Chaovalit,P., Gangopadhyay,A., Karabatis,G., & Chen,Z.(2011). Discrete wavelet transform-based time series analysis and mining. ACM Computing Surveys, 43(2), 1–37.
[7] Percival, Donald B; Walden, A. T. (2006). the Discrete Wavelet Transform and Consider, Inverse Tranform. Cambridge University Press.
[8] Vasavi, K. P., Raju, P. R. S. S. V., Radhika, S., & Prasad, G. D. K. (2016). A Mind Operated Computer Mouse Using Discrete Wavelet Transforms for Elderly People with Multiple Disabilities. Procedia Computer Science, 85(Cms), 166–175.
[9] Mathworks. 1994. Whati isi MATLAB di matlab. Diakses pada Oktoberi 2018.
[10] Mathworks. 2007. Matlab GUI di gui.html. Di akses pada tanggal 1 November 2018.