EEG CLASSIFICATION FOR EPILEPSY BASED ON WAVELET PACKET DECOMPOSITION AND RANDOM FOREST

Authors

  • Yuna Sugianela Sepuluh Nopember Institute of Technology
  • Qonita Luthfia Sutino
  • Darlis Herumurti

DOI:

https://doi.org/10.21609/jiki.v11i1.549

Keywords:

EEG, epilepsy, seizure, wavelet, random forest

Abstract

EEG (electroencephalogram) can detect epileptic seizures by neurophysiologists in clinical practice with visually scan long recordings. Epilepsy seizure is a condition of brain disorder with chronic noncommunicable that affects people of all ages. The challenge of study is how to develop a method for signal processing that extract the subtle information of EEG and use it for automating the detection of epileptic with high accuration, so we can use it for monitoring and treatment the epileptic patient. In this study we developed a method to classify the EEG signal based on Wavelet Packet Decomposition that decompose the EEG signal and Random Forest for seizure detetion. The result of study shows that Random Forest classification has the best performance than KNN, ANN, and SVM. The best combination of statisctical features is standard deviation, maximum and minimum value, and bandpower. WPD is has best decomposition in 5th level.

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Published

2018-02-28

How to Cite

Sugianela, Y., Sutino, Q. L., & Herumurti, D. (2018). EEG CLASSIFICATION FOR EPILEPSY BASED ON WAVELET PACKET DECOMPOSITION AND RANDOM FOREST. Jurnal Ilmu Komputer Dan Informasi, 11(1), 27–33. https://doi.org/10.21609/jiki.v11i1.549

Issue

Vol. 11 No. 1 (2018): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information)

Section

Articles

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