KOMPARASI METODE SCICA DAN WICA PADA PRAPROSES DATA EEG OTAK MANUSIA UNTUK DETEKSI PENYAKIT EPILEPSI

Authors

  • Aditya Bagusmulya Jurusan Teknik Informatika, Fakultas Teknologi Informasi, Institut Teknologi Sepuluh Nopember
  • Handayani Tjandrasa Jurusan Teknik Informatika, Fakultas Teknologi Informasi, Institut Teknologi Sepuluh Nopember
  • Chastine Fatichah Jurusan Teknik Informatika, Fakultas Teknologi Informasi, Institut Teknologi Sepuluh Nopember
Views: 926 Downloads: 671

DOI:

https://doi.org/10.12962/j24068535.v14i2.a564

Abstract

Epilepsi merupakan salah satu kelainan pada otak manusia yang tidak dapat disembuhkan. Penyakit ini menimbulkan kejang pada tubuh dan sangat mengganggu aktivitas. Pada tingkat yang parah, epilepsi dapat membahayakan nyawa penderitanya. Oleh sebab itu, epilepsi harus dideteksi secara dini agar penderita segera mendapatkan penanganan yang tepat sehingga keadaannya tidak memburuk. Pada penelitian ini, deteksi epilepsi dilakukan dengan menggunakan beberapa metode, yaitu Independent Component Analysis (ICA), Wavelet Transform (WT), dan Multilayer Perceptron (MLP). Hasil deteksi diklasifikasikan ke dalam tiga kelas, yaitu normal, epilesi tidak kejang, dan epilepsi kejang. Data rekaman electroencephalogram (EEG) yang digunakan berasal dari ''Klinik für Epileptologie, Universität Bonn” yang diperoleh secara online. Data tersebut merupakan EEG single channel sehingga harus menggunakan teknik-teknik ICA untuk single channel, seperti Single Channel Independent Component Analysis (SCICA) dan Wavelet Independent Component Analysis (WICA). Penelitian ini membandingkan kedua teknik tersebut dalam melakukan praproses data sehingga akan terlihat teknik mana yang lebih baik. Hasil pendeteksian terbaik dihasilkan dari model yang menggunakan teknik SCICA sebagai penghilang derau dan ektraksi fitur Discrete Wavelet Transform Daubechies 6 dengan 4 level. Berdasarkan uji coba, metode tersebut menghasilkan akurasi sebesar 92.09%.

Downloads

Download data is not yet available.

References

[1] World Health Organization, Oktober 2012. [Online]. Available: http://www.who.int/mediacentre/factsheets/fs999/en

[2] “Epilepsy: social consequences and economic aspects,” [Online]. Available: http://www.allcountries.org/health/epilepsy_social_consequences_and_economic_aspects.html. [Diakses 3 Juni 2015].

[3] R. Harikumar dan T. Vijayakumar, “Performance Analysis of Patient Specific Elmanchaotic Optimization Model for Fuzzy Based Epilepsy Risk Level Classification from EEG Signals,” International Journal on Smart Sensing and Intelligent Systems, Vol. 2, p. 612, 2009.

[4] N. Nicolau dan J. Gergiou, “Detection of Epileptic Electroencephalogram Based on Permutation Entropy and Support Vector Machines,” 2012.

[5] Riwinoto dan B. Kusumoputro, “Penggunaan Independent Component Analysis (ICA) untuk Pembuangan Noise dan Artefak pada Sinyal Campuran,” National Conference: Design and Application of Technology, 2010.

[6] W. Zhou dan J. Gotman, “Automatic Removal of Eye Movement Artifacts from the EEG Using ICA and the Dipole Model,” Progress in Natural Science, vol. 19, p. 1165, 2009.

[7] M. Sheoran, S. Kumar dan A. Kumar, “Wavelet-ICA based Denoising of Electroencephalogram Signal,” International Journal of Information & Computation Technology, vol. 4, p. 1205, 2014.

[8] B. Mijovic, M. De Vos, I. Gligorijevic, J. Taelman dan S. Van Huffel, “Source Separation From Single-Channel Recordings by Combining Empirical-Mode Decomposition and Independent Component Analysis,” IEEE Transactions on Biomedical Engineering, vol. 57, p. 2189, 2010.

[9] H. Susanto, “Transformasi Wavelet Haar,” 10 Maret 2010. [Online]. Available: http://www.scribd.com/doc/50467423/Transformasi-Wavelet-Haar.

[10] G. Panchal, A. Ganatra, Y. P Kosta dan D. Panchal, “Behaviour Analysis of Multilayer Perceptron with Multiple Hidden Neurons an Hidden Layer,” International Journal of Computer Theory and Engineering, Vol. 1 dari 23, No. 2, p. 333, 2011.

[11] [Online]. Available: http://www.compumine.com/web/public/newsletter/20071/precision-recall. [Diakses 5 Mei 2015].

[12] A. Quotb, Y. Bornat dan S. Renaud, “Wavelet transform for real-time detection of action potentials in neural signals,” 15 Juli 2011. [Online]. Available: http://journal.frontiersin.org/article/10.3389/fneng.2011.00007/full. [Diakses 3 Juni 2015].

[13] C. W. Dawson, R. L. Wilby, C. Harpham, M. R. Brown, E. Cranston dan E. J. Darby, “Modelling Ranunculus Presence in the Rivers Test and Itchen Using Artificial Neural Networks,” [Online]. Available: http://www.geocomputation.org/2000/GC016/Gc016.htm. [Diakses 20 Januari 2015].

[14] P. Jahankhani, V. Kodogiannis dan K. Revett, “EEG Signal Classification Using Wavelet Feature Extraction and Neural Networks,” International Symposium on Modern Computing, p. 52, 2006.

Downloads

Published

2016-07-01

Issue

Vol 14, No. 2, Juli 2016

Section

Articles

How to Cite

[1]
A. Bagusmulya, H. Tjandrasa, and C. Fatichah, “KOMPARASI METODE SCICA DAN WICA PADA PRAPROSES DATA EEG OTAK MANUSIA UNTUK DETEKSI PENYAKIT EPILEPSI”, JUTI, vol. 14, no. 2, pp. 127–134, Jul. 2016, doi: 10.12962/j24068535.v14i2.a564.

License

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in JUTI unless they receive approval for doing so from the Editor-in-Chief.

JUTI open access articles are distributed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.