KLASIFIKASI DATA EEG UNTUK MENDETEKSI KEADAAN TIDUR DAN BANGUN MENGGUNAKAN AUTOREGRESSIVE MODEL DAN SUPPORT VECTOR MACHINE
Abstract
Tidur merupakan kebutuhan dasar manusia. Salah satu gangguan tidur yang cukup berbahaya adalah narkolepsi, yaitu gangguan tidur kronis yang ditandai dengan rasa kantuk yang luar biasa di siang hari dan serangan tidur yang terjadi secara tiba-tiba. Salah satu metode dokter untuk mendiagnosis penyakit narkolepsi adalah dengan melihat aktivitas gelombang otak (melalui sinyal EEG) pasien. Penelitian ini bertujuan untuk mengembangkan perangkat lunak yang dapat mengklasifikasikan keadaan tidur dan bangun melalui sinyal EEG secara otomatis. Dataset EEG yang digunakan tersedia di Physionet. Pertama-tama data EEG yang menjadi masukan dilakukan normalisasi dan filtering. Proses filtering dilakukan untuk membagi data menjadi 3 subband yaitu theta, alpha, dan beta. Setelah itu pada masing-masing subband dilakukan tahap ekstraksi fitur menggunakan Autoregressive Model. Hasil estimasi koefisien AR model digunakan sebagai fitur. Metode yang digunakan untuk mengestimasi koefisien AR model yaitu metode Yule-Walker dan metode Burg. Dataset dibagi menjadi data latih dan data uji menggunakan 10-fold cross validation. Data training digunakan untuk membuat SVM Model. SVM Model digunakan untuk mengklasifikasikan data testing sehingga menghasilkan keluaran label 1 untuk tidur dan label 0 untuk bangun. Untuk menentukan kelas final dilakukan majority vote dari hasil klasifikasi masing-masing subband. Performa sistem diperoleh dengan menghitung akurasi, presisi, dan sensitivitas pada setiap skenario uji coba. Skenario uji coba yang dilakukan antara lain dengan memvariasikan order AR, fungsi kernel, dan parameter C pada SVM. Dari hasil uji coba yang dilakukan, metode Yule-Walker menghasilkan rata-rata akurasi 80.60%, presisi 78.19%, dan sensitivitas 77.56%. Metode Burg menghasilkan akurasi 94.01%, presisi 95.70%, dan sensitivitas 93.39%. Hasil tersebut menunjukkan metode Burg memiliki performa lebih baik dibandingan dengan metode Yule-Walker.Downloads
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References
[1] C. R. Burgess and T. E. Scammell, "Narcolepsy: Neural Mechanisms of Sleepiness and Cataplexy," The Journal of Neuroscience, 2012.
[2] M. Telpan, "Fundamental of EEG Measurement," Measurement Science Review, Vols. 2, Section 2, 2002.
[3] A. H. Brilian, H. Tjandrasa and C. Fatichah, "Pengenalan Sandi Morse dari Sinyal Electroencephalogram yang Direkam Perangkat Neurosky Mindwave Menggunakan Dinamic Time Warping," JUTI, vol. 14, pp. 63-71, 2016.
[4] H. Tjandrasa and S. Djanali, "Classification of EEG Signals Using Single Channel Independent Component Analysis, Power Spectrum, and Linear," Lecture Notes in Electrical Engineering (LNEE), Springer, vol. 387, pp. 259-268, 2016.
[5] A. Rechtschaffen and A. Kales, A Manual of Standardized Terminology Techniques and Scoring System for Sleep Stages of Human Subjects, Los Angeles, USA: Brain Research Institute, UCLA, 1968.
[6] M. S. Scher, "Automated EEG-sleep analyses and neonatal neurointensive care," Sleep Medicine, vol. 5, pp. 533-540, 2004.
[7] K. A. I. Aboalayon, H. T. Ocbagabir and M. Faezipour, "Efficient Sleep Stage Classification Based on EEG Signals," Institiute of Electrical and Electronics Engineers, 2014.
[8] T. Kayikcioglu, M. Maleki and K. Eroglu, "Fast and Accurate PLS-Based Classification of EEG Sleep Using Single Channel Data," Expert System with Applications, pp. 7825-7830, 2015.
[9] G. J. McLachlan, K.-A. Do and C. Ambroise, Analyzing Microarray Gene Expression Data, Wiley, 2004.
[10] B. A. Shenoi, Introduction To Digital Signal Processing and Filter Design, New Jersey: Wiley Interscience, 2006.
[11] J. G. Proakis and D. G. Manolakis, Digital Signal Processing, Upper Saddle River, New Jersey: Prentice Hall, 1996.
[12] M. H. hayes, Statistical Digital Signal Processing and Modeling, New York: John Wiley & Sons, 1996.
[13] P. Stoica and R. L. Moses, Introduction of Spectral Analysis, Upper Saddle River, New Jersey: Prentice-Hall, 1997.
[14] K. Kazlauskas, "The Burg Algorithm with Extrapolation for Improving the Frequency Estimation," INFORMATICA, pp. 177-188, 2011.
[15] A. S. Nugroho, A. B. Witarto and D. Handoko, "Support Vector Machine-Teori dan Aplikasinya dalam Bioinformatika," IlmuKomputer.Com, 2003.
[2] M. Telpan, "Fundamental of EEG Measurement," Measurement Science Review, Vols. 2, Section 2, 2002.
[3] A. H. Brilian, H. Tjandrasa and C. Fatichah, "Pengenalan Sandi Morse dari Sinyal Electroencephalogram yang Direkam Perangkat Neurosky Mindwave Menggunakan Dinamic Time Warping," JUTI, vol. 14, pp. 63-71, 2016.
[4] H. Tjandrasa and S. Djanali, "Classification of EEG Signals Using Single Channel Independent Component Analysis, Power Spectrum, and Linear," Lecture Notes in Electrical Engineering (LNEE), Springer, vol. 387, pp. 259-268, 2016.
[5] A. Rechtschaffen and A. Kales, A Manual of Standardized Terminology Techniques and Scoring System for Sleep Stages of Human Subjects, Los Angeles, USA: Brain Research Institute, UCLA, 1968.
[6] M. S. Scher, "Automated EEG-sleep analyses and neonatal neurointensive care," Sleep Medicine, vol. 5, pp. 533-540, 2004.
[7] K. A. I. Aboalayon, H. T. Ocbagabir and M. Faezipour, "Efficient Sleep Stage Classification Based on EEG Signals," Institiute of Electrical and Electronics Engineers, 2014.
[8] T. Kayikcioglu, M. Maleki and K. Eroglu, "Fast and Accurate PLS-Based Classification of EEG Sleep Using Single Channel Data," Expert System with Applications, pp. 7825-7830, 2015.
[9] G. J. McLachlan, K.-A. Do and C. Ambroise, Analyzing Microarray Gene Expression Data, Wiley, 2004.
[10] B. A. Shenoi, Introduction To Digital Signal Processing and Filter Design, New Jersey: Wiley Interscience, 2006.
[11] J. G. Proakis and D. G. Manolakis, Digital Signal Processing, Upper Saddle River, New Jersey: Prentice Hall, 1996.
[12] M. H. hayes, Statistical Digital Signal Processing and Modeling, New York: John Wiley & Sons, 1996.
[13] P. Stoica and R. L. Moses, Introduction of Spectral Analysis, Upper Saddle River, New Jersey: Prentice-Hall, 1997.
[14] K. Kazlauskas, "The Burg Algorithm with Extrapolation for Improving the Frequency Estimation," INFORMATICA, pp. 177-188, 2011.
[15] A. S. Nugroho, A. B. Witarto and D. Handoko, "Support Vector Machine-Teori dan Aplikasinya dalam Bioinformatika," IlmuKomputer.Com, 2003.
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Published
2017-01-01
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How to Cite
[1]
Y. H. Mahendra, H. Tjandrasa, and C. Fatichah, “KLASIFIKASI DATA EEG UNTUK MENDETEKSI KEADAAN TIDUR DAN BANGUN MENGGUNAKAN AUTOREGRESSIVE MODEL DAN SUPPORT VECTOR MACHINE”, JUTI, vol. 15, no. 1, pp. 35–42, Jan. 2017, doi: 10.12962/j24068535.v15i1.a633.
