Implementasi deteksi serangan epilepsi dari data rekaman EEG menggunakan Weighted Permutation Entropy dan Support Vector Machine.

Lophita Y Napitupulu, Nainik Suciati, Dini Adni Navastara
Submission Date: 2017-07-24 11:27:07
Accepted Date: 2018-01-09 21:27:31

Abstract


Epilepsi merupakan gangguan neurologis jangka panjang yang ditandai dengan serangan-serangan epileptik. Serangan epileptik dapat terjadi dalam waktu singkat hingga guncangan kuat dalam waktu yang lama. Epilepsi adalah penyakit yang cenderung terjadi secara berulang dan tidak dapat disembuhkan, namun serangan-serangan epileptik yang terjadi dapat dikontrol melalui pengobatan.

Pada tugas akhir ini, data rekaman electroencephalogram (EEG) dibagi menjadi beberapa window menggunakan segmentasi atau dekomposisi. Proses selanjutnya adalah mengekstraksi setiap window dengan menggunakan Weighted Permutation Entropy yang menghasilkan satu fitur setiap window. Uji coba fitur menggunakan k-fold cross-validation dengan membagi data menjadi data training dan data testing. Selanjutnya data diklasifikasi menggunakan Support Vector Machine. Data rekaman EEG yang digunakan untuk pengujian ini berasal dari ''Klinik für Epileptologie, Universität Bonn” yang diperoleh secara online yang berjumlah 500 data. Data ini terdiri dari serangan epilepsi (set S) dan bukan serangan epilepsi (set Z, N, O, F) yang masing-masing set terdiri dari 100 data.

Uji coba dilakukan pada data set S digabung dengan setiap set lain. Sehingga data yang digunakan sebanyak 200 data rekaman EEG untuk setiap uji coba. Berdasarkan uji coba, metode tersebut menghasilkan akurasi rata-rata sebesar 91,88%.

Keywords


Epilepsi, EEG, Weighted Permutation Entropy, Support Vector Machine.

References


N. S. Tawfik, S. M. Youssef, dan M. Kholief, “A hybrid automated detection of epileptic seizures in EEG records,” Comput. Electr. Eng., vol. 53, hal. 177–190, Jul 2016.

[2] “Epilepsi,” Wikipedia bahasa Indonesia, ensiklopedia bebas. 30-Okt-2016.

[3] S. B. Wilson, M. L. Scheuer, C. Plummer, B. Young, dan S. Pacia, “Seizure detection: correlation of human experts,” Clin. Neurophysiol. Off. J. Int. Fed. Clin. Neurophysiol., vol. 114, no. 11, hal. 2156–2164, Nov 2003.

[4] “WHO | Epilepsy,” WHO. [Daring]. Tersedia pada: http://www.who.int/mediacentre/factsheets/fs999/en/. [Diakses: 30-Mei-2017].

[5] N. Nicolaou dan J. Georgiou, “Detection of epileptic electroencephalogram based on Permutation Entropy and Support Vector Machines,” Expert Syst. Appl., vol. 39, no. 1, hal. 202–209, Jan 2012.

[6] A. T. Tzallas, M. G. Tsipouras, dan D. I. Fotiadis, “The use of time-frequency distributions for epileptic seizure detection in EEG recordings,” Conf. Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. IEEE Eng. Med. Biol. Soc. Annu. Conf., vol. 2007, hal. 3–6, 2007.

[7] M. Teplan, “FUNDAMENTALS OF EEG MEASUREMENT,” vol. 2, 2002.

[8] H. Azami, K. Mohammadi, dan B. Bozorgtabar, “An Improved Signal Segmentation Using Moving Average and Savitzky-Golay Filter,” J. Signal Inf. Process., vol. 03, no. 01, hal. 39, Feb 2012.

[9] J. M. Barreiro, F. Martin-Sanchez, V. Maojo, dan F. Sanz, Biological and Medical Data Analysis: 5th International Symposium, ISBMDA 2004, Barcelona, Spain, November 18-19, 2004, Proceedings. Springer, 2004.

[10] A. Graps, “An Introduction to Wavelets.”

[11] R. Haddadi, E. Abdelmounim, M. E. Hanine, dan A. Belaguid, “Discrete Wavelet Transform based algorithm for recognition of QRS complexes,” in 2014 International Conference on Multimedia Computing and Systems (ICMCS), 2014, hal. 375–379.

[12] C. Bandt dan B. Pompe, “Permutation Entropy: A Natural Complexity Measure for Time Series.” [Daring]. Tersedia pada: https://www.researchgate.net/publication/11364831_Permutation_Entropy_A_Natural_Complexity_Measure_for_Time_Series. [Diakses: 30-Mei-2017].

[13] “Weighted-Permutation Entropy Analysis of Resting State EEG from Diabetics with Amnestic Mild Cognitive Impairment (PDF Download Available).” [Daring]. Tersedia pada: https://www.researchgate.net/publication/307851235_Weighted-Permutation_Entropy_Analysis_of_Resting_State_EEG_from_Diabetics_with_Amnestic_Mild_Cognitive_Impairment. [Diakses: 03-Jun-2017].

[14] “Weighted-permutation entropy: A complexity measure for time series incorporating amplitude information,” ResearchGate. [Daring]. Tersedia pada: https://www.researchgate.net/publication/236051016_Weighted-permutation_entropy_A_complexity_measure_for_time_series_incorporating_amplitude_information. [Diakses: 30-Mei-2017].

[15] P. Gaspar, J. Carbonell, dan J. L. Oliveira, “On the parameter optimization of Support Vector Machines for binary classification,” J. Integr. Bioinforma., vol. 9, no. 3, hal. 201, Jul 2012.

[16] C. Cortes dan V. Vapnik, “Support-Vector Machine,” Kluwer Acad. Publ. Boston, hal. 273–297, 1995.

[17] R. Kurnia, “Support Vector Machine –Teori dan Aplikasinya dalam Bioinformatika 1.”

[18] R. Amami, D. B. Ayed, dan N. Ellouze, “Practical Selection of SVM Supervised Parameters with Different Feature Representations for Vowel Recognition,” ArXiv150706020 Cs, Jul 2015.

[19] “Confusion Matrix-based Feature Selection. (PDF Download Available),” ResearchGate. [Daring]. Tersedia pada: https://www.researchgate.net/publication/220833270_Confusion_Matrix-based_Feature_Selection. [Diakses: 14-Jun-2017].

[20] P. Refaeilzadeh, L. Tang, dan H. Liu, “Cross-Validation.”

[21] “EEG time series downlaod page.” [Daring]. Tersedia pada: http://epileptologie-bonn.de/cms/front_content.php?idcat=193〈=3.

[22] R. G. Andrzejak, K. Lehnertz, F. Mormann, C. Rieke, P. David, dan C. E. Elger, “Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state,” vol. 64, Nov 2001.


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