Peningkatan Ekstrasi Ciri Sinyal Epilepsi Menggunakan Teknik Sampling

Ade Eviyanti, Hindarto Hindarto, M. Abror

Abstract


Epilepsy is a brain disorder characterized by repeated seizures. Epileptic seizures are episodes that can vary from short periods to long periods and are almost undetectable. Electroencephalography (EEG) is a way to record the activities of the human brain. EEG is a brain sensor that can be used to determine epilepsy. The purpose of this study is to classify the signals of people who have epilepsy disease and the signals of people in good health. The method used is a sampling technique method to find the characteristics of EEG signals and the K-Nearest Neighbor (KNN) method to find the classification of EEG signals. The data used is EEG signal data consisting of five data sets (data set A, data set B, data set C, data set D, and data set E), this data comes from public data. Each data set contains 100 EEG signal data on one EEG sensor channel. This study only uses two classes, the first is Data Set A and the second is Data Set E. Data Set A is a person in normal condition and data set E is a person in a state of having epilepsy. In the sampling technique process, the values taken are the average value and the standard deviation value. Research that has been done yields an accuracy rate of 100%.

Keywords


Epilepsy signal; Sampling; technique KNN

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Alto, P., Trammell, P. E. P., & Dam, A. E. Q. (1998). United States Patent ( 19 ) Date of Patent : (19).

Debo Cheng, Shichao Zhang, Zhenyun Deng, Yonghua Zhu, And Ming Zong (2014). KNN Algorithm With Data-Driven K Value. Adma 2014, Lnai 8933, Pp. 499–512, 2014. C Springer International Publishing Switzerland 2014.

Diker, A., Avci, E., Comert, Z., Avci, D., Kacar, E., & Serhatlioglu, I. (2018). Classification of ECG signal by using machine learning methods. 2018 26th Signal Processing and Communications Applications Conference (SIU). doi: https://10.1109/siu.2018.8404298.

Eeg Time Series, (Epileptic Eeg Data) (2005, Nov.) [Online], Http://Www.Meb.Uni-Bonn.De/Epileptologie/Science/Physik/Eegdata.Html.

Ekong, U., Lam, H. K., Xiao, B., Ouyang, G., Liu, H., & Chan, K. Y. (2016). Classification of Epilepsy Seizure Phase using Interval Type-2 Fuzzy Support Vector Machines. (19), 1–26. doi: https://doi.org/10.1016/j.neucom.2016.03.033

Han, J. dan M. Kamber. 2001. Data Mining: Concepts and Techniques Tutorial. Morgan Kaufman Publisher. San Francisco.

J. Pais-Ribeiro and R. F. Meneses (2011). Management of Epilepsy - Research, Results and Treatmen. ISBN 978-953-307-680-5 Hard cover, 194 pages Publisher InTech Published online 15, September, 2011 Published in print edition September, 2011

Gajic, D., Djurovic, Z., Gennaro, S. Di, & Gustafsson, F. (2014). Classification of EEG signals for detection of epileptic seizures based on wavelets and statistical pattern recognition. (26). doi:10.4015/S10162372145002151450021-1

Ko, M., Majkowski, A., & Rak, R. J. (2010). A new method of feature extraction from EEG signal for brain- computer interface design. (9), 35–38.

Kulkarni, P. K. (2013). EEG signal classification for Epilepsy Seizure Detection using Improved Approximate Entropy. 2(1).

Li, Y., & Wen, P. (2009). Classification of EEG Signals Using Sampling Techniques. 375–376.

Priyanka, S., & Dema, D. (2017). Feature Selection and Classification of Epilepsy from EEG Signal. 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS), (September 2018), 2404–2406. https://doi.org/10.1109/ICECDS.2017.8389880

Ratham, H., Ghayab, A., Li, Y., Abdulla, S., Diykh, M., & Wan, X. (2016). Classification of epileptic EEG signals based on simple random sampling and sequential feature selection. Brain Informatics, 3(2), 85–91. https://doi.org/10.1007/s40708-016-0039-1

Rincon, A. Q., Prendes, J., Pereyra, M. A., Risk, M., Rincon, A. Q., Prendes, J., … Risk, M. (2018). Multivariate Bayesian classification of epilepsy EEG signals To cite this version : HAL Id : hal-01782569.

Technology, I., & Email, I. (2016). Feature Extraction Of Signals Using Fast Fourier. 10(2), 49–52.

Update, P., & Guideline, C. (2013). The Epilepsies The diagnosis and management of the epilepsies in.

Yazdani, A., Ebrahimi, T., & Hoffmann, U. (2009). Classification Of Eeg Signals Using Dempster Shafer Theory And A K-Nearest Neighbor Classifier. (1), 327–330.

M. Kuramochi And G. Karypis (2001). Gene Classification Using Expression Profiles: A Feasibility Study. In Bibe ’01: Proceedings Of The 2nd Ieee Internationalsymposium On Bioinformatics And Bioengineering, Page 191, Washington, Dc, 2001. Ieee Computer Society.

Natalia Labuda, Julia Seeliger, Tomasz Gedrande, Karol Kozak (2017). Selecting Adaptive Number Of Nearest Neighbors In K-Nearest Neighbor Classifier Apply Diabetes Data. Journal Of Mathematics And Statistical Science, 1-13 | Science Signpost Publishing. Published: Volume 2017, Issue 1 / January 25, 2017.

Shichao Zhang, Xuelong Li, Ming Zong, Xiaofeng Zhu, And Debo Cheng (2017). Learning K For Knn Classification. Acm Transactions On Intelligent Systems And Technology, Vol. 8, No. 3, Article 43, Publication Date: January 2017.

Rg Andrzejak, K Lehnertz, F Mormann, C Rieke, P David, And Ce Elger (2001). Indications Of Nonlinear Deterministic And Finite-Dimensional Structures In Time Series Of Brain Electrical Activity: Dependence On Recording Region And Brain State, Phys Rev E Stat Nonlin Soft Matter Phys, Vol. 64, No. 6, Dec 2001, Pp. 061907. doi: http://dx.doi.org/10.1103/physreve.64.061907




DOI: http://dx.doi.org/10.35671/telematika.v13i2.964

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Telematika
ISSN: 2442-4528 (online) | ISSN: 1979-925X (print)
Published by : Universitas Amikom Purwokerto
Jl. Let. Jend. POL SUMARTO Watumas, Purwonegoro - Purwokerto, Indonesia


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