Examining the Effect of Dimension Reduction on EEG Signals by KNearest Neighbors Algorithm

KAYA, Duygu; KAYA, Turgay; Türk, Mustafa

Examining the Effect of Dimension Reduction on EEG Signals by KNearest Neighbors Algorithm

Duygu KAYA, (Fırat Üniversitesi)

Mustafa TÜRK, (Fırat Üniversitesi)

Turgay KAYA (Fırat Üniversitesi)

El-Cezerî Journal of Science and Engineering

1 0

Yıl: 2018 Cilt: 5 Sayı: 2 Sayfa Aralığı: 591 - 595 Metin Dili: İngilizce İndeks Tarihi: 06-03-2020

Examining the Effect of Dimension Reduction on EEG Signals by KNearest Neighbors Algorithm

Öz:

Machine learning which a paradigm of methods that makes inferences from existing data usingmathematical and statistical methods and is inferred to be unknown. The proposed method in this paper,supervised learning algorithm is applied to EEG (electroencephalography) data and classification algorithmperformance is analyzed and results are examined in MATLAB. K-Nearest Neighbors Algorithm (k-NN) is usedin this paper as algorithm. This classification was evaluated in two stages, with and without PrincipalComponent Analysis (PCA). Dimension reduction is the process of reducing the size of dimension of the data.By reducing the size of the data set with PCA, it is expected to protect important data features. KNN has givenresults that can be regarded as prudent in terms of classification accuracy. The results of the present workshowed that appropriate features combined with classifier can be done significant classification for differentbioelectrical signal.

Anahtar Kelime:

En Yakın Komşu Algoritması Kullanılarak EEG Sinyallerine Boyut Azaltmanın Etkilerinin İncelenmesi

Öz:

Makine öğrenmesi, var olan verilerin çıkarımlarını matematiksel ve istatistiksel yöntemlerle yapan ve bilinmeyen bir yöntem paradigmasıdır. Bu çalışmada, denetimli öğrenme algoritması, EEG (elektroensefalografi) verilerine uygulanmış, sınıflandırma algoritması performans analiz sonuçları MATLAB ile incelenmiştir. Bu çalışmada, algoritma olarak en yakın komşu algoritması (k-NN) kullanılmıştır. Bu sınıflandırma, Temel Bileşen Analizinin (TBA) kullanıldığı ve kullanılmadığı durumlar için iki aşamada değerlendirilmiştir. Boyut azaltma, verilerin boyut boyutunu küçültme işlemidir. TBA ile veri kümesinin boyutunun azaltılarak, önemli veri özelliklerini korunması beklenir. KNN, sınıflandırma doğruluğu açısından önemli sayılabilecek sonuçlar vermiştir. Mevcut çalışma, farklı biyoelektriksel sinyaller için uygun özelliklerin uygun bir sınıflandırıcı ile kombine edildiğinde anlamlı bir sınıflandırma yapılabileceğini göstermiştir.

Anahtar Kelime:

Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık

[1] Shaker M. M., “EEG Waves Classifier using Wavelet Transformand Fourier Transform”, Int. Journal of Biological and Life Sciences, 2005, 1(3):85-90.
[2] Bhattacharya J. and Petsche H. “Universality in the brain while listening to music”, Proc. Royal Society Lond. B., 2001, 268(1484):2423-2433.
[3] Stam C. J., Pijn J.P.M., Suffczynski P. and Lopes da Silva F.H.. “Dynamics of the human alpha rhythm: evidence for non-linearity”, Clinical Neurophysiology, 1999, 110(10):18011813.
[4] Buzsaki G.. Rhythms of the Brain Oxford University Press, Oxford, (2006).
[5] Kumar S. P., Sriraam N., Benakop P.G. and Jinaga B.C. “Entropies based detection of epileptic seizures with artificial neural network classifiers”, Expert Syst. Appl., 2010, 37(4):3284– 3291.
[6] Iasemidis L.D., “Epileptic seizure prediction and control”, IEEE Trans Biomed Eng. 2003, 50(5):549-558.
[7] Haydari Z., Zhang Y., and Zadeh H.S., “SemiAutomatic Epilepsy Spike Detection from EEG Signal Using Genetic Algorithm and Wavelet Transform”, IEEE International Conference on Bioinformatics and Biomedicine Workshops, Atlanta, GA, USA, (2011).
[8] Park H. S., Lee Y. H., Kim N. G., Lee D.S. and. Kim S. I. “Detection of epileptic form activities in the EEG using neural network and expert system”, Studies in health technology and informatics, (1998), 9(2):1255–1259.
[9] Kaya D., Türk M., “Biyoelektriksel Kökenli İşaretlerde Rahatsızlık Teşhisinin Yorumlanması”, Fırat Üniversitesi Mühendislik Bilimleri Dergisi, Cilt 29, Sayı 1, (2017).
[10] Kaya T. and İnce M.C., “The Obtaining of Window Function Having Useful Spectral Parameters by Helping of Genetic Algorithm”, Procedia-Social and Behavioral Journal, Elsevier, 2012, 83:563-568,.
[11] Makinac M., “Support Vector Machine Approach for Classification of Cancerous Prostate Regions”, Proceedings of World Academy of Science, Engineering and Technology, 2005, vol(7):1307-6884.
[12] Christopher J. C. Burges, “A Tutorial on Support Vector Machines for Pattern Recognition”, Data Mining and Knowledge Discovery, 1998, 2(2): 121-167,
[13] Chandaka S., Chatterjee A., Munshi S., “Cross-correlation aided support vector machine classifier for classification of EEG signals”, Expert Systems with Applications: An International Journal, 2009, 36(2):1329-1336.
[14] Silver A.E., Lungren M.P., Johnson M.E., O'Driscoll, S.W., An, K.N. and Hughes, R.E., “Using support vector machines to optimally classify rotator cuff strength data and quantify post-operative strength in rotator cuff tear patients”, J. Biomech. Vol. 39, 973-979.
[15] Fan J., Shao C., Ouyang Y., Wang J., Li S., Wang Z., “Automatic seizure detection based on support vector machines with genetic algorithms”, Proceedings of the 6th international conference on Simulated Evolution And Learning, Hefei, China. October 15-18, (2006).
[16] Siuly Y. Li and Wen P., “EEG signal classification based on simple random sampling technique with least square support vector machines”, Int. J. Biomed. Engineering and Technology (2010),7(4):390-409.
[17] Chen G. and Hou R., “A New Machine Double-Layer Learning Method and Its Application in Non-Linear Time Series Forecasting,” in International Conference on Mechatronics and Automation, ICMA, 795 –799, (2007).
[18] Labview machine learning toolkit user manual.
[19] Bishop C.M., “Pattern Recognition and Machine Learning”, Springer, New York (2006).
[20] Hotelling H., “Analysis of a complex of statistical variables into principal components”. Journal of Educational Psychology, (1933), 24:417–441, and 498–520.
[21] Hotelling H., “Relations between two sets of variates”, Biometrika, 1936, vol.28: 321–377.
[22] Türk Ö., Özerdem M. S, “EEG İşaretlerinin k-NN ile Sınıflandırılmasında Dalgacıklara İlişkin Performansların Karşılaştırılması, TIPTEKNO-14, KAPADOKYA.
[23] Lee S., Kang P., Cho S., “Probabilistic local reconstruction for k-NN regression and its application to virtual metrology in semiductor manufacturing”, Neurocomputing, 2014, 131: 427– 439,
[24] Tomak L. and Bek Y. “İşlem Karakteristik Eğrisi Analizi ve Eğri Altında Kalan Alanların Karşılaştırılması”, Journal of Experimental and Clinical Medicine, 2009, 27(2): 58-65.
[25] EEG Data http://www.meb.uni- bonn.de/ epileptologie/science/ physik/eegdata.html.

APA	KAYA D, Türk M, KAYA T (2018). Examining the Effect of Dimension Reduction on EEG Signals by KNearest Neighbors Algorithm. , 591 - 595.
Chicago	KAYA Duygu,Türk Mustafa,KAYA Turgay Examining the Effect of Dimension Reduction on EEG Signals by KNearest Neighbors Algorithm. (2018): 591 - 595.
MLA	KAYA Duygu,Türk Mustafa,KAYA Turgay Examining the Effect of Dimension Reduction on EEG Signals by KNearest Neighbors Algorithm. , 2018, ss.591 - 595.
AMA	KAYA D,Türk M,KAYA T Examining the Effect of Dimension Reduction on EEG Signals by KNearest Neighbors Algorithm. . 2018; 591 - 595.
Vancouver	KAYA D,Türk M,KAYA T Examining the Effect of Dimension Reduction on EEG Signals by KNearest Neighbors Algorithm. . 2018; 591 - 595.
IEEE	KAYA D,Türk M,KAYA T "Examining the Effect of Dimension Reduction on EEG Signals by KNearest Neighbors Algorithm." , ss.591 - 595, 2018.
ISNAD	KAYA, Duygu vd. "Examining the Effect of Dimension Reduction on EEG Signals by KNearest Neighbors Algorithm". (2018), 591-595.

APA	KAYA D, Türk M, KAYA T (2018). Examining the Effect of Dimension Reduction on EEG Signals by KNearest Neighbors Algorithm. El-Cezerî Journal of Science and Engineering, 5(2), 591 - 595.
Chicago	KAYA Duygu,Türk Mustafa,KAYA Turgay Examining the Effect of Dimension Reduction on EEG Signals by KNearest Neighbors Algorithm. El-Cezerî Journal of Science and Engineering 5, no.2 (2018): 591 - 595.
MLA	KAYA Duygu,Türk Mustafa,KAYA Turgay Examining the Effect of Dimension Reduction on EEG Signals by KNearest Neighbors Algorithm. El-Cezerî Journal of Science and Engineering, vol.5, no.2, 2018, ss.591 - 595.
AMA	KAYA D,Türk M,KAYA T Examining the Effect of Dimension Reduction on EEG Signals by KNearest Neighbors Algorithm. El-Cezerî Journal of Science and Engineering. 2018; 5(2): 591 - 595.
Vancouver	KAYA D,Türk M,KAYA T Examining the Effect of Dimension Reduction on EEG Signals by KNearest Neighbors Algorithm. El-Cezerî Journal of Science and Engineering. 2018; 5(2): 591 - 595.
IEEE	KAYA D,Türk M,KAYA T "Examining the Effect of Dimension Reduction on EEG Signals by KNearest Neighbors Algorithm." El-Cezerî Journal of Science and Engineering, 5, ss.591 - 595, 2018.
ISNAD	KAYA, Duygu vd. "Examining the Effect of Dimension Reduction on EEG Signals by KNearest Neighbors Algorithm". El-Cezerî Journal of Science and Engineering 5/2 (2018), 591-595.