Abstract
EEG signals are widely used epilepsy studies. Utilizing features of these signals, a great number of methods have been proposed for seizure detection. Obtained feature matrix is classified using different classifiers. Processing load is directly related to the size of the matrix. For real time applications, it is major problem that processing load is too much. Dimension reduction and feature selection use to eliminate this problem. In this study, effects of size reduction on classifiers' performances are investigated. A feature matrix of size 300x16 has obtained from EEG signals, which were taken from healthy and epileptic subjects in different conditions. This matrix has been classified using Multilayer Perceptron Neural Networks (MLPNN), Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM). Feature matrix has been reduced to 300x5 size using Principal Component Analysis (PCA). New feature matrix has been classified using the same classifiers again. The results of both conditions have been compared.
References
- (KAYNAKLAR) Shoeb, A. and Guttag, J. (2010), ‘Application of Machine Learning To Epileptic Seizure Detection’, The 27th International Conference on Machine Learning, Haifa, Israel, pp. 975-982.
- Sivasankari, N. and Thanushkodi, K. (2009) Automated Epileptic Seizure Detection in EEG Signals Using FastICA and Neural Network, Int. J. Advance, Soft Comput. Appl., Vol.1, No. 2, November, pp. 1-14.
- Subasi, A. (2005) Epileptic seizure detection using dynamic wavelet network, Expert Systems with Applications, vol. 29, No. 2, August, pp.343–355.
- Mohseni, H.R,. Maghsoudi, A. and Shamsollahi, M.B. (2006) ‘Seizure Detection in EEG signals: A Comparison of Different Approaches’, 28th IEEE EMBS Annual International Conference, New York City, USA, pp. 6724-6727.
- Subaşı, A. and Gürsoy, M.İ. (2010) EEG signal classification using PCA, ICA, LDA and support vector machines, Expert Systems with Applications, Vol. 37, No. 12, December, pp. 8659–8666.
- Temko, A., Thomas, E., Marnane, W., Lightbody G. and Boylan, G. (2011) EEG-based neonatal seizure detection with Support Vector Machines, Clinical Neurophysiology, Vol. 122, March, pp. 464–473.
- Alkan, A., Koklukaya, E. and Subasi, A. (2005) Automatic seizure detection in EEG using logistic regression and artificial neural network” Journal of Neuroscience Methods, vol. 148, No. 2, October, pp.167–176.
- Tzalles, A.Z., Tsipouras, M.G. and Fotiadis, D.I. (2007) Automatic Seizure Detection Based on Time-frequency analysis and Artificial Neural Networks", Computational Intelligence and Neuroscience,Vol. 2007, No. 18, August, 13 pages.
- Ocak, H. (2008) Optimal classification of epileptic seizures in EEG using wavelet analysis and genetic algorithm, Signal Processing, Vol. 88, No. 7, July, pp. 1858-1867.
- Liang, S.F., Wang, H.C. and Chang, W.L. (2010)
- ‘Combination of EEG Complexity and Spectral Analysis for Epilepsy Diagnosis and Seizure Detection’, EURASIP Journal on Advances in Signal Processing, Vol. 2010, No. 62, February.
- Kim, H. and Rosen, J. (2010) ‘Epileptic Seizure
- Detection - An AR Model Based Algorithm for Implantable Device’, Engineering in Medicine and Biology Society (EMBC), 2010 Annual
- International Conference of the IEEE, Buenos Aires, Argentine, pp. 5541-5544.
- Mahajan, K.., Vargantwar, M.R. and Rajput, S.M. (2011) Classification of EEG using PCA, ICA and Neural Network, International Journal of Engineering and Advanced Technology, Vol. 1, No. 1, October, pp. 80-83.
- Andrzejak, R.G. (2013) EEG time series, [Online], http://www.meb.unionn.de/epileptologie/cms/upload/workgroup/lehn ertz/eegdata.html . [1 Aug 2013]
- Bow, S.T. (2002) Pattern Recognition and Image Preprocessing, Marcel Dekker, New York, USA.
- Costaridou, L. (2005) Medical Image Analysis
- Methods, CRC Pres, USA. Meyer-Baese, A. (2004) Pattern Recognition for
- Medical Imaging, Elsevier Academic Pres, California, USA. Duda, R.O., Hart, P.E. and Stork, D.G. (2001)
- Pattern Classification, Wiley-Interscience, New York, USA. Haykin, S. (1999) Neural Networks: A
- Comprehensive Foundation, Prentice Hall, New Jersey, USA. Mitchell, T.M. (1997) Machine Learning, McGraw-Hill Science/Engineering/Math, USA.
- Zheng, N. and Xue, J. (2009) Statistical Learning and Pattern Analysis for Image and Video
- Processing, Springer-Verlag London Limited, London. Vapnik, V.N. (1998) Statistical Learning Theory, Wiley-Interscience, New York.
- Abe, S. (2005) Support Vector Machines for
- Pattern Classification, Springer, New York, USA. Junoh, A.K. and Mansor, M.N. (2012) ‘Safety
- System Based on Linear Discriminant Analysis’, 2012 International Symposium on Instrumentation & Measurement, Sensor
- Network and Automation (IMSNA), Vol:1, pp. 32Fielding, A. (2000) Cluster and Classification
- Techniques for the Biosciences, Cambridge University Press.
Abstract
EEG işaretleri epilepsi çalışmalarında yaygın olarak kullanılmaktadır. Bu işaretlerin özelliklerinden yararlanarak
nöbet algılayan birçok yöntem önerilmiştir. Elde edilen özellik matrisi farklı sınıflandırıcılar kullanılarak
sınıflandırılmaktadır. İşlem yükü özellik matrisinin boyutuyla doğrudan ilgilidir. Gerçek zamanlı uygulamalarda
işlem yükünün fazla olması başlıca sorunlardandır. Bu problemi çözmek için özellik seçimi ve boyut azaltımı
kullanılmaktadır. Bu çalışmada boyut azaltımının sınıflandırıcı performansları üzerindeki etkileri incelenmiştir.
Sağlıklı ve epileptik bireylerden farklı koşullarda alınan EEG işaretlerinden, 300x16 boyutunda özellik matrisi elde
edilmiştir. Bu matris Çok Katmanlı Yapay Sinir Ağları, Lineer Diskriminant Analizi ve Destek Vektör Makineleri
yöntemleri kullanılarak sınıflandırılmıştır. Özellik matrisinin boyutu Temel Bileşenler Analiziyle 300x5 boyutuna
indirgenmiştir. Sınıflandırma işlemleri boyutu indirgenmiş özellik matrisi için tekrarlanmıştır. Her iki durum için
sonuçlar karşılaştırılmıştır.
Keywords
EEG işaretleri epilepsi Yapay Sinir Ağları Lineer Diskriminant Analizi Destek Vektör Makineleri
References
- (KAYNAKLAR) Shoeb, A. and Guttag, J. (2010), ‘Application of Machine Learning To Epileptic Seizure Detection’, The 27th International Conference on Machine Learning, Haifa, Israel, pp. 975-982.
- Sivasankari, N. and Thanushkodi, K. (2009) Automated Epileptic Seizure Detection in EEG Signals Using FastICA and Neural Network, Int. J. Advance, Soft Comput. Appl., Vol.1, No. 2, November, pp. 1-14.
- Subasi, A. (2005) Epileptic seizure detection using dynamic wavelet network, Expert Systems with Applications, vol. 29, No. 2, August, pp.343–355.
- Mohseni, H.R,. Maghsoudi, A. and Shamsollahi, M.B. (2006) ‘Seizure Detection in EEG signals: A Comparison of Different Approaches’, 28th IEEE EMBS Annual International Conference, New York City, USA, pp. 6724-6727.
- Subaşı, A. and Gürsoy, M.İ. (2010) EEG signal classification using PCA, ICA, LDA and support vector machines, Expert Systems with Applications, Vol. 37, No. 12, December, pp. 8659–8666.
- Temko, A., Thomas, E., Marnane, W., Lightbody G. and Boylan, G. (2011) EEG-based neonatal seizure detection with Support Vector Machines, Clinical Neurophysiology, Vol. 122, March, pp. 464–473.
- Alkan, A., Koklukaya, E. and Subasi, A. (2005) Automatic seizure detection in EEG using logistic regression and artificial neural network” Journal of Neuroscience Methods, vol. 148, No. 2, October, pp.167–176.
- Tzalles, A.Z., Tsipouras, M.G. and Fotiadis, D.I. (2007) Automatic Seizure Detection Based on Time-frequency analysis and Artificial Neural Networks", Computational Intelligence and Neuroscience,Vol. 2007, No. 18, August, 13 pages.
- Ocak, H. (2008) Optimal classification of epileptic seizures in EEG using wavelet analysis and genetic algorithm, Signal Processing, Vol. 88, No. 7, July, pp. 1858-1867.
- Liang, S.F., Wang, H.C. and Chang, W.L. (2010)
- ‘Combination of EEG Complexity and Spectral Analysis for Epilepsy Diagnosis and Seizure Detection’, EURASIP Journal on Advances in Signal Processing, Vol. 2010, No. 62, February.
- Kim, H. and Rosen, J. (2010) ‘Epileptic Seizure
- Detection - An AR Model Based Algorithm for Implantable Device’, Engineering in Medicine and Biology Society (EMBC), 2010 Annual
- International Conference of the IEEE, Buenos Aires, Argentine, pp. 5541-5544.
- Mahajan, K.., Vargantwar, M.R. and Rajput, S.M. (2011) Classification of EEG using PCA, ICA and Neural Network, International Journal of Engineering and Advanced Technology, Vol. 1, No. 1, October, pp. 80-83.
- Andrzejak, R.G. (2013) EEG time series, [Online], http://www.meb.unionn.de/epileptologie/cms/upload/workgroup/lehn ertz/eegdata.html . [1 Aug 2013]
- Bow, S.T. (2002) Pattern Recognition and Image Preprocessing, Marcel Dekker, New York, USA.
- Costaridou, L. (2005) Medical Image Analysis
- Methods, CRC Pres, USA. Meyer-Baese, A. (2004) Pattern Recognition for
- Medical Imaging, Elsevier Academic Pres, California, USA. Duda, R.O., Hart, P.E. and Stork, D.G. (2001)
- Pattern Classification, Wiley-Interscience, New York, USA. Haykin, S. (1999) Neural Networks: A
- Comprehensive Foundation, Prentice Hall, New Jersey, USA. Mitchell, T.M. (1997) Machine Learning, McGraw-Hill Science/Engineering/Math, USA.
- Zheng, N. and Xue, J. (2009) Statistical Learning and Pattern Analysis for Image and Video
- Processing, Springer-Verlag London Limited, London. Vapnik, V.N. (1998) Statistical Learning Theory, Wiley-Interscience, New York.
- Abe, S. (2005) Support Vector Machines for
- Pattern Classification, Springer, New York, USA. Junoh, A.K. and Mansor, M.N. (2012) ‘Safety
- System Based on Linear Discriminant Analysis’, 2012 International Symposium on Instrumentation & Measurement, Sensor
- Network and Automation (IMSNA), Vol:1, pp. 32Fielding, A. (2000) Cluster and Classification
- Techniques for the Biosciences, Cambridge University Press.
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Engineering |
| Journal Section | Research Articles |
| Authors | |
| Publication Date | July 17, 2014 |
| Submission Date | February 19, 2013 |
| Acceptance Date | August 12, 2013 |
| Published in Issue | Year 2014 Volume: 18 Issue: 2 |
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