Abstract
In clinical practice, study of brain functions is fundamental to notice several diseases potentially dangerous for the health of the subject. Electroencephalography (EEG) can be used to detect cerebral disorders but EEG study is often difficult to implement, taking into account the multivariate and non-stationary nature of the signals and the invariable presence of noise. In the field of Signal Processing exist many algorithms and methods to analyze and classify signals reducing and extracting useful information. Support Vector Machine (SVM) based algorithms can be used as classification tool and allow to obtain an efficient discrimination between different pathology and to support physicians while studying patients. In this paper, we report an experience on designing and using an SVM based algorithm to study and classify EEG signals. We focus on Creutzfeldt-Jakob disease (CJD) EEG signals. To reduce the dimensionality of the dataset, principal component analysis (PCA) is used. These vectors are used as inputs for the SVM classifier with two classification classes: pathologic or healthy. The classification accuracy reaches 96.67% and a validation test has been performed, using unclassified EEG data.
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References
Bhuvaneswari, P., Kumar, J.S.: Support vector machine technique for EEG signals. Int. J. Comput. Appl. 63(13) (2013)
Li, Y., Wen, P., et al.: Classification of EEG signals using sampling techniques and least square support vector machines. In: Rough Sets and Knowledge Technology. Springer (2009)
Shoeb, A.H., Guttag, J.V.: Application of machine learning to epileptic seizure detection. In: Proceedings of the 27th International Conference on Machine Learning (2010)
Kumari, R., Jose, J.P.: Seizure detection in EEG using time frequency analysis and SVM. In: 2011 International Conference on Emerging Trends in Electrical and Computer Technology (ICETECT) (2011)
Panda, R., Khobragade, P.S., Jambhule, P.D., Jengthe, S.N., Pal, P.R., Gandhi, T.K.: Classification of EEG signal using wavelet transform and support vector machine for epileptic seizure diction. In: International Conference on Systems in Medicine and Biology (ICSMB) (2010)
Li, S., Zhou, W., Yuan, Q., Geng, S., Cai, D.: Feature extraction and recognition of ICTAL EEG using EMD and SVM. Comput. Biol. Med. (Elsevier) 43(7) (2013)
Temko, A., Thomas, E., Marnane, W., Lightbody, G., Boylan: EEG-based neonatal seizure detection with support vector machines. Clin. Neurophysiol. (Elsevier) 122(3) (2011)
Murugesan, M., Sukanesh, R.: Towards detection of brain tumor in electroencephalogram signals using support vector machines. Int. J. Comput. Theory Eng. (IACSIT Press) 1(5) (2009)
Sabeti, M., Boostani, R., Katebi, S.D., Price, G.W.: Selection of relevant features for EEG signal classification of schizophrenic patients. Biomed. Signal Process. Control (Elsevier) 2(2) (2007)
Yeo, M.V.M., Li, X., Shen, K., Wilder-Smith, E.P.V.: Can SVM be used for automatic EEG detection of drowsiness during car driving. Saf. Sci. (Elsevier) 47(1) (2009)
Shoker, L., Saeid, S., Alex, S.: Distinguishing between left and right finger movement from EEG using SVM. In: 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS (2005)
Hazarika, N., Chen, J.Z., Ah Chung, T., Sergejew, A.: Classification of EEG signals using the wavelet transform. Digit. Signal Process. Proc. (IEEE) 1 (1997)
Subasi, A., Erelebi, E.: Classification of EEG signals using neural network and logistic regression. Comput. Methods Programs Biomed. 78 (2005)
Subasi, A., Alkana, A., Koklukayab, E., Kemal Kiymika, M.: Wavelet neural network classification of EEG signals by using AR model with MLE preprocessing. Neural Netw. (Elsevier) 18 (2005)
Labate, D., Palamara, I., Mammone, N., Morabito, G., La Foresta, F., Morabito, F.C.: SVM classification of epileptic EEG recordings through multiscale permutation entropy. Neural Netw. (IEEE) (2013)
Morabito, F.C., Campolo, M., Labate, D., Morabito, G., Bonanno, L., Bramanti, A., De Salvo, S., Marra, A., Bramanti, P.: A longitudinal EEG study of Alzheimer’s disease progression based on a complex network approach. Int. J. Neural Syst. 25(02) (2015)
Vizza, P., Curcio, A., Tradigo, G., Indolfi, C., Veltri, P.: A framework for the a trial fibrillation prediction in electrophysiological studies. Comput. Methods Programs Biomed. (Elsevier) (2015)
Wieser, H.G., Schindler, K., Zumsteg, D.: EEG in Creutzfeldt–Jakob disease. Clin. Neurophysiol. (Elsevier) 117(5) (2006)
Rajesh, S., Brijil, C., Arun, K., Santosh, Jayashree: Comparison of SVM and ANN for classification of eye events in EEG. J. Biomed. Sci. Eng. (Scientific Research Publishing) 4(01) (2011)
Adeli, H., Zhou, Z., Dadmehr, N.: Analysis of EEG records in an epileptic patient using wavelet transform. J. Neurosci. Methods (Elsevier) 123 (2003)
Inuso, G., La Foresta, F., Mammone, N., Morabito, F.C.: Brain activity investigation by EEG processing: wavelet analysis, Kurtosis and Renyi’s entropy for artifact detection. Inf. Acquis. (IEEE) (2007)
Inuso, G., La Foresta, F., Mammone, N., Morabito, F.C.: Wavelet-ICA methodology for efficient artifact removal from electroencephalographic recordings. Neural Netw. (IEEE) (2007)
Martnez, A.M., Kak, A.C.: PCA versus LDA. IEEE Trans. Pattern Anal. Mach. Intell. 23(2) (2001)
Gursoy, Subast: A comparison of PCA, ICA and LDA in EEG signal classification using SVM. In: 2008 IEEE 16th Signal Processing, Communication and Applications Conference (2008)
Subasi, A., Gursoy, M.I.: EEG signal classification using PCA, ICA, LDA and support vector machines. Expert Syst. Appl. (Elsevier) 37(12) (2010)
Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. (TIST) 2(3) (2011)
Hsu, C.-W., Chang, C.-C., Lin, C.-J., et al.: A practical guide to support vector classification (2003)
Weston, J.: Leave-One-Out Support Vector Machines (IJCAI) (1999)
Acknowledgements
Authors thank Rocco Cutellé for his support and experiments in denoising and preprocessing signals, and Umberto Aguglia for furnishing supports for EEG signals.
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Saccá, V., Campolo, M., Mirarchi, D., Gambardella, A., Veltri, P., Morabito, F.C. (2018). On the Classification of EEG Signal by Using an SVM Based Algorithm. In: Esposito, A., Faudez-Zanuy, M., Morabito, F., Pasero, E. (eds) Multidisciplinary Approaches to Neural Computing. Smart Innovation, Systems and Technologies, vol 69. Springer, Cham. https://doi.org/10.1007/978-3-319-56904-8_26
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DOI: https://doi.org/10.1007/978-3-319-56904-8_26
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