Detection of Epileptic Seizure Based on Phase Space Reconstruction and Support Vector Machine
Keywords:
Electrocardiogram signals, Support Vector Machine, Epilepsy Seizure, Phase Space ReconstructionsAbstract
Electroencephalogram (EEG) is an important brain signal for disease diagnosis. Automated detection of epilepsy is still an open field for research. In this study, a simulation of epilepsy detection approach is achieved by a combination of feature extraction and classification algorithms. The features were extracted using phase space reconstruction, and classified by support vector machine. The performance evaluation was tested using dataset available by University of Bonn. The results of our experiments showed excellent classification accuracy (100%), sensitivity (100%) and specificity (99%).
References
B. Fisch and R. Spehlmann, Fisch and Spehlmann's EEG primer: basic principles of digital and analog EEG, 3rd ed. Ámsterdam (Holanda): Elsevier, 1991.
T. Gasser and J. Möcks, "Correction of EOG Artifacts in Event‐Related Potentials of the EEG: Aspects of Reliability and Validity," Psychophysiology, vol. 19, pp. 472-480, 1982, 2011.
A. G. Correa, E. Laciar, H. Patiño, and M. Valentinuzzi, "Artifact removal from EEG signals using adaptive filters in cascade," in Journal of Physics: Conference Series, 2007, p. 012081.
P. S. Kumar, R. Arumuganathan, K. Sivakumar, and C. Vimal, "An adaptive method to remove ocular artifacts from EEG signals using wavelet transform," J. Appl. Sci. Res, vol. 5, pp. 711-745, 2009.
C. Jutten and J. Herault, "Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture," Signal processing, vol. 24, pp. 1-10, 1991.
P. LeVan, E. Urrestarazu, and J. Gotman, "A system for automatic artifact removal in ictal scalp EEG based on independent component analysis and Bayesian classification," Clinical neurophysiology, vol. 117, pp. 912-927, 2006.
S. Casarotto, A. M. Bianchi, S. Cerutti, and G. A. Chiarenza, "Principal component analysis for reduction of ocular artefacts in event-related potentials of normal and dyslexic children," Clinical neurophysiology, vol. 115, pp. 609-619, 2004.
P. S. Kumar, R. Arumuganathan, K. Sivakumar, and C. Vimal, "A wavelet based statistical method for de-noising of ocular artifacts in EEG signals," IJCSNS International Journal of Computer Science and Network Security, vol. 8, pp. 87-92, 2008.
M. Salwani and Y. Jasmy, "Comparison of few wavelets to filter ocular artifacts in EEG using lifting wavelet transform," in TENCON 2005-2005 IEEE Region 10 Conference, 2005, pp. 1-6.
P. Fergus, A. Hussain, D. Hignett, D. Al-Jumeily, K. Abdel-Aziz, and H. Hamdan, "A machine learning system for automated whole-brain seizure detection," Applied Computing and Informatics, vol. 12, pp. 70-89, 2016.
A. Tzallas, M. Tsipouras, and D. Fotiadis, "Automatic seizure detection based on time-frequency analysis and artificial neural networks," Computational Intelligence and Neuroscience, vol. 2007, 2007.
J.-H. Kang, Y. G. Chung, and S.-P. Kim, "An efficient detection of epileptic seizure by differentiation and spectral analysis of electroencephalograms," Computers in biology and medicine, vol. 66, pp. 352-356, 2015.
Y. Song and J. Zhang, "Discriminating preictal and interictal brain states in intracranial EEG by sample entropy and extreme learning machine," Journal of Neuroscience Methods, vol. 257, pp. 45-54, 2016.
Y. Song, J. Crowcroft, and J. Zhang, "Automatic epileptic seizure detection in EEGs based on optimized sample entropy and extreme learning machine," Journal of Neuroscience Methods, vol. 210, pp. 132-146, 2012.
M. Zabihi, S. Kiranyaz, A. B. Rad, A. K. Katsaggelos, M. Gabbouj, and T. Ince, "Analysis of high-dimensional phase space via Poincaré section for patient-specific seizure detection," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 24, pp. 386-398, 2016.
R. Djemal, A. G. Bazyed, K. Belwafi, S. Gannouni, and W. Kaaniche, "Three-Class EEG-Based Motor Imagery Classification Using Phase-Space Reconstruction Technique," Brain Sciences, vol. 6, p. 36, 2016.
R. G. Andrzejak, K. Lehnertz, F. Mormann, C. Rieke, P. David, and 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," Physical Review E, vol. 64, p. 061907, 2001.
F. J. Harris, "On the use of windows for harmonic analysis with the discrete Fourier transform," Proceedings of the IEEE, vol. 66, pp. 51-83, 1978.
K. Polat and S. Güneş, "Classification of epileptiform EEG using a hybrid system based on decision tree classifier and fast Fourier transform," Applied Mathematics and Computation, vol. 187, pp. 1017-1026, 2007.
F. Takens, "Detecting strange attractors in turbulence," in Dynamical systems and turbulence, Warwick 1980, ed: Springer, 1981, pp. 366-381.
H.-g. Ma and C.-z. Han, "Selection of embedding dimension and delay time in phase space reconstruction," Frontiers of Electrical and Electronic Engineering in China, vol. 1, pp. 111-114, 2006.
V. Lawhern, W. D. Hairston, K. McDowell, M. Westerfield, and K. Robbins, "Detection and classification of subject-generated artifacts in EEG signals using autoregressive models," Journal of Neuroscience Methods, vol. 208, pp. 181-189, 2012.
L. T. P. Refaeilzadeh, and H. Liu, "Cross-validation," Encyclopedia of database systems, vol, pp. 532-538, 2009.
Z. R. Zamir, "Detection of epileptic seizure in EEG signals using linear least squares preprocessing," Computer methods and programs in biomedicine, vol. 133, pp. 95-109, 2016.
Downloads
Published
How to Cite
Issue
Section
License
Authors who submit papers with this journal agree to the following terms.
