* Corresponding Author
Hide
Show all
Combination of Coarse-Grained Procedure and Fractal Dimension for Epileptic EEG Classification
https://doi.org/10.22146/ijccs.69845 Dien Rahmawati(1*), Achmad Rizal(2), Desri Kristina Silalahi(3)
(1) School of Electrical Engineering, Telkom University, Bandung
(2) School of Electrical Engineering, Telkom University, Bandung
(3) School of Electrical Engineering, Telkom University, Bandung
(*) Corresponding Author
Abstract
Epilepsy, cured by some offered treatments such as medication, surgery, and dietary plan, is a neurological brain disorder due to disturbed nerve cell activity characterized by repeated seizures. Electroencephalographic (EEG) signal processing detects and classifies these seizures as one of the abnormality types in the brain within temporal and spectral content. The proposed method in this paper employed a combination of two feature extractions, namely coarse-grained and fractal dimension, a challenge to obtain a highly accurate procedure to evaluate and predict the epileptic EEG signal of normal, interictal, and seizure classes. The result of classification accuracy using variance fractal dimension (VFD) and quadratic support machine vector (SVM) with a number scale of 10 is 99% as the highest one, excellent performance of the predictive model in terms of the error rate. In addition, a higher scale number does not determine a higher accuracy in this study.
Keywords
Full Text:
PDFReferences
[1] WHO, “Epilepsy : the disorder,” Epilepsy Atlas, 2005.
[2] I. Wijayanto, A. Rizal, and S. Hadiyoso, “Multilevel Wavelet Packet Entropy and Support Vector Machine for Epileptic EEG Classification,” 2018, doi: 10.1109/ICSTC.2018.8528634.
[3] I. Wijayanto, A. Rizal, and A. Humairani, “Seizure Detection Based on EEG Signals Using Katz Fractal and SVM Classifiers,” 2019, doi: 10.1109/ICSITech46713.2019.8987487.
[4] A. Rizal, Estananto, and R. D. Atmaja, “Epileptic EEG signal classification using multiresolution higuchi fractal dimension,” Int. J. Eng. Res. Technol., vol. 12, no. 4, 2019.
[5] A. Rizal and S. Hadiyoso, “Sample entropy on multidistance signal level difference for epileptic EEG classification,” Sci. World J., vol. 2018, 2018, doi: 10.1155/2018/8463256.
[6] R. San-Segundo, M. Gil-Martín, L. F. D’Haro-Enríquez, and J. M. Pardo, “Classification of epileptic EEG recordings using signal transforms and convolutional neural networks,” Comput. Biol. Med., vol. 109, 2019, doi: 10.1016/j.compbiomed.2019.04.031.
[7] Y. Gao, B. Gao, Q. Chen, J. Liu, and Y. Zhang, “Deep convolutional neural network-based epileptic electroencephalogram (EEG) signal classification,” Front. Neurol., vol. 11, 2020, doi: 10.3389/fneur.2020.00375.
[8] H. T. Shiao et al., “SVM-Based System for Prediction of Epileptic Seizures from iEEG Signal,” IEEE Trans. Biomed. Eng., 2017, doi: 10.1109/TBME.2016.2586475.
[9] P. W. Mirowski, Y. LeCun, D. Madhavan, and R. Kuzniecky, “Comparing SVM and convolutional networks for epileptic seizure prediction from intracranial EEG,” 2008, doi: 10.1109/MLSP.2008.4685487.
[10] 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,” Phys. Rev. E - Stat. Physics, Plasmas, Fluids, Relat. Interdiscip. Top., 2001, doi: 10.1103/PhysRevE.64.061907.
[11] M. Costa, A. L. Goldberger, and C.-K. Peng, “Multiscale entropy analysis of complex physiologic time series.,” Phys. Rev. Lett., vol. 89, no. 6, p. 068102, 2002, doi: 10.1103/PhysRevLett.92.089803.
[12] X. Yan and M. Jia, “Intelligent fault diagnosis of rotating machinery using improved multiscale dispersion entropy and mRMR feature selection,” Knowledge-Based Syst., 2019, doi: 10.1016/j.knosys.2018.09.004.
[13] A. Humeau-heurtier, “The Multiscale Entropy Algorithm and Its Variants: A Review,” Entropy, vol. 17, pp. 3110–3123, 2015, doi: 10.3390/e17053110.
[14] B. B. Mandelbrot, “Self-Affine Fractals and Fractal Dimension,” Phys. Scr., vol. 32, no. 4, pp. 257–260, 1985, doi: 10.1088/0031-8949/32/4/001.
[15] C. Sevcik, “A procedure to Estimate the Fractal Dimension of Waveforms,” Complex. Int. [online], vol. 5, 1998.
[16] R. Uthayakumar, “Fractal dimension in Epileptic EEG signal analysis,” Underst. Complex Syst., 2013, doi: 10.1007/978-3-642-34017-8_4.
[17] T. Higuchi, “Approach to an irregular time series on the basis of the fractal theory,” Phys. D Nonlinear Phenom., vol. 31, no. 2, pp. 277–83, 1988.
[18] M. J. Katz, “Fractals and The Analysis of Waveforms,” Comput. Biol. Med, vol. 18, no. 3, pp. 145–56, 1988.
[19] A. Phinyomark, P. Phukpattaranont, and C. Limsakul, “Applications of variance fractal dimension: A survey,” Fractals, vol. 22, no. 1–2. 2014, doi: 10.1142/S0218348X14500030.
[20] A. Petrosian, “Kolmogorov Complexity of Finite Sequences and Recognition of Different Preictal EEG Patterns,” in 8th IEEE Symposium on Computer-Based Medical Systems, 1995, pp. 212–17.
[21] G. B. So, H. R. So, and G. G. Jin, “Enhancement of the Box-Counting Algorithm for fractal dimension estimation,” Pattern Recognit. Lett., 2017, doi: 10.1016/j.patrec.2017.08.022.
[22] J. Wu, X. Jin, S. Mi, and J. Tang, “An effective method to compute the box-counting dimension based on the mathematical definition and intervals,” Results Eng., 2020, doi: 10.1016/j.rineng.2020.100106.
[23] B. S. Raghavendra and D. Narayana Dutt, “A note on fractal dimensions of biomedical waveforms,” Comput. Biol. Med., vol. 39, no. 11, 2009, doi: 10.1016/j.compbiomed.2009.08.001.
[24] B. Dubuc and S. Dubuc, “Error bounds on the estimation of fractal dimension,” SIAM J. Numer. Anal., vol. 33, no. 2, 1996, doi: 10.1137/0733032.
[25] B. S. Raghavendra and D. N. Dutt, “Computing fractal dimension of signals using multiresolution box-counting method,” World Acad. Sci. Eng. Technol., vol. 37, 2010.
[26] D. Anguita, L. Ghelardoni, A. Ghio, L. Oneto, and S. Ridella, “The ‘K’ in K-fold cross validation,” 2012.
[27] A. Rizal, R. Hidayat, and H. A. Nugroho, “Fractal dimension for lung sound classification in multiscale scheme,” J. Comput. Sci., vol. 14, no. 8, 2018, doi: 10.3844/jcssp.2018.1081.1096.
[28] M. Schneider, P. N. Mustaro, and C. A. M. Lima, “Automatic recognition of epileptic seizure in EEG via support vector machine and dimension fractal,” in 2009 International Joint Conference on Neural Networks, Jun. 2009, pp. 2841–2845, doi: 10.1109/IJCNN.2009.5179059.
[29] D. K. Silalahi, A. Rizal, D. Rahmawati, and B. Sri, “Epileptic Seizure Detection using Multidistance Signal Level Difference Fractal Dimension and Support Vector Machine,” J. Theor. Appl. Inf. Technol., vol. 99, no. 4, pp. 909–920, 202 DOI: https://doi.org/10.22146/ijccs.69845
Article Metrics
Abstract views : 1466
| 
views : 1200
Refbacks
- There are currently no refbacks.
Copyright (c) 2021 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
View My Stats1