Abstract
EEG classification has received much attention in computer-aided diagnosis for seizure. However, the present feature extraction strategies lead to uninterpretable features to which human vision is not sensitive, and due to the lack of explicit knowledge consistent with human intuition in such features, their usage in clinic diagnosis is limited. Inspired by human perception of seizure patterns, from a morphological point of view, we propose to make use of blanket-covering dimensions for seizure diagnosis, which act as a morphological lens of waveform complexity to enable visually straightforward features consistent with human perception. Moreover, we apply feature selection to mine the relevant features for seizure diagnosis from a pool of blanket-covering dimensions computed by combining different scales. The fractal dimensions computed as such combined with multifractals lead to 97.72% precision in classifying healthy people, seizure-inactive patients, and seizure-active patients on a public benchmark.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Nagaraj, S.B., Stevenson, N.J., Marnane, W.P., Boylan, G.B., Lightbody, G.: Neonatal seizure detection using atomic decomposition with a novel dictionary. IEEE Trans. Biomed. Eng. 61(11), 2724–2732 (2014)
Yang, S., Liang, J., Wang, Y., Winstanley, A.: Feature selection based on run covering. In: Chang, L.-W., Lie, W.-N. (eds.) PSIVT 2006. LNCS, vol. 4319, pp. 208–217. Springer, Heidelberg (2006). https://doi.org/10.1007/11949534_21
Liang, S.F., Wang, H.C., Chang, W.L.: Combination of EEG complexity and spectral analysis for epilepsy diagnosis and seizure detection. EURASIP J. Adv. Signal Process. 2010, 62 (2010)
Guler, I., Ubeyli, E.D.: Multiclass support vector machines for EEG-signals classification. IEEE Trans. Inf Technol. Biomed. 11(2), 117–126 (2007)
Güler, N.F., Ubeyli, E.D., Güler, I.: Recurrent neural networks employing Lyapunov exponents for EEG signals classification. Expert Syst. Appl. 29(3), 506–514 (2005)
Yang, S., Zhang, A., Zhang, J., Zhang, W.: A new chaotic feature for EEG classification based seizure diagnosis. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4651–4655. IEEE (2017)
Ubeyli, E.D., Güler, I.: Features extracted by eigenvector methods for detecting variability of EEG signals. Pattern Recogn. Lett. 28(5), 592–603 (2007)
Ubeyli, E.D.: Analysis of EEG signals by combining eigenvector methods and multiclass support vector machines. Comput. Biol. Med. 38(1), 14–22 (2008)
Maragos, P., Sun, F.K.: Measuring the fractal dimension of signals: morphological covers and iterative optimization. IEEE Trans. Signal Process. 41(1), 108 (1993)
Sadati, N., Mohseni, H.R., Maghsoudi, A.: Epileptic seizure detection using neural fuzzy networks. In: 2006 IEEE International Conference on Fuzzy Systems, pp. 596– 600. IEEE (2006)
Güler, I., Ubeyli, E.D.: Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients. J, Neurosci. Methods 148(2), 113–121 (2005)
Ubeyli, E.D.: Wavelet/mixture of experts network structure for EEG signals classification. Expert Syst. Appl. 34(3), 1954–1962 (2008)
Tzallas, A.T., Tsipouras, M.G., Fotiadis, D.I.: Epileptic seizure detection in EEGs using time–frequency analysis. IEEE Trans. Inf Technol. Biomed. 13(5), 703–710 (2009)
Meisel, L., Johnson, M., Cote, P.: Box-counting multifractal analysis. Phys. Rev. A 45(10), 6989 (1992)
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. (TIST) 2(3), 27 (2011)
Ghosh, D., Dutta, S., Chakraborty, S.: Multifractal detrended cross-correlation analysis for epileptic patient in seizure and seizure free status. Chaos, Solitons Fractals 67, 1–10 (2014)
Alam, S.S., Bhuiyan, M.I.H.: Detection of seizure and epilepsy using higher order statistics in the EMD domain. IEEE J. Biomed. Health Inf. 17(2), 312–318 (2013)
Wang, Y., Zhou, W., Yuan, Q., Li, X., Meng, Q., Zhao, X., Wang, J.: Comparison of ictal and interictal EEG signals using fractal features. Int. J. Neural Syst. 23(06), 1350028 (2013)
Andrzejak, R.G., Lehnertz, K., Mormann, F., Rieke, C., David, P., Elger, C.E.: 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 64(6), 061907 (2001)
Dhiman, R., Saini, J., et al.: Genetic algorithms tuned expert model for detection of epileptic seizures from EEG signatures. Appl. Soft Comput. 19, 8–17 (2014)
Kaya, Y., Uyar, M., Tekin, R., Yıldırım, S.: 1D-local binary pattern based feature extraction for classification of epileptic EEG signals. Appl. Math. Comput. 243, 209–219 (2014)
Zandi, A.S., Tafreshi, R., Javidan, M., Dumont, G.A.: Predicting epileptic seizures in scalp eeg based on a variational bayesian gaussian mixture model of zero-crossing intervals. IEEE Trans. Biomed. Eng. 60(5), 1401–1413 (2013)
Yang, S., Shen, I.-F.: Manifold analysis in reconstructed state space for nonlinear signal classification. In: Huang, D.-S., Heutte, L., Loog, M. (eds.) ICIC 2007. LNCS, vol. 4681, pp. 930–937. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-74171-8_94
Duque-Muñoz, L., Espinosa-Oviedo, J.J., Castellanos-Dominguez, C.G.: Identification and monitoring of brain activity based on stochastic relevance analysis of short–time EEG rhythms. Biomed. Eng. Online 13(1), 123 (2014)
Acknowledgment
This work is supported by Shanghai Science and Technology Commission (grant No. 17511104203) and NSFC (grant NO. 61472087).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Yang, S., Li, G., Lu, J., Sun, Y., Huang, Z. (2019). EEG-Based Seizure Diagnosis Using Discriminative Fractal Features from Feature Selection. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Application. ICIC 2019. Lecture Notes in Computer Science(), vol 11643. Springer, Cham. https://doi.org/10.1007/978-3-030-26763-6_42
Download citation
DOI: https://doi.org/10.1007/978-3-030-26763-6_42
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-26762-9
Online ISBN: 978-3-030-26763-6
eBook Packages: Computer ScienceComputer Science (R0)