Abstract
Epileptic seizure is the common neurological disorder, which is generally identified by electroencephalogram (EEG) signals. In this paper, a new feature extraction methodology based on optimum allocation sampling (OAS) and Teager energy operator (TEO) is proposed for detection of seizure EEG signals. The OAS scheme selects the finite length homogeneous sequence from non-homogeneous recorded EEG signal. The trend of selected sequence by OAS is still non-linear, which is analyzed by non-linear operator TEO. The TEO convert non-linear but homogenous EEG sequence into amplitude–frequency modulated (AM–FM) components. The statistical measures of AM–FM components used as input features to least squares support vector machine classifier for classification of seizure and seizure-free EEG signals. The proposed methodology is evaluated on a benchmark epileptic seizure EEG database. The experimental results demonstrate that the proposed scheme has capability to effectively distinguish seizure and seizure-free EEG signals.
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References
Global campaign against epilepsy. Programme for Neurological Diseases, Neuroscience (World Health Organization), International Bureau of Epilepsy, and International League against Epilepsy. Atlas: epilepsy care in the world. World Health Organization; 2005.
Mormann F, Andrzejak RG, Elger CE, Lehnertz K. Seizure prediction: the long and winding road. Brain. 2006;130(2):314–33.
Ray GC. An algorithm to separate nonstationary part of a signal using mid-prediction filter. IEEE Trans Signal Process. 1994;42(9):2276–9.
Mukhopadhyay S, Ray GC. A new interpretation of nonlinear energy operator and its efficacy in spike detection. IEEE Trans Biomed Eng. 1998;45(2):180–7.
Mosh SL, Perucca E, Ryvlin P, Tomson T. Epilepsy: new advances. Lancet. 2015;385(9971):884–98.
Duque-Muz L, Espinosa-Oviedo JJ, Castellanos-Dominguez CG. Identification and monitoring of brain activity based on stochastic relevance analysis of shorttime EEG rhythms. Biomed Eng Online. 2014;13(1):123.
Srinivasan V, Eswaran C, Sriraam AN. Artificial neural network based epileptic detection using time-domain and frequency-domain features. J Med Syst. 2005;29(6):647–60.
Polat K, Gne S. Classification of epileptiform EEG using a hybrid system based on decision tree classifier and fast Fourier transform. Appl Math Comput. 2007;187(2):1017–26.
Uthayakumar R, Easwaramoorthy D. Epileptic seizure detection in EEG signals using multifractal analysis and wavelet transform. Fractals. 2013;21(02):1350011.
Guo L, Rivero D, Pazos A. Epileptic seizure detection using multiwavelet transform based approximate entropy and artificial neural networks. J Neurosci Methods. 2010;193(1):156–63.
Altunay S, Telatar Z, Erogul O. Epileptic EEG detection using the linear prediction error energy. Expert Syst Appl. 2010;37(8):5661–5.
Joshi V, Pachori RB, Vijesh A. Classification of ictal and seizure-free EEG signals using fractional linear prediction. Biomed Signal Process Control. 2014;9:1–5.
Siuly S, Li Y, Wen PP. Clustering technique-based least square support vector machine for EEG signal classification. Comput Methods Programs Biomed. 2011;104(3):358–72.
Gandhi T, Panigrahi BK, Bhatia M, Anand S. Expert model for detection of epileptic activity in EEG signature. Expert Syst Appl. 2010;37(4):3513–20.
Pachori RB, Patidar S. Epileptic seizure classification in EEG signals using second-order difference plot of intrinsic mode functions. Comput Methods Programs Biomed. 2014;113(2):494–502.
Bajaj V, Pachori RB. Epileptic seizure detection based on the instantaneous area of analytic intrinsic mode functions of EEG signals. Biomed Eng Lett. 2013;3(1):17–21.
Bajaj V, Pachori RB. Classification of seizure and nonseizure EEG signals using empirical mode decomposition. IEEE Trans Inf Technol Biomed. 2012;16(6):1135–42.
Sharma R, Pachori RB. Classification of epileptic seizures in EEG signals based on phase space representation of intrinsic mode functions. Expert Syst Appl. 2015;42(3):1106–17.
Hassan AR, Siuly S, Zhang Y. Epileptic seizure detection in EEG signals using tunable-Q factor wavelet transform and bootstrap aggregating. Comput Methods Programs Biomed. 2016;137:247–59.
Patidar S, Panigrahi T. Detection of epileptic seizure using Kraskov entropy applied on tunable-Q wavelet transform of EEG signals. Biomed Signal Process Control. 2017;34:74–80.
Acharya UR, Sree SV, Ang PCA, Yanti R, Suri JS. Application of non-linear and wavelet based features for the automated identification of epileptic EEG signals. Int J Neural Syst. 2012;22(02):1250002.
Srinivasan V, Eswaran C, Sriraam N. Approximate entropy-based epileptic EEG detection using artificial neural networks. IEEE Trans Inf Technol Biomed. 2007;11(3):288–95.
Lehnertz K, Elger CE. Spatio-temporal dynamics of the primary epileptogenic area in temporal lobe epilepsy characterized by neuronal complexity loss. Electroencephalogr Clin Neurophysiol. 1995;95(2):108–17.
Beyli ED. Lyapunov exponents/probabilistic neural networks for analysis of EEG signals. Expert Syst Appl. 2010;37(2):985–92.
Li Y. A novel statistical algorithm for multiclass EEG signal classification. Eng Appl Artif Intell. 2014;34:154–67.
Siuly S, Li Y. Discriminating the brain activities for brain–computer interface applications through the optimal allocation-based approach. Neural Comput Appl. 2015;26(4):799–811.
Siuly S, Wang H, Zhang Y. Detection of motor imagery EEG signals employing Naive Bayes based learning process. Measurement. 2016;86:148–58.
Kvedalen E. Signal processing using the Teager Energy Operator and other nonlinear operators. Master, University of Oslo Department of Informatics, p. 21 (2003).
Zhou G, Hansen JH, Kaiser JF. Classification of speech under stress based on features derived from the nonlinear Teager energy operator. In: Proceedings of the 1998 IEEE international conference on acoustics, speech and signal processing, May 1998, vol. 1, p. 549–552 (1998).
Cao M, Xu W, Ostachowicz W, Su Z. Damage identification for beams in noisy conditions based on Teager energy operator-wavelet transform modal curvature. J Sound Vib. 2014;333(6):1543–53.
Santhanam B, Maragos P. Energy demodulation of two-component AM–FM signal mixtures. IEEE Signal Process Lett. 1996;3(11):294–8.
Andrzejak RG, Lehnertz K, Mormann F, Rieke C, David P, Elger CE. 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. 2001;64(6):061907.
Sample Size Calculator. https://www.surveysystem.com/sscalc.htm.
Cochran WG. Sampling techniques. 3rd ed. New York: Wiley; 1977.
Maragos P, Kaiser JF, Quatieri TF. Energy separation in signal modulations with application to speech analysis. IEEE Trans Signal Process. 1993;41(10):3024–51.
Boudraa AO, Cexus JC, Salzenstein F, Guillon L. IF estimation using empirical mode decomposition and nonlinear Teager energy operator. In: First international symposium on control, communications and signal processing, p. 45–48 (2004).
De Veaux RD, Velleman PF, Bock DE. Intro stats. 3rd ed. Boston: Pearson Addison Wesley; 2008.
Suykens JA, Vandewalle J. Least squares support vector machine classifiers. Neural Process Lett. 1999;9(3):293–300.
Li Y, Wen P. Analysis and classification of EEG signals using a hybrid clustering technique. In: 2010 IEEE/ICME international conference on complex medical engineering (CME), July 2010, p. 34–39 (2010).
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Taran, S., Bajaj, V. & Siuly, S. An optimum allocation sampling based feature extraction scheme for distinguishing seizure and seizure-free EEG signals. Health Inf Sci Syst 5, 7 (2017). https://doi.org/10.1007/s13755-017-0028-7
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DOI: https://doi.org/10.1007/s13755-017-0028-7