A Multistage System for Automatic Detection of Epileptic Spikes

Nguyen Thi Anh-Dao, Nguyen Linh-Trung, Ly Van Nguyen, Tan Tran-Duc, Nguyen The Hoang Anh, Boualem Boashash

Abstract


A multistage automatic detection system for epileptic spikes is introduced as an assistant tool for epileptic analysis and diagnosis based on electroencephalogram (EEG). The system consists of four stages: preprocessing, feature extraction, classifier and expert system. Multiple state-of-the-art signal processing and machine learning techniques including wavelet transform, spectral filtering, artificial neural network are utilized in order to improve the ability of the overall system stage by stage. Compared to other works, our contributions are three-fold: peaks in the EEG recording are categorized into two groups of non-epileptic spikes and possible epileptic spikes by a committee of three perceptrons; appropriate mother wavelet and wavelet scales are selected for the best system performance; and, based on the neurological fact that an epileptic spike is usually followed by a slow wave, a simple expert system is presented to eliminate pseudo-spikes which are closely analogous to true epileptic spikes. Experimental results show that the proposed system is capable of detecting epileptic spikes efficiently.

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References


K. Indiradevi, E. Elias, P. Sathidevi, S. Dinesh Nayak, and K. Radhakrishnan, “A multi-level wavelet approach for automatic detection of epileptic spikes in the electroencephalogram,” Computers in Biology and Medicine, vol. 38, no. 7, pp. 805–816,

J. Gotman, “Automatic detection of seizures and spikes,” Journal of Clinical Neurophysiology, vol. 16, no. 2, pp. 130–140, 1999.

S. B. Wilson and R. Emerson, “Spike detection: A review and comparison of algorithms,” Clinical Neurophysiology, vol. 113, no. 12, pp. 1873–1881, 2002.

A. T. Tzallas, M. G. Tsipouras, D. G. Tsalikakis, E. C. Karvounis, L. Astrakas, S. Konitsiotis, and M. Tzaphlidou, “Automated epileptic seizure detection methods: A review study,” Epilepsy-histological, electroencephalographic and psychological aspects,

edited by D. Stevanovic, pp. 75–98, 2012.

N. Acır, I. Öztura, B. Baklan, and C. Güzeli¸s, “Automatic Detection of Epileptiform Events in EEG by a Three-Stage Procedure Based on Artificial Neural Networks,” IEEE Transactions on Biomedical Engineering, vol. 52, no. 1, pp. 30–40, 2005.

P. Y. Ktonas and J. R. Smith, “Quantification of abnormal EEG spike characteristics,” Computers in biology and medicine, vol. 4, no. 2, pp. 157–163, 1974.

J. Gotman and P. Gloor, “Automatic recognition and quantification of interictal epileptic activity in the human scalp EEG,” Electroenceph. Clin. Neurophysiol, vol. 41, pp. 513–529, 1976.

G. Pfurtscheller and G. Fischer, “A new approach to spike detection using a combination of inverse and matched filter techniques,” Electroencephalography and clinical neurophysiology, vol. 44, no. 2, pp. 243–247, 1978.

J. Gotman and L. Wang, “State-dependent spike detection: concepts and preliminary results,” Electroencephalography and clinical Neurophysiology, vol. 79, no. 1, pp. 11–19, 1991.

J. Gotman, “Automatic recognition of epileptic seizures in the EEG,” Electroencephalography and clinical Neurophysiology, vol. 54, no. 5, pp. 530–540, 1982.

A. Ossadtchi, S. Baillet, J. Mosher, D. Thyerlei, W. Sutherling, and R. Leahy, “Automated interictal spike detection and source localization in magnetoencephalography using independent components analysis and spatio-temporal clustering,” Clinical Neurophysiology, vol. 115, no. 3, pp. 508–522, 2004.

G. Xu, J. Wang, Q. Zhang, S. Zhang, and J. Zhu, “A spike detection method in EEG based on improved morphological filter,” Computers in biology and medicine, vol. 37, no. 11, pp. 1647–1652, 2007.

Ö. Özdamar and T. Kalayci, “Detection of spikes with artificial neural networks using raw EEG,” Computers and Biomedical Research, vol. 31, no. 2, pp. 122–142, 1998.

C. C. Pang, A. R. Upton, G. Shine, and M. V. Kamath, “A comparison of algorithms for detection of spikes in the electroencephalogram,” IEEE Transactions on Biomedical Engineering, vol. 50, no. 4, pp. 521–526, 2003.

T.-W. Shen, X. Kuo, and Y.-L. Hsin, “Ant K-Means Clustering Method on Epileptic Spike Detection,” in Natural Computation, 2009. ICNC’09. Fifth International Conference on, vol. 6. IEEE, 2009, pp. 334–338.

N. Acır and C. Güzeli¸s, “Automatic spike detection in EEG by a two-stage procedure based on support vector machines,” Computers in Biology and Medicine, vol. 34, no. 7, pp. 561–575, 2004.

H. Hamid and B. Boashash, “A time-frequency approach for EEG spike detection,” Iranica Journal of Energy & Environment, vol. 2, no. 4, pp. 390–395, 2011.

H. S. Liu, T. Zhang, and F. S. Yang, “A multistage, multimethod approach for automatic detection and classification of epileptiform EEG,” IEEE Transactions on Biomedical Engineering, vol. 49, no. 12, pp. 1557–1566, 2002.

C. F. Boos, F. M. d. A. G. R. Scolaro, and V. P. Maria do Carmo, Automatic Detection of Paroxysms in EEG Signals using Morphological Descriptors and Artificial Neural Networks. INTECH Open Access Publisher, 2011.

E. Niedermeyer and F. L. da Silva, Electroencephalography: basic principles, clinical applications, and related fields. Lippincott

Williams & Wilkins, 2005.

J. S. Barlow, “Methods of analysis of nonstationary eegs, with emphasis on segmentation techniques: a comparative review.” Journal of Clinical Neurophysiology, vol. 2, no. 3, pp. 267–304, 1985.

H. Azami, S. Sanei, K. Mohammadi, and H. Hassanpour, “A hybrid evolutionary approach to segmentation of non-stationary signals,” Digital Signal Processing, vol. 23, no. 4, pp. 1103–1114, 2013.

B. Boashash, “Heuristic formulation of time-frequency distributions,” 2003.

J. P. Amezquita-Sanchez and H. Adeli, “A new music-empirical wavelet transform methodology for time–frequency analysis of noisy nonlinear and non-stationary signals,” Digital Signal Processing, vol. 45, pp. 55–68, 2015.

V. Sucic, N. Saulig, and B. Boashash, “Analysis of local time-frequency entropy features for nonstationary signal components time supports detection,” Digital Signal Processing, vol. 34, pp. 56–66, 2014.

B. Boashash and G. Azemi, “A review of time–frequency matched filter design with application to seizure detection in multichannel newborn eeg,” Digital Signal Processing, vol. 28, pp. 28–38, 2014.

B. Boashash, N. A. Khan, and T. Ben-Jabeur, “Time–frequency features for pattern recognition using high-resolution tfds: A tutorial review,” Digital Signal Processing, vol. 40, pp. 1–30, 2015.

O. Rioul and M. Vetterli, “Wavelets and signal processing,” IEEE Signal Processing Magazine, vol. 8, no. 4, pp. 14–38, 1991.

I. Daubechies, Ten lectures on wavelets. SIAM, 1992, vol. 61.

O. Rioul and M. Vetterli, “Wavelets and signal processing,” IEEE signal processing magazine, vol. 8, no. LCAV-ARTICLE-1991-005, pp. 14–38, 1991.

M. Alfaouri and K. Daqrouq, “Ecg signal denoising by wavelet transform thresholding,” American Journal of applied sciences, vol. 5, no. 3, pp. 276–281, 2008.

V. J. Samar, A. Bopardikar, R. Rao, and K. Swartz, “Wavelet analysis of neuroelectric waveforms: a conceptual tutorial,” Brain and language, vol. 66, no. 1, pp. 7–60, 1999.

S. J. Schiff, A. Aldroubi, M. Unser, and S. Sato, “Fast wavelet transformation of EEG,” Electroencephalography and Clinical Neurophysiology, vol. 91, no. 6, pp. 442–455, 1994.

T. Kalayci and Ö. Özdamar, “Wavelet preprocessing for automated neural network detection of EEG spikes,” Engineering in Medicine and Biology Magazine, IEEE, vol. 14, no. 2, pp. 160–166, 1995.

Z. Nenadic and J. W. Burdick, “Spike detection using the continuous wavelet transform,” Biomedical Engineering, IEEE Transactions on, vol. 52, no. 1, pp. 74–87, 2005.

M. Latka, Z. Was, A. Kozik, and B. J. West, “Wavelet analysis of epileptic spikes,” Physical Review E, vol. 67, no. 5, p. 052902, 2003.

R. Q. Quiroga, Z. Nadasdy, and Y. Ben-Shaul, “Unsupervised spike detection and sorting with wavelets and superparamagnetic clustering,” Neural computation, vol. 16, no. 8, pp. 1661–1687, 2004.

S. Ghosh-Dastidar, H. Adeli, and N. Dadmehr, “Mixed-band wavelet-chaos-neural network methodology for epilepsy and epileptic seizure detection,” Biomedical Engineering, IEEE Transactions on, vol. 54, no. 9, pp. 1545–1551, 2007.

E. D. Übeyli, “Combined neural network model employing wavelet coefficients for eeg signals classification,” Digital Signal Processing, vol. 19, no. 2, pp. 297–308, 2009.

Y. Khan and J. Gotman, “Wavelet based automatic seizure detection in intracerebral electroencephalogram,” Clinical Neurophysiology, vol. 114, no. 5, pp. 898–908, 2003.

H. Adeli, S. Ghosh-Dastidar, and N. Dadmehr, “A wavelet-chaos methodology for analysis of eegs and eeg subbands to detect seizure and epilepsy,” Biomedical Engineering, IEEE Transactions on, vol. 54, no. 2, pp. 205–211, 2007.

X. Mi, H. Ren, Z. Ouyang, W. Wei, and K. Ma, “The use of the Mexican Hat and the Morlet wavelets for detection of ecological patterns,” Plant Ecology, vol. 179, no. 1, pp. 1–19, 2005.

C. Cortes and V. Vapnik, “Support-vector networks,” Machine learning, vol. 20, no. 3, pp. 273–297, 1995.

C. A. Bouman, M. Shapiro, G. Cook, C. B. Atkins, and H. Cheng, “CLUSTER: An unsupervised algorithm for modeling Gaussian mixtures,” 1997.

C. M. Bishop and N. M. Nasrabadi, Pattern recognition and machine learning. springer New York, 2006, vol. 1.

J. Lafferty, A. McCallum, and F. C. Pereira, “Conditional random fields: Probabilistic models for segmenting and labeling sequence data,” in Proceedings of the Eighteenth International Conference on Machine Learning, San Francisco, CA, USA, 2001,

pp. 282–289.

D. E. Rumelhart, G. E. Hintont, and R. J. Williams, “Learning representations by back-propagating errors,” Nature, vol. 323, no. 6088, pp. 533–536, 1986.

Casson, A. J., E. Luna, and E. Rodriguez-Villegas, “Performance metrics for the accurate characterisation of interictal spike detection algorithms,” Journal of neuroscience methods, vol. 177, no. 2, pp. 479–487, 2009.

J. D. Frost, “Automatic Recognition and Characterization of Epileptiform Discharges in the Human EEG,” Clinical Neurophysiology, no. 231–249, 1985.

A. A. Dingle, R. D. Jones, G. Carroll, and W. Fright, “A multistage system to detect epileptiform activity in the EEG,” Biomedical Engineering, IEEE Transactions on, vol. 40, no. 12, pp. 1260–1268, 1993.

C. A. Lima, A. L. Coelho, and M. Eisencraft, “Tackling EEG signal classification with least squares support vector machines: A sensitivity analysis study,” Computers in Biology and Medicine, vol. 40, no. 8, pp. 705–714, 2010.

A. R. Johansen, J. Jin, T. Maszczyk, J. Dauwels, S. S. Cash, and M. B. Westover, “Epileptiform spike detection via convolutional neural networks,” in Acoustics, Speech and Signal Processing (ICASSP), 2016 IEEE International Conference on. IEEE, 2016, pp.

–758.




DOI: http://dx.doi.org/10.21553/rev-jec.166

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