Abstract
Finding efficient and effective automatic methods for the identification and prediction of epileptic seizures is highly desired, due to the relevance of this brain disorder. Despite the large amount of research going on in identification and prediction solutions, still it is required to find confident methods suitable to be used in real applications. In this paper, we discuss the principal challenges found in epilepsy identification, when it is carried on offline analyzing electro-encephalograms (EEG) recordings. Indeed, we present the results obtained so far in our research group, with a system based on multi-resolution analysis and feed-forward neural networks, which focus on tackling three important challenges found in this type of problems: noise reduction, feature extraction and pertinence of the classifier. A 3-fold validation of our strategy reported an accuracy of 99.26 ± 0.26 %, a sensitive of 98.93 % and a specificity of 99.59 %, using data provided by the University of Bonn. Several combinations of filters and wavelet transforms were tested, found that the best results occurs when a Chebyshev II filter was used to eliminate noise, 5 characteristics were obtained using a Discrete Wavelet Transform (DWT) with a Haar wavelet and a feed-forward neural network with 18 hidden nodes was used for classification.
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
Similar content being viewed by others
References
Addison, P.S.: The Illustrated Wavelet Transform Handbook: Introductory Theory and Applications in Science, Engineering Medicine and Finance. IOP Publishing, England (2002)
Andrzejak, R.G., Lehnertz, K., Mormann F., Rieke D., David P., Elger, C.: 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), 061907-1, 061907-8 (2001). doi:10.1103/PhysRevE.64.061907
Anusha, K.S., Mathew, T.M., Subha, D.P.: Classification of normal and epileptic EEG signal using time & frequency domain features through artificial neural network. In: International Conference on Advances in Computing and Communications. IEEE (2012)
Bashashati, M., Fatourechi, R., Ward, K., Birch, G.E.: A survey of signal processing algorithms in brain-computer interfaces based on electrical brain signals. J. Neural Eng. 4(2), R32–R57 (2007)
Berger, H.: Über das elektrenkephalogramm des menschen. Arch. F. Psichiat. 87, 527–570 (1929)
Cetin, E., Gerek, O.N., Yardimci, Y.: Equiripple FIR filter design by the FFT algorithm. IEEE Signal Process. Mag. 14, 60–64 (1997)
Durka, P.: Matching Pursuit and Unification in EEG Analysis. Artech House Norwood, Boston (2007)
EU FTP 7. ICT -2007 5. Epilepsy and seizures. EPILEPSIAE project (Online).: Advanced ICT for risk assessment and patient safety grant 211713. Available: http://www.epilepsiae.eu/about_project/epilepsy_and_seizures/, (2013). Accessed in 27 Nov 2013 (2913)
García-González, Y.: Modelos y algoritmos para redes Neuronales recurrentes basadas en wavelets aplicados a la detección de intrusos. Master thesis, Department of Computing, Electronics and Mechatronics, Universidad de las Américas, Puebla (2011)
Ghosh-Dastidar, S., Adeli, H., Dadmehr, N.: Mixed-band wavelet-chaos neural network methodology for epilepsy and epileptic seizure detection. IEEE Trans. Biomed. Eng. 54(9), 1545–1551 (2007)
Gómez-Gil, P.: Tutorial: an introduction to the use of artificial neural networks. Available at: http://ccc.inaoep.mx/~pgomez/tutorials/ATutorialOnANN2012.zip
Gómez-Gil, P., Ramírez-Cortés, J.M., Pomares Hernández, S.E., Alarcón-Aquino, V.: A neural network scheme for long-term forecasting of chaotic time series. Neural Process. Lett. 33(3), 215–233 (2011)
Güler, N.F., Übeylib, E.D., Güler, I.: Recurrent neural networks employing Lyapunov exponents for EEG signals classification. Expert Syst. Appl. 29, 506–514 (2005)
Huang, N.E., Shen, Z., Long, S.R., Wu, M.L.C., Shih, H.H., Zheng, Q.N., et al.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. P Roy Soc Lond a Mat 1998(454), 903–995 (1971)
Husain, S.J., Rao, K.S.: Epileptic seizures classification from EEG signals using neural networks. In: International Conference on Information and Network Technology, (37) (2012)
Ihle, M., Feldwisch-Drentrupa, H., Teixeirae, C., Witonf, A., Schelter, B., Timmerb, J., Schulze-Bonhagea, A.: EPILEPSIAE—a European epilepsy database. Comput. Methods Programs Biomed. 106, 127–138 (2012)
Juárez-Guerra, E., Gómez-Gil, P., Alarcon-Aquino, V.: Biomedical signal processing using wavelet-based neural networks. In: Special Issue: Advances in Pattern Recognition, Research in Computing Science, vol. 61, pp. 23–32 (2013a)
Juárez-Guerra, E., Alarcón-Aquino, V., Gómez-Gil P.: Epilepsy seizure detection in eeg signals using wavelet transforms and neural networks. To be published in: Proceedings of the Virtual International Joint Conference on Computer, Information and Systems Sciences and Engineering (CISSE 2013), 12–14 Dec 2013 (2013b)
Kaur, S.: Detection of epilepsy disorder by EEG using discrete wavelet transforms. Thesis in Master of Engineering in Electronic Instrumentation and Control, Thapar University, July 2012
Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of the International Joint Conference on Artificial Intelligence, vol. 14, pp. 1137–1145. Lawrence Erlbaum Associates Ltd (1995)
Mandic, D., Rehman, N., Wu, Z., Huang, N.: Empirical mode decomposition-based time-frequency analysis of multivariate signals. IEEE Signal Process. Mag. 30(6), 74–86 (2013)
Martis, J.R., Acharya, U.R., Tan, J.H., Petznick, A., Yanti, R., Chua, C.K., Ng, E.K.Y., Tong, L.: Application of empirical mode decomposition (EMD) for automated detection of epilepsy using EEG signals. Int. J. Neural Syst. 22(6), 1250027-1, 16 (2012)
Martosini, A.N.: Animal electricity, CA 2+ and muscle contraction. A brief history of muscle research. Acta Biochim. Pol. 47(3), 493–516 (2000)
MathWorks Incorporation: Documentation, Signal Processing Toolbox, Analog and Digital Filters. Matlab R2013b (2013)
Mirzaei, A., Ayatollahi, A., Vavadi, H.: Statistical analysis of epileptic activities based on histogram and wavelet-spectral entropy. J. Biomed. Sci. Eng. 4, 207–213 (2011)
Petrosian, A., Prokhorov, D., Homan, R., Dasheiff, R., Wunsch II, D.: Recurrent neural network based prediction of epileptic seizures in intra- and extracranial EEG. Neurocomputing 30, 201–218 (2000)
Proakis, J.G., Manolakis, D.G.: Digital signal processing. Principles, algorithms and applications, 3rd edn. Englewood Prentice Hall, Cliffs (1996)
Ravish, D.K., Devi, S.S.: Automated seizure detection and spectral analysis of EEG seizure time series. Eur. J. Sci. Res. 68(1), 72–82 (2012)
Subasi, A., Ercelebi, E.: Classification of EEG signals using neural network and logistic regression. J Comput. Methods Programs Biomed. 78, 87–99 (2005)
Sung, J.Y., Jin, B.P., Yoon, H.C.: Direct adaptive control using self recurrent wavelet neural network via adaptive learning rates for stable path tracking of mobile robots. In: Proceedings of the 2005 American Control Conference, pp. 288–293. Portland (2005)
Sunhaya, S., Manimegalai, P.: Detection of epilepsy disorder in EEG signal. Int. J. Emerg Dev. 2(2), 473–479 (2012)
Tzallas, A.T., Tsipouras, M.T., Fotiadis, D.I.: Epileptic seizure detection in EEGs using time-frequency analysis. IEEE Trans. Inf. Technol. Biomed. 13(5), 703–710 (2009)
Universitat Bonn, Kinik für Epiteptologie: EEG time series download page. URL: http://epileptologie-bonn.de/cms/front_content.php?idcat=193, Last Accessed at 12 Dec 2013
Wacker, M., Witte, H.: Time-frequency techniques in biomedical signal analysis: a tutorial review of similarities and differences. Methods Inf. Med. 52(4), 297–307 (2013)
Wang, Y. et al.: Comparison of ictal and interictal EEG signals using fractal features. Int. J. Neural Syst. 23(6), 1350028 (11 pages) (2013)
Acknowledgments
The first author gratefully acknowledges the financial support from the “Universidad Autónoma de Tlaxcala” and PROMEP by scholarship No. UATLX-244. This research has been partially supported by CONACYT, project grant No. CB-2010-155250.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Gómez-Gil, P., Juárez-Guerra, E., Alarcón-Aquino, V., Ramírez-Cortés, M., Rangel-Magdaleno, J. (2014). Identification of Epilepsy Seizures Using Multi-resolution Analysis and Artificial Neural Networks. In: Castillo, O., Melin, P., Pedrycz, W., Kacprzyk, J. (eds) Recent Advances on Hybrid Approaches for Designing Intelligent Systems. Studies in Computational Intelligence, vol 547. Springer, Cham. https://doi.org/10.1007/978-3-319-05170-3_23
Download citation
DOI: https://doi.org/10.1007/978-3-319-05170-3_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-05169-7
Online ISBN: 978-3-319-05170-3
eBook Packages: EngineeringEngineering (R0)