Abstract
Epilepsy is one of the diseases that are more subject to consultation in neurological clinics. To help neurologists to accurately diagnose this disease, several technological tools have been developed. Electroencephalography (EEG) of scalp or deep is a signal acquisition tool from electrical discharges of the brain areas. These signals are often accompanied by transient events commonly called interictal paroxystic events (IPE) or spikes of short durations. Analysis of these IPE could help with the diagnosis of drug-resistant epilepsy. With this intention, we will first of all seek to detect IPE, by separating them from the basic activity of signal EEG. In this paper, we propose spike detection method based on Smoothed Nonlinear Energy Operator (SNEO) using adaptive threshold. Then we will implement a new approach using principal components analysis (PCA) before classification to separate the events detected according to their morphologies. The objective in the long term is to characterize their space-time distribution over all the duration of the EEG signal.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Löscher, W., Schmidt, D.: Modern antiepileptic drug development has failed to deliver: ways out of the current dilemma. Epilepsia 52(4), 657–678 (2011)
Sagher, O.: Editorial: epilepsy surgery. J. Neurosurg. 118, 167–168 (2012)
Bourien, J., Bartolomei, F., Bellanger, J.J.: A method to identify reproducible subsets of co-activated structures during interictal spikes. Application to intra-cerebral EEG in temporal lobe epilepsy. Clin. Neurophysiol. 116, 443–455 (2005)
Tzallas, A.T., Tsipouras, M.G., Tsalikakis, D.G., Karvounis, E.C., Astrakas, L., Konitsiotis, S., Tzaphlidou, M.: Automated epileptic seizure detection methods: a review study. Department of Medical Physics, Medical School, University of Ioannina, Ioannina, Greece (2012)
Khouma, O., Ndiaye, M.L., Farsi, S.M., Montois, J.-J., Diop, I., Diouf, B.: Comparative methods of spike detection in epilepsy. In: Science and Information Conference (SAI), pp. 749–745. IEEE, London (2015)
Kaiser, J.F.: Some useful properties of teager’s energy operators. In: Proceedings of IEEE ICASSP 1993, Minneapolis, NN, April 1993, vol. 3, pp. 149–152 (1993)
Hassanpour, H., Boashash, B.: A time-frequency approach for EEG spike detection. Iran J. Energy Environ. 2(4), 390–395 (2011)
Mukhopadhyay, S., Ray, G.C.: A new interpretation of nonlinear energy operator and its efficiency in spike detection. IEEE Trans. Biomed. Eng. 49(12), 1526–1533 (2002)
Pages, J., Escofier, B.: Introduction à l’analyse en composantes principales à partir de l’étude d’un tableau de notes. Méthode d’analyses statistiques multidimensionnelles en didactiques des mathématiques, IRMAR et IRESTE NANTES, pp. 27–29 (1995)
Voisine, N.: Approche adaptative de coopération hiérarchique de méthodes de segmentation, application aux images multi composantes. Ph.D. thesis, Université de Rennes 1, France (2002)
McCune, B., Grace, J.B.: Analysis of Ecological Communities. MjM Software Design, Gleneden Beach (2002)
Mallat, S.: A Wavelet Tour of Signal Processing. Academic Press, Cambridge (1998)
Fawcett, T.: An introduction to ROC analysis. Pattern Recogn. Lett. 27, 861–874 (2006). Science Direct
Candillier, L., Tellier, I., Torre, F., Bousquet, O.: Évaluation en cascade d’algorithmes de clustering. CAP Lille (2006)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Khouma, O. et al. (2018). Classification Model of Spikes Morphology Using Principal Components Analysis in Drug-Resistant Epilepsy. In: M. F. Kebe, C., Gueye, A., Ndiaye, A. (eds) Innovation and Interdisciplinary Solutions for Underserved Areas. CNRIA InterSol 2017 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 204. Springer, Cham. https://doi.org/10.1007/978-3-319-72965-7_27
Download citation
DOI: https://doi.org/10.1007/978-3-319-72965-7_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-72964-0
Online ISBN: 978-3-319-72965-7
eBook Packages: Computer ScienceComputer Science (R0)