Detection of epileptic spikes in electroencephalographic signals for patients with temporal lobe epilepsy using wavelets
Main Article Content
Keywords
multiresolution analysis, biorthogonal wavelet, detection of epileptic spikes.
Abstract
This paper describes a method for detecting epileptic spikes in a record electroencephalographic (EEG) surface by taking a single channel. We identified a pattern using multiresolution analysis with a biorthogonal wavelet after processing and analyzing the Wavelet Toolbox of Matlab, 207 records and 132 records of tips tricks previously classified by Neurophysiologist. This pattern enabled an algorithm for detecting spikes in patients with refractory temporal lobe epilepsy, based on the maximum voltage in each of the six levels of reconstruction using biorthogonal 3.7 wavelet. The algorithm was applied on records of patients with epilepsy, getting a sensitivity of 92% and a specificity of 80% in the diagnosis of epileptic spikes.
PACS: 87.57.-s
MSC: 65T60, 42C40
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References
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[3] M. Dümpelmann and C. E. Elger. Visual and automatic investigation of epileptiform spikes in intracranial EEG recordings. Epilepsia, ISSN 0013–9580, 40(3), 275–285 (1999).
[4] J. Gotman and P. Gloor. Automatic recognition and quantification of interictal epileptic activity in human scalp EEG. Electroencephalography and clinical neurophysiology, ISSN 0013–4694, 4 I S, 13–29 (1976).
[5] I. Clark, R. Biscay and M. Echeverría. Virués, Multiresolution decomposition of non–stationary EEG signals: a preliminary study. Computers in Biology and Medicine, ISSN 0010–4825, 25(4) 373–382 (1995).
[6] L. Senhadji, J. L. Dillenseger, F. Wendling, C. Rocha and A. Kinie. Wavelet analysis of EEG for three–dimensional mapping of epileptic events. Annals of biomedical engineering, ISSN 0090–6964, 23(5) 543–552 (1995).
[7] K. S. Rakesh. Artificial Neural Network and Wavelet Based Automated Detection of Sleep Spindles, REM Sleep and Wake States. Journal of Medical Systems, ISSN 0148–5598, 32(4), 291–299 (2008).
[8] M. K. Kiymik, M. Akin and A. Subasi. Automatic recognition of alertness level by using wavelet transform and artificial neural network. Journal of neuroscience methods, ISSN 0165–0270, 139(2), 231–240 (2004).
[9] J. D. Z. Chen, Z. Lin, Q.Wu and R.W.McCallum. Non-invasive identification of gastric contractions from surface electrogastrogram using back-propagation neural networks. Medical Engineering & Physics, ISSN 1350–4533, 17(3), 219–225 (1995).
[10] L. M. Patnaik and O. K. Manyam. Epileptic EEG detection using neural networks and post–classification. Computer methods and programs in biomedicine, ISSN 0169–2607, 91(2), 100–109 (2008).
[11] Abdulhamit Subasi. Application of adaptive neuro-fuzzy inference system for epileptic seizure detection using wavelet feature extraction. Computers in Biology and Medicine, ISSN 0010–4825, 37(2), 227–244 (2007).
[12] 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, ISSN 0010–4825, 37(11), 1647–1652 (2007).
[13] F.I. Argoud, F. M. de Azevedo, J. M. Neto and E. Grillo. SADE3: an effective system for automated detection of epileptiform events in long-term EEG based on context information. Medical & biological engineering & computing, ISSN 0140–0118, 44(6), 459–470 (2006).
[14] D. Bosnyakova, A. Gabova, G. Kuznetsova, Y. Obukhov, I. Midzyanovskaya, D. Salonin, C. Van Rijn, A. Coenen, L. Tuomisto and G. Van Luijtelaar. Time– frequency analysis of spike-wave discharges using a modified wavelet transform. Journal of neuroscience methods, ISSN 0165–0270, 154(1,2), 80–88 (2006).
[15] H. Adeli, Z. Zhou and N. Dadmehr. Analysis of EEG records in an epileptic patient using wavelet transform. Journal of neuroscience methods, ISSN 0165– 0270, 123(1), 69–87 (2003).
[16] H. S. Park, Y. H. Lee, N. G. Kim, D. S. Lee and S. I. Kim. Detection of epileptiform activities in the EEG using neural network and expert system, DOI 10.1109/IEMBS.1997.756576. Studies in health technology and informatics, ISSN 0926–9630 52 Pt 2, 1255–1259 (1998).
[17] Ilmore, American Electroencephalographic Society guidelines in electroencephalography, evoked potentials, and polysomnography. Journal of clinical neurophysiology, ISSN 0736–0258, 11(1), 1–147 (1994).
[18] C. S. Burrus, R. A. Gopinath and H. Guo. Introduction to Wavelets and Wavelet Transforms: A Primer, ISBN 0–13–489600–9. Prentice Hall, New Jersey, 1997.
[19] Charles K. Chui. Wavelets: A Mathematical Tool for Signal Analysis, ISBN 0– 89871–384–6. SIAM, Monographs on Mathematical Modeling and Computation, Philadelphia, 1987.
[20] Ingrid Daubechies. Ten Lectures on Wavelets, ISBN 978–0898712742. CBMS Series 61, SIAM, Philadelphia 1992.
[21] Eugenio Hernández and Guido L. Weiss. A First Course on Wavelets, ISBN 0–8493–8274–2. CRC Press, Boca Raton, FL, 1996.
[22] Stephane Mallat. A wavelet tour of signal processing, second edition, ISBN 978– 0124666061. Academic Press, New York, 1999.
[23] David F. Walnut. An Introduction to Wavelets Analysis, ISBN 978–0–8176– 3962–4. Birkhäuser, Boston, 2002.
[24] A. Cohen, I. Daubechies and J. C. Feauveau. Biorthogonal basis of compactly supported wavelets. Communications on Pure and Applied Mathematics, ISSN 0010–3640, 45(5), 485–560 (1992).
[25] K. P. Indiradevi, E. Elias, P. S. Sathidevi, N. S. Dinesh and K. Radhakrishnan. A multi–level wavelet approach for automatic detection of epileptic spikes in the electroencephalogram. Computers in Biology and Medicine, ISSN 0010–4825, 38(7), 805–816 (2008).
[26] A. Jacquin, E. Causevicand E. R. John. Automatic identification of spike–wave events and non–convulsive seizures with a reduced set of electrodes. Conference proceedings: IEEE Engineering in Medicine and Biology Society, ISSN 1557– 170X, 1928–1931 (2007).