Detección de puntas epilépticas en señales electroencefalográficas para pacientes con epilepsia del lóbulo temporal utilizando wavelets
Main Article Content
Keywords
Análisis multirresolución, wavelet biortogonal, detección de puntas epilépticas.
Resumen
En este trabajo se describe un método para la detección de puntas epilépticasen un registro electroencefalográfico(EEG) de superficie tomando un solocanal. Se identificó un patrón al utilizar el análisis multirresolución con unawavelet biortogonal después de procesar y analizar con el Toolbox Waveletde Matlab, 207 registros de puntas y 132 registros de artificios previamenteclasificadas por el neurofisiólogo. Este patrón permitió diseñar un algoritmopara la detección de puntas en pacientes con epilepsia refractaria del lóbulotemporal, a partir de los máximos voltajes en cada uno de los seis niveles de reconstrucción usando la wavelet biortogonal 3.7. El algoritmo se aplicó sobreregistros de pacientes con epilepsia, obteniéndose una sensibilidad del 92% yuna especificidad del 80% en el diagnóstico de las puntas epilépticas.
PACS: 87.57.-s
MSC: 65T60, 42C40
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Referencias
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[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).
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[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).