Implicit wiener series analysis of epileptic seizure recordings
Entity
UAM. Departamento de Ingeniería InformáticaPublisher
Institute of Electrical and Electronics EngineersDate
2009Citation
10.1109/IEMBS.2009.5333080
Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2009. EMBC 2009. IEEE, 2009. 5304 - 5307
ISSN
1557-170XISBN
978-1-4244-3296-7DOI
10.1109/IEMBS.2009.5333080Funded by
Universidad Autónoma de Madrid - Instituto de Ingeniería del Conocimiento . Authors have been partially supported by Spain’s TIN 2007– 66862 and Cátedra UAM–IIC en Modelado y Predicción. The first author is kindly supported by the FPU–MEC grant reference AP2006–02285.Editor's Version
http://dx.doi.org/10.1109/IEMBS.2009.5333080Subjects
Algorithms; Electrodes; Electroencephalography; Epilepsy; InformáticaNote
Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. A. Barbero, M. O. Franz, W. Van Drongelen, J. R. Dorronsoro, B. Schölkopf,and M. Grosse-Wentrup, "Implicit Wiener series analysis of epileptic seizure recordings", in Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2009. EMBC 2009, Minneapolis, MN, 2009, pp. 5304 - 5307Rights
© 2009 IEEEAbstract
Implicit Wiener series are a powerful tool to build Volterra representations of time series with any degree of non-linearity. A natural question is then whether higher order representations yield more useful models. In this work we shall study this question for ECoG data channel relationships in epileptic seizure recordings, considering whether quadratic representations yield more accurate classifiers than linear ones. To do so we first show how to derive statistical information on the Volterra coefficient distribution and how to construct seizure classification patterns over that information. As our results illustrate, a quadratic model seems to provide no advantages over a linear one. Nevertheless, we shall also show that the interpretability of the implicit Wiener series provides insights into the inter-channel relationships of the recordings.
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Google Scholar:Barbero Jiménez, Álvaro
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Franz, Matthias Otto
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Van Drongelen, Wim
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Dorronsoro Ibero, José Ramón
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Schölkopf, Bernhard
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Grosse-Wentrup, Moritz
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