English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
Add to Basket
  Local TagsRelease HistoryDetailsSummary

Released
Conference Paper

Implicit Wiener Series Analysis of Epileptic Seizure Recordings

MPS-Authors
/persons/resource/persons84193

Schölkopf,  B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons83948

Grosse-Wentrup,  M
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

External Resource

https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5333080
(Publisher version)

Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available
Citation

Barbero Jimenez, A., Franz, M., Drongelen, W., Dorronsoro, J., Schölkopf, B., & Grosse-Wentrup, M. (2009). Implicit Wiener Series Analysis of Epileptic Seizure Recordings. In Y. Kim, B. He, & X. Pan (Eds.), 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (pp. 5304-5307). Piscataway, NJ, USA: IEEE Service Center.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-C30B-A
Abstract
Implicit Wiener series are a powerful tool to build Volterra representations of time series with any degree of nonlinearity.
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.