Nonlinear Classification of EEG Data for Seizure Detection

Abstract

We address the problem of classification of EEG recordings for the detection of epileptic seizures. We assume that the EEG measurements can be described by a low dimensional manifold. The geometry of the manifold is typically nonlinear and can be recovered with the Laplacian eigenmaps method. Our experiments demonstrate that the manifold can reveal the intrinsic structure of the data and that baseline and ictal states are well separated. We use a kernel ridge regression to identify the boundary between ictal and baseline states. We have performed a quantitative evaluation of our new approach using an acute rat model of epilepsy. Our experiments show that our approach outperforms PCA combined with a kernel ridge classifier.

Keywords

Artificial intelligencePattern recognition (psychology)ElectroencephalographyNonlinear dimensionality reductionIctalManifold (fluid mechanics)Nonlinear systemComputer scienceKernel (algebra)Classifier (UML)Laplace operatorEpileptic seizureMathematicsDimensionality reductionPhysicsPsychologyNeuroscienceMathematical analysisEngineering

Affiliated Institutions

Related Publications

Multiway analysis of epilepsy tensors

Evrim Acar , Canan Aykut Bingöl , Haluk O. Bingol +2 more

Abstract Motivation: The success or failure of an epilepsy surgery depends greatly on the localization of epileptic focus (origin of a seizure). We address the problem of identi...

2007 Bioinformatics 278 citations

Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering

Mikhail Belkin , Partha Niyogi

Drawing on the correspondence between the graph Laplacian, the Laplace-Beltrami operator on a manifold, and the connections to the heat equation, we propose a geometrically moti...

2002 The MIT Press eBooks 4494 citations

Laplacian Eigenmaps for Dimensionality Reduction and Data Representation

Mikhail Belkin , Partha Niyogi

One of the central problems in machine learning and pattern recognition is to develop appropriate representations for complex data. We consider the problem of constructing a rep...

2003 Neural Computation 7514 citations

A Proposed Diagnostic Scheme for People with Epileptic Seizures and with Epilepsy: Report of the ILAE Task Force on Classification and Terminology

Jerome Engel

The International League Against Epilepsy (ILAE) made a major contribution when it established standardized classifications and terminology for epileptic seizures and syndromes....

2001 Epilepsia 2235 citations

Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment

Zhenyue Zhang , Hongyuan Zha

We present a new algorithm for manifold learning and nonlinear dimensionality reduction. Based on a set of unorganized data points sampled with noise from a parameterized manifo...

2004 SIAM Journal on Scientific Computing 1466 citations

Publication Info

Year: 2006
Type: article
Volume: 92
Pages: 956-959
Citations: 6
Access: Closed

External Links

View on DOI.org

Social Impact

Altmetric

Nonlinear Classification of EEG Data for Seizure Detection

PlumX Metrics

Social media, news, blog, policy document mentions

Citation Metrics

OpenAlex

Cite This

APA Style

                            
                                    Robinson Ramírez‐Vélez, 
                                
                                    Richard J. Staba, 
                                
                                    Daniel S. Barth
                                
                                et al.
                            
                            (2006). 
                            Nonlinear Classification of EEG Data for Seizure Detection. 
                            
                            , 92
                            
                            , 956-959.
                            https://doi.org/10.1109/isbi.2006.1625078

Identifiers

DOI: 10.1109/isbi.2006.1625078