Epileptic Seizure Detection in EEG Signals Using Discriminative Stein Kernel-Based Sparse Representation

The automatic seizure detection in electroencephalogram (EEG) signals is crucial for the monitoring, diagnosis, and treatment of epilepsy. In this study, an intelligent detection framework with the discriminative Stein kernel-based sparse representation (DSK-SR) is constructed to distinguish epilept...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on instrumentation and measurement 2022, Vol.71, p.1-15
Main Authors: Lei, Chang, Zheng, Shuzhen, Zhang, Xuan, Wang, Dixin, Wu, Hongtong, Peng, Hong, Hu, Bin
Format: Article
Language:English
Subjects:
Convulsions & seizures
Covariance matrices
Discriminative Stein kernel (DSK)
Eigenvalues and eigenfunctions
electroencephalogram (EEG)
Electroencephalography
Epilepsy
Euclidean geometry
Euclidean space
Hilbert space
Kernel
Kernels
Manifolds
Manifolds (mathematics)
Mathematical analysis
Matrices (mathematics)
Representations
Riemann manifold
seizure detection
Seizures
Sparse matrices
sparse representation (SR)
symmetric positive definite (SPD) matrix
Task analysis
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The automatic seizure detection in electroencephalogram (EEG) signals is crucial for the monitoring, diagnosis, and treatment of epilepsy. In this study, an intelligent detection framework with the discriminative Stein kernel-based sparse representation (DSK-SR) is constructed to distinguish epileptic EEG signals. Specifically, in the scheme of DSK-SR, EEG samples are presented by symmetric positive definite (SPD) matrices in the form of covariance descriptors (CovDs). Taking into account the non-Euclidean geometry of the Riemannian manifold of SPD matrices, the traditional SR in Euclidean space cannot be applied in its original form on the manifold. To this end, the DSK defined on the manifold can permit us to embed the manifold into a high-dimensional reproducing kernel Hilbert space (RKHS) to perform SR. Then, test samples are sparsely coded over the training sets, and the classification decision is performed by assessing which class generates the minimal reconstructed residuals. Eventually, competitive experimental results on three widely recognized EEG datasets against the state-of-the-art methods demonstrate the efficacy of the proposed DSK-SR in identifying epileptic EEG signals, indicating its powerful application potential in the automatic seizure detection.
ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2021.3137159