title: Epileptic Seizure Detection with Log-Euclidean Gaussian Kernel-Based Sparse Representation.
abstract: Epileptic seizure detection plays an important role in the diagnosis of epilepsy and reducing the massive workload of reviewing electroencephalography (EEG) recordings. In this work, a novel algorithm is developed to detect seizures employing log-Euclidean Gaussian kernel-based sparse representation (SR) in long-term EEG recordings. Unlike the traditional SR for vector data in Euclidean space, the log-Euclidean Gaussian kernel-based SR framework is proposed for seizure detection in the space of the symmetric positive definite (SPD) matrices, which form a Riemannian manifold. Since the Riemannian manifold is nonlinear, the log-Euclidean Gaussian kernel function is applied to embed it into a reproducing kernel Hilbert space (RKHS) for performing SR. The EEG signals of all channels are divided into epochs and the SPD matrices representing EEG epochs are generated by covariance descriptors. Then, the testing samples are sparsely coded over the dictionary composed by training samples utilizing log-Euclidean Gaussian kernel-based SR. The classification of testing samples is achieved by computing the minimal reconstructed residuals. The proposed method is evaluated on the Freiburg EEG dataset of 21 patients and shows its notable performance on both epoch-based and event-based assessments. Moreover, this method handles multiple channels of EEG recordings synchronously which is more speedy and efficient than traditional seizure detection methods.
authors.affiliation: 1 School of Information Science and Engineering, Shandong University, Jinan 250100, P. R. China.|2 Suzhou Institute of Shandong University, Suzhou 215123, P. R. China.
authors.forename: Shasha
authors.initials: S
authors.lastname: Yuan
authors.affiliation: 1 School of Information Science and Engineering, Shandong University, Jinan 250100, P. R. China.|2 Suzhou Institute of Shandong University, Suzhou 215123, P. R. China.
authors.forename: Weidong
authors.initials: W
authors.lastname: Zhou
authors.affiliation: 1 School of Information Science and Engineering, Shandong University, Jinan 250100, P. R. China.|2 Suzhou Institute of Shandong University, Suzhou 215123, P. R. China.
authors.forename: Qi
authors.initials: Q
authors.lastname: Wu
authors.affiliation: 1 School of Information Science and Engineering, Shandong University, Jinan 250100, P. R. China.|2 Suzhou Institute of Shandong University, Suzhou 215123, P. R. China.
authors.forename: Yanli
authors.initials: Y
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D001921:Brain / Q000503:physiopathology* / Q000601:surgery; D066264:Datasets as Topic; D000069280:Electrocorticography / Q000379:methods*; D004827:Epilepsy / Q000175:diagnosis* / Q000503:physiopathology* / Q000601:surgery; D005260:Female; D006801:Humans; D008297:Male; D017711:Nonlinear Dynamics; D012680:Sensitivity and Specificity
keywords: EEG; log-Euclidean Gaussian kernel; seizure detection; sparse representation; symmetric positive definite matrix