scholarly journals Automated Epileptic Seizure Detection Methods: A Review Study

10.5772/31597 ◽  
2012 ◽  
Author(s):  
Alexandros T. ◽  
Markos G. ◽  
Dimitrios G. ◽  
Evaggelos C. ◽  
Loukas Astrakas ◽  
...  
Keyword(s):  
Epileptic Seizure ◽  
Seizure Detection ◽  
Detection Methods ◽  
Epileptic Seizure Detection ◽  
Review Study

Epileptic Seizure Detection with Log-Euclidean Gaussian Kernel-Based Sparse Representation

2016 ◽  
Vol 26 (03) ◽  
pp. 1650011 ◽  
Author(s):  
Shasha Yuan ◽  
Weidong Zhou ◽  
Qi Wu ◽  
Yanli Zhang
Keyword(s):  
Riemannian Manifold ◽  
Sparse Representation ◽  
Epileptic Seizure ◽  
Reproducing Kernel ◽  
Reproducing Kernel Hilbert Space ◽  
Seizure Detection ◽  
Detection Methods ◽  
Gaussian Kernel ◽  
Epileptic Seizure Detection ◽  
Eeg Recordings

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.

scholarly journals A review of epileptic seizure detection using machine learning classifiers

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Mohammad Khubeb Siddiqui ◽  
Ruben Morales-Menendez ◽  
Xiaodi Huang ◽  
Nasir Hussain
Keyword(s):  
Machine Learning ◽  
Epileptic Seizure ◽  
Seizure Detection ◽  
Epileptic Seizure Detection ◽  
Machine Learning Classifiers ◽  
Learning Classifiers

scholarly journals Epileptic Seizure Detection With a Reduced Montage: A Way Forward for Ambulatory EEG Devices

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 65880-65890 ◽  
Author(s):  
Raheel Asif ◽  
Sajid Saleem ◽  
Syed Ali Hassan ◽  
Soltan Abed Alharbi ◽  
Awais Mehmood Kamboh
Keyword(s):  
Epileptic Seizure ◽  
Seizure Detection ◽  
Epileptic Seizure Detection
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