Journal Article10.1142/S0129065715500033
Kernel collaborative representation-based automatic seizure detection in intracranial EEG.
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TL;DR: The seizure detection system based on KCR yields both a high sensitivity and a low false detection rate for long-term EEG, which is of great significance in the monitoring and diagnosis of epilepsy.
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Abstract: Automatic seizure detection is of great significance in the monitoring and diagnosis of epilepsy. In this study, a novel method is proposed for automatic seizure detection in intracranial electroencephalogram (iEEG) recordings based on kernel collaborative representation (KCR). Firstly, the EEG recordings are divided into 4s epochs, and then wavelet decomposition with five scales is performed. After that, detail signals at scales 3, 4 and 5 are selected to be sparsely coded over the training sets using KCR. In KCR, l2-minimization replaces l1-minimization and the sparse coefficients are computed with regularized least square (RLS), and a kernel function is utilized to improve the separability between seizure and nonseizure signals. The reconstructed residuals of each EEG epoch associated with seizure and nonseizure training samples are compared and EEG epochs are categorized as the class that minimizes the reconstructed residual. At last, a multi-decision rule is applied to obtain the final detection decision. In total, 595 h of iEEG recordings from 21 patients with 87 seizures are employed to evaluate the system. The average sensitivity of 94.41%, specificity of 96.97%, and false detection rate of 0.26/h are achieved. The seizure detection system based on KCR yields both a high sensitivity and a low false detection rate for long-term EEG.
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Citations
Multifractal Analysis and Relevance Vector Machine-Based Automatic Seizure Detection in Intracranial EEG
TL;DR: The aim of this work is to develop a seizure detection system with high accuracy that is mainly based on multifractal analysis, which describes the local singular behavior of fractal objects and characterizes the multifractional structure using a continuous spectrum.
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Epileptic Seizure Prediction Using Diffusion Distance and Bayesian Linear Discriminate Analysis on Intracranial EEG.
TL;DR: The proposed algorithm for seizure prediction using a novel feature - diffusion distance (DD) in intracranial Electroencephalograph (iEEG) recordings yields a high sensitivity as well as a low false prediction rate, which demonstrates its potential for real-time prediction of seizures.
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Epileptic Seizure Detection Based on Stockwell Transform and Bidirectional Long Short-Term Memory
Minxing Geng,Weidong Zhou,Guoyang Liu,Chaosong Li,Yanli Zhang +4 more
- 13 Jan 2020
TL;DR: An efficient automatic seizure detection method based on Stockwell transform (S-transform) and bidirectional long short-term memory (BiLSTM) neural networks for intracranial EEG recordings and satisfactory results indicate that this seizure detection approach possess promising potential for clinical practice.
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Epileptic Seizure Detection with EEG Textural Features and Imbalanced Classification Based on EasyEnsemble Learning.
TL;DR: Comparisons with 14 published methods demonstrated the improved detection performance for imbalanced EEG signals and the generalizability of the proposed model.
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EEG-Based Seizure detection using linear graph convolution network with focal loss.
Yanna Zhao,Changxu Dong,Gaobo Zhang,Yaru Wang,Xin Chen,Weikuan Jia,Qi Yuan,Fangzhou Xu,Yuanjie Zheng +8 more
TL;DR: In this article, a novel seizure detection model based on linear graph convolution network (LGCN) was proposed to enhance the feature embedding of raw EEG signals during seizure and non-seizure periods.
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