Int J Neural Syst - Kernel collaborative representation-based automatic seizure detection in intracranial EEG.

Tópicos

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Resumo

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.

Resumo Limpo

automat seizur detect great signific monitor diagnosi epilepsi studi novel method propos automat seizur detect intracrani electroencephalogram ieeg record base kernel collabor represent kcr first eeg record divid s epoch wavelet decomposit five scale perform detail signal scale select spars code train set use kcr kcr lminim replac lminim spars coeffici comput regular least squar rls kernel function util improv separ seizur nonseizur signal reconstruct residu eeg epoch associ seizur nonseizur train sampl compar eeg epoch categor class minim reconstruct residu last multidecis rule appli obtain final detect decis total h ieeg record patient seizur employ evalu system averag sensit specif fals detect rate h achiev seizur detect system base kcr yield high sensit low fals detect rate longterm eeg

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