Author(s): Yuan Q, Zhou W, Liu Y, Wang J
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Abstract Automatic seizure detection is significant in both diagnosis of epilepsy and relieving the heavy workload of inspecting prolonged EEG. This paper presents a new seizure detection method for multi-channel long-term EEG. The fractal intercept derived from fractal geometry is extracted as a novel nonlinear feature of EEG signals, and the relative fluctuation index is calculated as a linear feature. The feature vector, consisting of the two EEG descriptors, is fed into a single-layer neural network for classification. Extreme learning machine (ELM) algorithm is adopted to train the neural network. Finally, post-processing including smoothing, channel fusion, and collar technique is employed to obtain more accurate and stable results. Both the segment-based and event-based assessments are used for the performance evaluation of this method on the 21-patient Freiburg dataset. The segment-based sensitivity of 91.72\% and specificity of 94.89\% were achieved. For the event-based assessment, this method yielded a sensitivity of 93.85\% with a false detection rate of 0.35/h. Copyright © 2012 Elsevier Inc. All rights reserved.
This article was published in Epilepsy Behav
and referenced in Journal of Computer Science & Systems Biology