Performance evaluation for epileptic electroencephalogram (EEG) detection by using Neyman–Pearson criteria and a support vector machine

Chun-mei Wang, Chong-ming Zhang, Jun-zhong Zou and Jian Zhang

Physica A: Statistical Mechanics and its Applications, 2012, vol. 391, issue 4, 1602-1609

Abstract: The diagnosis of several neurological disorders is based on the detection of typical pathological patterns in electroencephalograms (EEGs). This is a time-consuming task requiring significant training and experience. A lot of effort has been devoted to developing automatic detection techniques which might help not only in accelerating this process but also in avoiding the disagreement among readers of the same record. In this work, Neyman–Pearson criteria and a support vector machine (SVM) are applied for detecting an epileptic EEG. Decision making is performed in two stages: feature extraction by computing the wavelet coefficients and the approximate entropy (ApEn) and detection by using Neyman–Pearson criteria and an SVM. Then the detection performance of the proposed method is evaluated. Simulation results demonstrate that the wavelet coefficients and the ApEn are features that represent the EEG signals well. By comparison with Neyman–Pearson criteria, an SVM applied on these features achieved higher detection accuracies.

Keywords: EEG; Epileptic EEG; Discrete wavelet transform; Approximate entropy; Support vector machine (SVM); Neyman–Pearson criteria (search for similar items in EconPapers)
Date: 2012
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:391:y:2012:i:4:p:1602-1609

DOI: 10.1016/j.physa.2011.09.010

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