4.2 Article

Epileptic EEG classification based on extreme learning machine and nonlinear features

Journal

EPILEPSY RESEARCH
Volume 96, Issue 1-2, Pages 29-38

Publisher

ELSEVIER
DOI: 10.1016/j.eplepsyres.2011.04.013

Keywords

Epileptic EEG; Approximate entropy (ApEn); Hurst exponent; Detrended fluctuation analysis (DFA); Extreme learning machine (ELM); Support vector machine (SVM)

Funding

  1. National Natural Science Foundation of China [30870666]
  2. National Key Technology R&D Program of China [2008BAI52B03]
  3. Independent Innovation Foundation of Shandong University [2009JC004]
  4. Program of Development of Science and Technology of Shandong [2010GSF10243]

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The automatic detection and classification of epileptic EEG are significant in the evaluation of patients with epilepsy. This paper presents a new EEG classification approach based on the extreme learning machine (ELM) and nonlinear dynamical features. The theory of nonlinear dynamics has been a powerful tool for understanding brain electrical activities. Nonlinear features extracted from EEG signals such as approximate entropy (ApEn), Hurst exponent and scaling exponent obtained with detrended fluctuation analysis (DFA) are employed to characterize interictal and ictal EEGs. The statistics indicate that the differences of those nonlinear features between interictal and ictal EEGs are statistically significant. The ELM algorithm is employed to train a single hidden layer feedforward neural network (SLFN) with EEG nonlinear features. The experiments demonstrate that compared with the backpropagation (BP) algorithm and support vector machine (SVM), the performance of the ELM is better in terms of training time and classification accuracy which achieves a satisfying recognition accuracy of 96.5% for interictal and ictal EEG signals. (C) 2011 Elsevier B.V. All rights reserved.

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