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Classification of epileptic seizures using wavelet packet log energy and norm entropies with recurrent Elman neural network classifier

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Abstract

Electroencephalogram shortly termed as EEG is considered as the fundamental segment for the assessment of the neural activities in the brain. In cognitive neuroscience domain, EEG-based assessment method is found to be superior due to its non-invasive ability to detect deep brain structure while exhibiting superior spatial resolutions. Especially for studying the neurodynamic behavior of epileptic seizures, EEG recordings reflect the neuronal activity of the brain and thus provide required clinical diagnostic information for the neurologist. This specific proposed study makes use of wavelet packet based log and norm entropies with a recurrent Elman neural network (REN) for the automated detection of epileptic seizures. Three conditions, normal, pre-ictal and epileptic EEG recordings were considered for the proposed study. An adaptive Weiner filter was initially applied to remove the power line noise of 50 Hz from raw EEG recordings. Raw EEGs were segmented into 1 s patterns to ensure stationarity of the signal. Then wavelet packet using Haar wavelet with a five level decomposition was introduced and two entropies, log and norm were estimated and were applied to REN classifier to perform binary classification. The non-linear Wilcoxon statistical test was applied to observe the variation in the features under these conditions. The effect of log energy entropy (without wavelets) was also studied. It was found from the simulation results that the wavelet packet log entropy with REN classifier yielded a classification accuracy of 99.70 % for normal-pre-ictal, 99.70 % for normal-epileptic and 99.85 % for pre-ictal-epileptic.

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Acknowledgments

The authors would like to acknowledge Dr. R.G. Andrzejak of University of Bonn, Germany, for providing permission to use the EEG data available in the public domain. The authors would like to thank Dr. A.S. Hegde, Centre for Neuro Science, M.S. Ramaiah Memorial Hospital, Bangalore, India for the useful discussion. The authors would also like to thank the anonymous reviewers for their helpful comments and suggestions that greatly improved the quality and clarity of the manuscript.

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Authors and Affiliations

  1. Center for Medical Electronics and Computing, M. S. Ramaiah Institute of Technology (An Autonomous Institution Affiliated to VTU Belgaum), Bangalore, India

    S. Raghu & N. Sriraam

  2. Department of ECE, Christ University, Bangalore, India

    G. Pradeep Kumar

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  1. S. Raghu
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Correspondence to N. Sriraam.

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Raghu, S., Sriraam, N. & Kumar, G.P. Classification of epileptic seizures using wavelet packet log energy and norm entropies with recurrent Elman neural network classifier. Cogn Neurodyn 11, 51–66 (2017). https://doi.org/10.1007/s11571-016-9408-y

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  • DOI: https://doi.org/10.1007/s11571-016-9408-y

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