Classification of epileptic seizures using wavelet packet log energy and norm entropies with recurrent Elman neural network classifier
- Title
- Classification of epileptic seizures using wavelet packet log energy and norm entropies with recurrent Elman neural network classifier
- Creator
- Raghu S.; Sriraam N.; Kumar G.P.
- Description
- 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 50Hz from raw EEG recordings. Raw EEGs were segmented into 1s 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. 2016, Springer Science+Business Media Dordrecht.
- Source
- Cognitive Neurodynamics, Vol-11, No. 1, pp. 51-66.
- Date
- 2017-01-01
- Publisher
- Springer Science and Business Media B.V.
- Subject
- Electroencephalogram; Entropy; Log energy entropy; Norm entropy; Recurrent Elman neural network; Wavelet packets
- Coverage
- Raghu S., Center for Medical Electronics and Computing, M. S. Ramaiah Institute of Technology (An Autonomous Institution Affiliated to VTU Belgaum), Bangalore, India; Sriraam N., Center for Medical Electronics and Computing, M. S. Ramaiah Institute of Technology (An Autonomous Institution Affiliated to VTU Belgaum), Bangalore, India; Kumar G.P., Department of ECE, Christ University, Bangalore, India
- Rights
- All Open Access; Green Open Access
- Relation
- ISSN: 18714080
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
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,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 23, 2025, https://archives.christuniversity.in/items/show/17123.