ArticleCognitive neurodynamics2017
Classification of epileptic seizures using wavelet packet log energy and norm entropies with recurrent Elman neural network classifier.
Article in Cognitive neurodynamics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed.
- Effectual seizure detection using MBBF-GPSO with CNN network.Cognitive neurodynamics · 2024Article
- A Review of Recurrent Neural Network-Based Methods in Computational Physiology.IEEE transactions on neural networks and learning systems · 2023Review
- An automated detection of epileptic seizures EEG using CNN classifier based on feature fusion with high accuracy.BMC medical informatics and decision making · 2023Article
- Sparse measures with swarm-based pliable hidden Markov model and deep learning for EEG classification.Frontiers in computational neuroscience · 2022Article
- Complex networks and deep learning for EEG signal analysis.Cognitive neurodynamics · 2021Review
- Novel channel selection method based on position priori weighted permutation entropy and binary gravity search algorithm.Cognitive neurodynamics · 2021Article
- The successful discrimination of depression from EEG could be attributed to proper feature extraction and not to a particular classification method.Cognitive neurodynamics · 2020Article
- Frontal EEG Changes with the Recovery of Carotid Blood Flow in a Cardiac Arrest Swine Model.Sensors (Basel, Switzerland) · 2020Article
- Complexity analysis and dynamic characteristics of EEG using MODWT based entropies for identification of seizure onset.Journal of biomedical research · 2019Article
- Prediction of epilepsy seizure from multi-channel electroencephalogram by effective connectivity analysis using Granger causality and directed transfer function methods.Cognitive neurodynamics · 2019Article
- Transition of brain networks from an interictal to a preictal state preceding a seizure revealed by scalp EEG network analysis.Cognitive neurodynamics · 2019Article
- EEG classification of driver mental states by deep learning.Cognitive neurodynamics · 2018Article
- Automated epileptic seizures detection using multi-features and multilayer perceptron neural network.Brain informatics · 2018Article
- Extreme learning machines for regression based on V-matrix method.Cognitive neurodynamics · 2017Article
- Classification of Focal and Non Focal Epileptic Seizures Using Multi-Features and SVM Classifier.Journal of medical systems · 2017Article
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3 authors.
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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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