EEG signal processing for epilepsy seizure detection using 5-level Db4 discrete wavelet transform, GA-based feature selection and ANN/SVM classifiers
Author:
Publisher
Springer Science and Business Media LLC
Subject
General Computer Science
Link
https://link.springer.com/content/pdf/10.1007/s12652-020-02837-8.pdf
Reference50 articles.
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2. Akbarian B, Erfanian A (2018) Automatic seizure detection based on nonlinear dynamical analysis of EEG signals and mutual information. Basic Clin Neurosci 9:227–240
3. Akter MS, Islam MR, Iimura Y et al (2020) Multiband entropy-based feature-extraction method for automatic identification of epileptic focus based on high-frequency components in interictal iEEG. Sci Rep 10:7044. https://doi.org/10.1038/s41598-020-62967-z
4. Al-Qerem A, Kharbat F, Nashwan S, Ashraf S, Blaou K (2020) General model for best feature extraction of EEG using discrete wavelet transform wavelet family and differential evolution. Int J Distrib Sens Netw. https://doi.org/10.1177/1550147720911009
5. Amin HU, Zuki YM, Fayyaz AR (2020) A novel approach based on wavelet analysis and arithmetic coding for automated detection and diagnosis of epileptic seizure in EEG signals using machine learning techniques. Biomed Signal Process Control 56:101707
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