Epileptic seizure recognition using EEG wavelet decomposition based on nonlinear and statistical features with support vector machine classification
| Autor: | Fethi Bereksi Reguig, Radhwane Benali, Dib Nabil |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Discrete wavelet transform
Support Vector Machine Entropy Wavelet Analysis Biomedical Engineering 02 engineering and technology Lyapunov exponent Electroencephalography Approximate entropy 03 medical and health sciences symbols.namesake 0302 clinical medicine Seizures Germany 0202 electrical engineering electronic engineering information engineering medicine Humans Sensitivity (control systems) Epilepsy medicine.diagnostic_test business.industry Data Collection Statistical parameter Records Signal Processing Computer-Assisted Pattern recognition Support vector machine symbols 020201 artificial intelligence & image processing Artificial intelligence Epileptic seizure medicine.symptom business Algorithms 030217 neurology & neurosurgery |
| Zdroj: | Biomedical Engineering / Biomedizinische Technik. 65:133-148 |
| ISSN: | 1862-278X 0013-5585 |
| DOI: | 10.1515/bmt-2018-0246 |
| Popis: | Epileptic seizure (ES) is a neurological brain dysfunction. ES can be detected using the electroencephalogram (EEG) signal. However, visual inspection of ES using long-time EEG recordings is a difficult, time-consuming and a costly procedure. Thus, automatic epilepsy recognition is of primary importance. In this paper, a new method is proposed for automatic ES recognition using short-time EEG recordings. The method is based on first decomposing the EEG signals on sub-signals using discrete wavelet transform. Then, from the obtained sub-signals, different non-linear parameters such as approximate entropy (ApEn), largest Lyapunov exponents (LLE) and statistical parameters are determined. These parameters along with phase entropies, calculated through higher order spectrum analysis, are used as an input vector of a multi-class support vector machine (MSVM) for ES recognition. The proposed method is evaluated using the standard EEG database developed by the Department of Epileptology, University of Bonn, Germany. The evaluation is carried out through a large number of classification experiments. Different statistical metrics namely Sensitivity (Se), Specificity (Sp) and classification accuracy (Ac) are calculated and compared to those obtained in the scientific research literature. The obtained results show that the proposed method achieves high accuracies, which are as good as the best existing state-of-the-art methods studied using the same EEG database. |
| Databáze: | OpenAIRE |
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