The classification of EEG signals using discretization-based entropy and the adaptive neuro-fuzzy inference system
| Autor: | Hekim, Mahmut |
|---|---|
| Přispěvatelé: | Gaziosmanpaşa Üniversitesi, Hekim, Mahmut |
| Rok vydání: | 2016 |
| Předmět: |
ComputingMethodologies_PATTERNRECOGNITION
Quantitative Biology::Neurons and Cognition EEG signals discrete wavelet transform (DWT) discretization-based entropy adaptive neuro-fuzzy inference system (ANFIS) General Computer Science Physics::Medical Physics Mühendislik Elektrik ve Elektronik Data_CODINGANDINFORMATIONTHEORY Electrical and Electronic Engineering |
| Zdroj: | Volume: 24, Issue: 1 285-297 Turkish Journal of Electrical Engineering and Computer Science |
| ISSN: | 1303-6203 1300-0632 |
| DOI: | 10.3906/elk-1306-164 |
| Popis: | A novel feature extraction called discretization-based entropy is proposed for use in the classification of EEG signals. To this end, EEG signals are decomposed into frequency subbands using the discrete wavelet transform (DWT), the coefficients of these subbands are discretized into the desired number of intervals using the discretization method, the entropy values of the discretized subbands are calculated using the Shannon entropy method, and these are then used as the inputs of the adaptive neuro-fuzzy inference system (ANFIS). The equal width discretization (EWD) and equal frequency discretization (EFD) methods are used for the discretization. In order to evaluate their performances in terms of classification accuracy, three different experiments are implemented using different combinations of healthy segments, epileptic seizure-free segments, and epileptic seizure segments. The experiments show that the EWD-based entropy approach achieves higher classification accuracy rates than the EFD-based entropy approach. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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