Epilepsy attacks recognition based on 1D octal pattern, wavelet transform and EEG signals
Authors:
- Türker Tuncer,
- Sengul Dogan,
- Ganesh R. Naik,
- Paweł Pławiak
Abstract
Electroencephalogram (EEG) signals have been generally utilized for diagnostic systems. Nowadays artificial intelligence-based systems have been proposed to classify EEG signals to ease diagnosis process. However, machine learning models have generally been used deep learning based classification model to reach high classification accuracies. This work focuses classification epilepsy attacks using EEG signals with a lightweight and simple classification model. Hence, an automated EEG classification model is presented. The used phases of the presented automated EEG classification model are (i) multileveled feature generation using one-dimensional (1D) octal-pattern (OP) and discrete wavelet transform (DWT). Here, main feature generation function is the presented octal-pattern. DWT is employed for level creation. By employing DWT frequency coefficients of the EEG signal is obtained and octal-pattern generates texture features from raw EEG signal and wavelet coefficients. This DWT and octal-pattern based feature generator extracts 128 × 8 = 1024 (Octal-pattern generates 128 features from a signal, 8 signal are used in the feature generation 1 raw EEG and 7 wavelet low-pass filter coefficients). (ii) To select the most useful features, neighborhood component analysis (NCA) is deployed and 128 features are selected. (iii) The selected features are feed to k nearest neighborhood classifier. To test this model, an epilepsy seizure dataset is used and 96.0% accuracy is attained for five categories. The results clearly denoted the success of the presented octal-pattern based epilepsy classification model.
- Record ID
- CUT39ba26b5b4f941c8a039b03dc031fb24
- Publication categories
- ;
- Author
- Journal series
- Multimedia Tools and Applications, ISSN 1380-7501, e-ISSN 1573-7721
- Issue year
- 2021
- Vol
- 80
- No
- 16
- Pages
- 25197-25218
- Other elements of collation
- rys.; tab.; wykr.; Bibliografia (na s.) - 25215-25218; Bibliografia (liczba pozycji) - 74; Oznaczenie streszczenia - Abstr.; Data udostępnienia on-line - 2021-04-14; Numeracja w czasopiśmie - Vol. 80, Iss. 16
- Keywords in English
- discrete wavelet transform, 1D octal pattern, electroencephalogram signals, classification, epilepsy
- DOI
- DOI:10.1007/s11042-021-10882-4 Opening in a new tab
- URL
- https://link.springer.com/article/10.1007%2Fs11042-021-10882-4 Opening in a new tab
- Language
- eng (en) English
- License
- Score (nominal)
- 70
- Publication indicators
- Citation count
- 28
- Additional fields
- Indeksowana w: Web of Science, Scopus
- Uniform Resource Identifier
- https://cris.pk.edu.pl/info/article/CUT39ba26b5b4f941c8a039b03dc031fb24/
- URN
urn:pkr-prod:CUT39ba26b5b4f941c8a039b03dc031fb24
* presented citation count is obtained through Internet information analysis, and it is close to the number calculated by the Publish or PerishOpening in a new tab system.