ABC optimized RBF network for classification of EEG signal for epileptic seizure identification
The brain signals usually generate certain electrical signals that can be recorded and analyzed for detection in several brain disorder diseases. These small signals are expressly called as Electroencephalogram (EEG) signals. This research work analyzes the epileptic disorder in human brain through...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2017-03-01
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| Series: | Egyptian Informatics Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110866516300159 |
| _version_ | 1818886283235164160 |
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| author | Sandeep Kumar Satapathy Satchidananda Dehuri Alok Kumar Jagadev |
| author_facet | Sandeep Kumar Satapathy Satchidananda Dehuri Alok Kumar Jagadev |
| author_sort | Sandeep Kumar Satapathy |
| collection | DOAJ |
| description | The brain signals usually generate certain electrical signals that can be recorded and analyzed for detection in several brain disorder diseases. These small signals are expressly called as Electroencephalogram (EEG) signals. This research work analyzes the epileptic disorder in human brain through EEG signal analysis by integrating the best attributes of Artificial Bee Colony (ABC) and radial basis function networks (RBFNNs). We have used Discrete Wavelet Transform (DWT) technique for extraction of potential features from the signal. In our study, for classification of these signals, in this paper, the RBFNNs have been trained by a modified version of ABC algorithm. In the modified ABC, the onlooker bees are selected based on binary tournament unlike roulette wheel selection of ABC. Additionally, kernels such as Gaussian, Multi-quadric, and Inverse-multi-quadric are used for measuring the effectiveness of the method in numerous mixtures of healthy segments, seizure-free segments, and seizure segments. Our experimental outcomes confirm that RBFNN with inverse-multi-quadric kernel trained with modified ABC is significantly better than RBFNNs with other kernels trained by ABC and modified ABC. |
| first_indexed | 2024-12-19T16:18:52Z |
| format | Article |
| id | doaj.art-cc5bc4f5e38842ed97fe70957c732ba7 |
| institution | Directory Open Access Journal |
| issn | 1110-8665 |
| language | English |
| last_indexed | 2024-12-19T16:18:52Z |
| publishDate | 2017-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Egyptian Informatics Journal |
| spelling | doaj.art-cc5bc4f5e38842ed97fe70957c732ba72022-12-21T20:14:33ZengElsevierEgyptian Informatics Journal1110-86652017-03-01181556610.1016/j.eij.2016.05.001ABC optimized RBF network for classification of EEG signal for epileptic seizure identificationSandeep Kumar Satapathy0Satchidananda Dehuri1Alok Kumar Jagadev2Department of Computer Science & Engineering, ITER, S’O’A University, Bhubaneswar 751030, Odisha, IndiaDepartment of Information & Communication Technology, Fakir Mohan University, Vyasa Vihar, Januganj, Balasore 756019, IndiaSchool of Computer Engineering, KIIT University, Bhubaneswar 751024, Odisha, IndiaThe brain signals usually generate certain electrical signals that can be recorded and analyzed for detection in several brain disorder diseases. These small signals are expressly called as Electroencephalogram (EEG) signals. This research work analyzes the epileptic disorder in human brain through EEG signal analysis by integrating the best attributes of Artificial Bee Colony (ABC) and radial basis function networks (RBFNNs). We have used Discrete Wavelet Transform (DWT) technique for extraction of potential features from the signal. In our study, for classification of these signals, in this paper, the RBFNNs have been trained by a modified version of ABC algorithm. In the modified ABC, the onlooker bees are selected based on binary tournament unlike roulette wheel selection of ABC. Additionally, kernels such as Gaussian, Multi-quadric, and Inverse-multi-quadric are used for measuring the effectiveness of the method in numerous mixtures of healthy segments, seizure-free segments, and seizure segments. Our experimental outcomes confirm that RBFNN with inverse-multi-quadric kernel trained with modified ABC is significantly better than RBFNNs with other kernels trained by ABC and modified ABC.http://www.sciencedirect.com/science/article/pii/S1110866516300159ElectroencephalographyRadial basis function neural networksArtificial Bee ColonyDiscrete Wavelet Transform |
| spellingShingle | Sandeep Kumar Satapathy Satchidananda Dehuri Alok Kumar Jagadev ABC optimized RBF network for classification of EEG signal for epileptic seizure identification Egyptian Informatics Journal Electroencephalography Radial basis function neural networks Artificial Bee Colony Discrete Wavelet Transform |
| title | ABC optimized RBF network for classification of EEG signal for epileptic seizure identification |
| title_full | ABC optimized RBF network for classification of EEG signal for epileptic seizure identification |
| title_fullStr | ABC optimized RBF network for classification of EEG signal for epileptic seizure identification |
| title_full_unstemmed | ABC optimized RBF network for classification of EEG signal for epileptic seizure identification |
| title_short | ABC optimized RBF network for classification of EEG signal for epileptic seizure identification |
| title_sort | abc optimized rbf network for classification of eeg signal for epileptic seizure identification |
| topic | Electroencephalography Radial basis function neural networks Artificial Bee Colony Discrete Wavelet Transform |
| url | http://www.sciencedirect.com/science/article/pii/S1110866516300159 |
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