Classification of epileptic EEG signals based on simple random sampling and sequential feature selection
Article
Article Title | Classification of epileptic EEG signals based on simple random sampling and sequential feature selection |
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ERA Journal ID | 211938 |
Article Category | Article |
Authors | Al Ghayab, Hadi (Author), Li, Yan (Author), Abdulla, Shahab (Author), Diykh, Mohammed (Author) and Wan, Xiangkui (Author) |
Journal Title | Brain Informatics |
Journal Citation | 3 (2), pp. 85-91 |
Number of Pages | 7 |
Year | 2016 |
Publisher | Springer |
Place of Publication | Germany |
ISSN | 2198-4018 |
2198-4026 | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/s40708-016-0039-1 |
Web Address (URL) | http://link.springer.com/article/10.1007/s40708-016-0039-1 |
Abstract | Electroencephalogram (EEG) signals are used broadly in the medical fields. The main applications of EEG signals are the diagnosis and treatment of diseases such as epilepsy, Alzheimer, sleep problems and so on. This paper presents a new method which extracts and selects features from multi-channel EEG signals. This research focuses on three main points. Firstly, simple random sampling (SRS) technique is used to extract features from the time domain of EEG signals. Secondly, the sequential feature selection (SFS) algorithm is applied to select the key features and to reduce the dimensionality of the data. Finally, the selected features are forwarded to a least square support vector machine (LS_SVM) classifier to classify the EEG signals. The LS_SVM classifier classified the features which are extracted and selected from the SRS and the SFS. The experimental results show that the method achieves 99.90, 99.80 and 100 % for classification accuracy, sensitivity and specificity, respectively. |
Keywords | electroencephalogram; epileptic seizures; simple random sampling; sequential feature selection; least square support vector machine |
ANZSRC Field of Research 2020 | 469999. Other information and computing sciences not elsewhere classified |
461399. Theory of computation not elsewhere classified | |
Byline Affiliations | Faculty of Health, Engineering and Sciences |
Hubei University of Technology, China | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q3695/classification-of-epileptic-eeg-signals-based-on-simple-random-sampling-and-sequential-feature-selection
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