Classification of epileptic EEG signals based on simple random sampling and sequential feature selection
| Autor: | Yan Li, Shahab Abdulla, Mohammed Diykh, Hadi Ratham Al Ghayab, Wan Xiangkui |
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| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
Computer science
Cognitive Neuroscience Speech recognition Simple random sampling Feature selection 02 engineering and technology Electroencephalography Article 03 medical and health sciences 0302 clinical medicine 0202 electrical engineering electronic engineering information engineering medicine Epileptic eeg Time domain Least square support vector machine medicine.diagnostic_test business.industry Pattern recognition Simple random sample Epileptic seizures Computer Science Applications Support vector machine Electroencephalogram Neurology Sequential feature selection 020201 artificial intelligence & image processing Artificial intelligence business Classifier (UML) 030217 neurology & neurosurgery Curse of dimensionality |
| Zdroj: | Brain Informatics |
| ISSN: | 2198-4026 2198-4018 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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