Regularized Universum twin support vector machine for classification of EEG Signal
| Autor: | Deepak Gupta, Kshitij Mishra, Hemanga Jyoti Sarma, Mukesh Prasad |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
medicine.diagnostic_test
business.industry Noise (signal processing) Computer science Feature extraction Feature selection Pattern recognition 02 engineering and technology Electroencephalography Signal Support vector machine Data point 020204 information systems Outlier Principal component analysis 0202 electrical engineering electronic engineering information engineering medicine 020201 artificial intelligence & image processing Artificial intelligence business |
| Zdroj: | SMC |
| Popis: | © 2019 IEEE. Electroencephalogram signal is the signal used for the detection of a neurological disorder as epilepsy disorder, sleep disorder and many more. The types of EEG signal gives the hidden information regarding the distribution of the data that may consist of a large volume of the poor and noisy signal. In order to reduce the outlier effects and noise, incorporation of prior knowledge in the model, universum may help and enhance the better generalization ability of the model. This paper proposes a regularized universum twin support vector machine (RUTWSVM) for classification of the healthy and seizure EEG signals. Here, the selection of the universum data points is obtained in two ways (i). Universum data has been generated from the healthy and seizure EEG signals itself and (ii). Interictal EEG signal has been used as universum data which may help to handle the outlier effects. Further, various feature selection techniques are applied to extract the important noise free features from the EEG signals. We have performed a comparative analysis of proposed RUTWSVM with USVM and UTWSVM to classify the EEG signals as well as benchmark real-world datasets in an optimum way. The experiment results clearly exhibit the applicability and usability of the proposed RUTWSVM with interictal EEG signals as universum data points as well as benchmark real-world datasets. |
| Databáze: | OpenAIRE |
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