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dc.contributor.authorNguyen, Thi Anh Dao-
dc.contributor.authorLe, Trung Thanh-
dc.date.accessioned2020-02-18T06:09:56Z-
dc.date.available2020-02-18T06:09:56Z-
dc.date.issued2019-
dc.identifier.citationNguyen, T. A. D., et al. (2019). New feature selection method for multi-channel EEG epileptic spike detection system. VNU Journal of Science: Comp. Science & Com. Eng., Vol. 35, No. 2 (2019) 47–59.vi
dc.identifier.issn2588-1094-
dc.identifier.urihttp://repository.vnu.edu.vn/handle/VNU_123/70684-
dc.description.abstractEpilepsy is one of the most common brain disorders. Electroencephalogram (EEG) is widely used in epilepsy diagnosis and treatment, with it the epileptic spikes can be observed. Tensor decomposition-based feature extraction has been proposed to facilitate automatic detection of EEG epileptic spikes. However, tensor decomposition may still result in a large number of features which are considered negligible in determining expected output performance. We proposed a new feature selection method that combines the Fisher score and p-value feature selection methods to rank the features by using the longest common sequences (LCS) to separate epileptic and non-epileptic spikes. The proposed method significantly outperformed several state-of-the-art feature selection methods.vi
dc.language.isoenvi
dc.publisherH. : ĐHQGHNvi
dc.relation.ispartofseriesComputer Science and Communication Engineering;-
dc.subjectElectroencephalogramvi
dc.subjectEEGvi
dc.subjectEpileptic spikesvi
dc.subjectTensor decompositionvi
dc.subjectFeature extractionvi
dc.subjectFeature selectionvi
dc.titleNew feature selection method for multi-channel EEG epileptic spike detection systemvi
dc.typeArticlevi
dc.identifier.lichttps://doi.org/10.25073/2588-1094/vnuees.230-
Appears in Collections:Computer Science and Communication Engineering


  • New feature selection method for multi-channel EEG epilep...
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  • Full metadata record
    DC FieldValueLanguage
    dc.contributor.authorNguyen, Thi Anh Dao-
    dc.contributor.authorLe, Trung Thanh-
    dc.date.accessioned2020-02-18T06:09:56Z-
    dc.date.available2020-02-18T06:09:56Z-
    dc.date.issued2019-
    dc.identifier.citationNguyen, T. A. D., et al. (2019). New feature selection method for multi-channel EEG epileptic spike detection system. VNU Journal of Science: Comp. Science & Com. Eng., Vol. 35, No. 2 (2019) 47–59.vi
    dc.identifier.issn2588-1094-
    dc.identifier.urihttp://repository.vnu.edu.vn/handle/VNU_123/70684-
    dc.description.abstractEpilepsy is one of the most common brain disorders. Electroencephalogram (EEG) is widely used in epilepsy diagnosis and treatment, with it the epileptic spikes can be observed. Tensor decomposition-based feature extraction has been proposed to facilitate automatic detection of EEG epileptic spikes. However, tensor decomposition may still result in a large number of features which are considered negligible in determining expected output performance. We proposed a new feature selection method that combines the Fisher score and p-value feature selection methods to rank the features by using the longest common sequences (LCS) to separate epileptic and non-epileptic spikes. The proposed method significantly outperformed several state-of-the-art feature selection methods.vi
    dc.language.isoenvi
    dc.publisherH. : ĐHQGHNvi
    dc.relation.ispartofseriesComputer Science and Communication Engineering;-
    dc.subjectElectroencephalogramvi
    dc.subjectEEGvi
    dc.subjectEpileptic spikesvi
    dc.subjectTensor decompositionvi
    dc.subjectFeature extractionvi
    dc.subjectFeature selectionvi
    dc.titleNew feature selection method for multi-channel EEG epileptic spike detection systemvi
    dc.typeArticlevi
    dc.identifier.lichttps://doi.org/10.25073/2588-1094/vnuees.230-
    Appears in Collections:Computer Science and Communication Engineering


  • New feature selection method for multi-channel EEG epilep...
    • Size : 2,33 MB

    • Format : Adobe PDF

    • View : 
    • Download :