In:
VNU Journal of Science: Computer Science and Communication Engineering, Vietnam National University Journal of Science, Vol. 35, No. 2 ( 2019-12-24)
Abstract:
Epilepsy is one of the most common and severe 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.
Type of Medium:
Online Resource
ISSN:
2588-1086
,
2615-9260
DOI:
10.25073/2588-1086/vnucsce.230
Language:
Unknown
Publisher:
Vietnam National University Journal of Science
Publication Date:
2019
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