TY  - INPR
ID  - SisLab3696
UR  - https://eprints.uet.vnu.edu.vn/eprints/id/eprint/3696/
A1  - Nguyen, Thi Anh Dao
A1  - Le, Trung Thanh
A1  - Nguyen, Viet Dung
A1  - Nguyen, Linh Trung
A1  - Le, Vu Ha
Y1  - 2019/12/01/
N2  - 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.
PB  - VNU
JF  - VNU Journal of Science: Computer Science and Communication Engineering
SN  - 2588-1086
TI  - New feature selection method for multi-channel EEG epileptic spike detection system
AV  - none
ER  -