relation: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/3696/
title: New feature selection method for multi-channel EEG epileptic spike detection system
creator: Nguyen, Thi Anh Dao
creator: Le, Trung Thanh
creator: Nguyen, Viet Dung
creator: Nguyen, Linh Trung
creator: Le, Vu Ha
subject: Electronics and Communications
subject: Information Technology (IT)
description: 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.
publisher: VNU
date: 2019-12-01
type: Article
type: PeerReviewed
identifier:   Nguyen, Thi Anh Dao and Le, Trung Thanh and Nguyen, Viet Dung and Nguyen, Linh Trung and Le, Vu Ha  (2019) New feature selection method for multi-channel EEG epileptic spike detection system.  VNU Journal of Science: Computer Science and Communication Engineering .    ISSN 2588-1086    (In Press)