New feature selection method for multi-channel EEG epileptic spike detection system

• Main Authors: Nguyen, Thi Anh Dao, Le, Trung Thanh
• Format: Article
• Language: English
• Published: H. : ĐHQGHN 2020
• Subjects: Electroencephalogram; EEG; Epileptic spikes; Tensor decomposition; Feature extraction; Feature selection
• Online Access: http://repository.vnu.edu.vn/handle/VNU_123/70684; https://doi.org/10.25073/2588-1094/vnuees.230
• Institution: Vietnam National University, Hanoi

Epilepsy 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.