Long-term epileptic EEG classification via 2D mapping and textural features
作者：
Highlights：
• A novel multivariate textural feature extraction for epileptic EEG analysis.
• EEG channels are mapped into gray-scale to form a texture image.
• Low-cost and compact representation of multi-channel epileptic EEG records.
• We perform an extensive comparison against other state-of-arts dedicated methods.
• Epilepsy detection with high sensitivity rate and low number of false alarms.
摘要
•A novel multivariate textural feature extraction for epileptic EEG analysis.•EEG channels are mapped into gray-scale to form a texture image.•Low-cost and compact representation of multi-channel epileptic EEG records.•We perform an extensive comparison against other state-of-arts dedicated methods.•Epilepsy detection with high sensitivity rate and low number of false alarms.
论文关键词：Electroencephalography,Epileptic seizure classification,Haralick,Textural features,Stochastic gradient descent,CHB-MIT dataset
论文评审过程：Available online 12 May 2015, Version of Record 4 June 2015.
论文官网地址：https://doi.org/10.1016/j.eswa.2015.05.002
