Convex optimisation-based methods for K-complex detection
作者：
Highlights：
• We develop three convex optimisation-based models for automatic detection of K-complexes.
• They extract key features of an EEG signal (a biological application).
• They significantly reduce the dimension of the problem and the computational time.
• They enhance the classification accuracy of an EEG signal in presence of K-complex.
• K-complexes are successfully detected in an EEG background.
摘要
•We develop three convex optimisation-based models for automatic detection of K-complexes.•They extract key features of an EEG signal (a biological application).•They significantly reduce the dimension of the problem and the computational time.•They enhance the classification accuracy of an EEG signal in presence of K-complex.•K-complexes are successfully detected in an EEG background.
论文关键词：Convex optimisation,Signal approximation,Data analysis,Feature extraction,Biological signal classification
论文评审过程：Received 17 December 2014, Revised 20 May 2015, Accepted 5 July 2015, Available online 23 July 2015, Version of Record 23 July 2015.
论文官网地址：https://doi.org/10.1016/j.amc.2015.07.005
