Classification of focal and non-focal EEG signals using neighborhood component analysis and machine learning algorithms
作者：
Highlights：
• CADFES tool is designed for automated classification of focal seizures
• Significant features were selected using neighbourhood component analysis
• Regularization parameter was optimized to ensure less classification loss
• Classification accuracy of 96.1% was attained using support vector machine classifier
• Results were better than existing approaches.
摘要
•CADFES tool is designed for automated classification of focal seizures•Significant features were selected using neighbourhood component analysis•Regularization parameter was optimized to ensure less classification loss•Classification accuracy of 96.1% was attained using support vector machine classifier•Results were better than existing approaches.
论文关键词：Automated detection,Epileptic seizures,Feature selection,Focal EEG,Neighborhood component analysis
论文评审过程：Received 9 November 2017, Revised 20 May 2018, Accepted 13 June 2018, Available online 28 June 2018, Version of Record 5 July 2018.
论文官网地址：https://doi.org/10.1016/j.eswa.2018.06.031
