Epileptic seizure classification using level-crossing EEG sampling and ensemble of sub-problems classifier

作者：

Highlights：

• Effectively combines LCADCs, MASA, and adaptive-rate FIR filter for signal denoising.

• Reduces data size using adaptive-rate techniques, features extraction, and selection.

• Proposes ensemble of sub-problems classification to separate closely related classes.

摘要

•Effectively combines LCADCs, MASA, and adaptive-rate FIR filter for signal denoising.•Reduces data size using adaptive-rate techniques, features extraction, and selection.•Proposes ensemble of sub-problems classification to separate closely related classes.

论文关键词：Electroencephalogram (EEG),Epilepsy,Feature selection,Ensemble classifier

论文评审过程：Received 7 July 2021, Revised 29 November 2021, Accepted 29 November 2021, Available online 4 December 2021, Version of Record 9 December 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.116356