Adaptive boost LS-SVM classification approach for time-series signal classification in epileptic seizure diagnosis applications

作者：

Highlights：

• Adaptive Boost Support Vector Machine (AB-LS-SVM) is designed to predict epilepsy.

• AB-LS-SVM classifier adopts covariance matrix to reduce EEG signal dimensionality.

• AB-LS-SVM classifier detects epileptic patterns with high classification accuracy.

• AB-LS-SVM classifier utilizes extracted EEG features to identify epilepsy.

• AB-LS-SVM classifier has practical implications in healthcare & expert systems.

摘要

•Adaptive Boost Support Vector Machine (AB-LS-SVM) is designed to predict epilepsy.•AB-LS-SVM classifier adopts covariance matrix to reduce EEG signal dimensionality.•AB-LS-SVM classifier detects epileptic patterns with high classification accuracy.•AB-LS-SVM classifier utilizes extracted EEG features to identify epilepsy.•AB-LS-SVM classifier has practical implications in healthcare & expert systems.

论文关键词：Epileptic seizure,Health informatics,Electroencephalogram,Covariance,Eigen values,Adaptive Boosting Least Square-Support Vector Machine,AB-LS-SVM

论文评审过程：Received 23 September 2019, Revised 27 May 2020, Accepted 16 June 2020, Available online 1 July 2020, Version of Record 8 July 2020.

论文官网地址：https://doi.org/10.1016/j.eswa.2020.113676