An EEG based hierarchical classification strategy to differentiate five intensities of pain

作者：

Highlights：

• Objective Pain Measurement is necessary for patients who cannot express their pain.

• The EEGs in alpha band for no-pain state have normal distribution.

• As the pain severity increases, the PDF deviates from Gaussian distribution.

• Five pain states are recognized by specifying the changes in the PDF of alpha band.

• Pain features are classified using a proposed bio-inspired hierarchical classifier.

摘要

•Objective Pain Measurement is necessary for patients who cannot express their pain.•The EEGs in alpha band for no-pain state have normal distribution.•As the pain severity increases, the PDF deviates from Gaussian distribution.•Five pain states are recognized by specifying the changes in the PDF of alpha band.•Pain features are classified using a proposed bio-inspired hierarchical classifier.

论文关键词：Bayesian Optimization,EEG,Pain measurement,Physiological based classifier,Data distribution,Alpha waves

论文评审过程：Received 19 January 2020, Revised 5 February 2021, Accepted 4 April 2021, Available online 8 April 2021, Version of Record 6 May 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.115010