title: A novel approach to predict subjective pain perception from single-trial laser-evoked potentials.
abstract: Pain is a subjective first-person experience, and self-report is the gold standard for pain assessment in clinical practice. However, self-report of pain is not available in some vulnerable populations (e.g., patients with disorders of consciousness), which leads to an inadequate or suboptimal treatment of pain. Therefore, the availability of a physiology-based and objective assessment of pain that complements the self-report would be of great importance in various applications. Here, we aimed to develop a novel and practice-oriented approach to predict pain perception from single-trial laser-evoked potentials (LEPs). We applied a novel single-trial analysis approach that combined common spatial pattern and multiple linear regression to automatically and reliably estimate single-trial LEP features. Further, we adopted a Naïve Bayes classifier to discretely predict low and high pain and a multiple linear prediction model to continuously predict the intensity of pain perception from single-trial LEP features, at both within- and cross-individual levels. Our results showed that the proposed approach provided a binary prediction of pain (classification of low pain and high pain) with an accuracy of 86.3 ± 8.4% (within-individual) and 80.3 ± 8.5% (cross-individual), and a continuous prediction of pain (regression on a continuous scale from 0 to 10) with a mean absolute error of 1.031 ± 0.136 (within-individual) and 1.821 ± 0.202 (cross-individual). Thus, the proposed approach may help establish a fast and reliable tool for automated prediction of pain, which could be potentially adopted in various basic and clinical applications.
authors.affiliation: Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China.
authors.forename: G
authors.initials: G
authors.lastname: Huang
authors.affiliation: Key Laboratory of Cognition and Personality (Ministry of Education) and School of Psychology, Southwest University, Chongqing, China.
authors.forename: P
authors.initials: P
authors.lastname: Xiao
authors.affiliation: Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China.
authors.forename: Y S
authors.initials: YS
authors.lastname: Hung
authors.affiliation: Department of Neuroscience, Physiology and Pharmacology, University College London, UK.
authors.forename: G D
authors.initials: GD
authors.lastname: Iannetti
authors.affiliation: Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China. Electronic address: zgzhang@eee.hku.hk.
authors.forename: Z G
authors.initials: ZG
authors.lastname: Zhang
authors.affiliation: Key Laboratory of Cognition and Personality (Ministry of Education) and School of Psychology, Southwest University, Chongqing, China. Electronic address: huli@swu.edu.cn.
authors.forename: L
authors.initials: L
authors.lastname: Hu
mesh_terms: D000293:Adolescent; D000328:Adult; D004569:Electroencephalography / Q000379:methods*; D005073:Evoked Potentials, Somatosensory / Q000502:physiology*; D005260:Female; D006358:Hot Temperature; D006801:Humans; D007834:Lasers; D008297:Male; D010147:Pain Measurement / Q000379:methods*; D058748:Pain Perception / Q000502:physiology*; D055815:Young Adult
keywords: Classification; Laser-evoked potentials (LEPs); Pain; Pain prediction; Regression