Literature DB >> 30503919

What does a pain 'biomarker' mean and can a machine be taught to measure pain?

Joshua Levitt1, Carl Y Saab2.   

Abstract

Artificial intelligence allows machines to predict human faculties such as image and voice recognition. Can machines be taught to measure pain? We argue that the two fundamental requirements for a device with 'pain biomarker' capabilities are hardware and software. We discuss the merits and limitations of electroencephalography (EEG) as the hardware component of a putative embodiment of the device, and advances in the application of machine learning approaches to EEG for predicting pain.
Copyright © 2018 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Biomarker; EEG; Machine learning; Pain

Mesh：

Substances：

Year:  2018        PMID： 30503919     DOI： 10.1016/j.neulet.2018.11.038

Source DB:  PubMed          Journal:  Neurosci Lett        ISSN： 0304-3940            Impact factor:   3.046


  6 in total

1.  EEG individual power profiles correlate with tension along spine in horses.

Authors:  Mathilde Stomp; Serenella d'Ingeo; Séverine Henry; Clémence Lesimple; Hugo Cousillas; Martine Hausberger
Journal:  PLoS One       Date:  2020-12-14       Impact factor: 3.240

2.  The Prediction of Acute Postoperative Pain Based on Neural Oscillations Measured before the Surgery.

Authors:  Qi Han; Lupeng Yue; Fei Gao; Libo Zhang; Li Hu; Yi Feng
Journal:  Neural Plast       Date:  2021-04-09       Impact factor: 3.599

3.  Electroencephalogram-derived pain index for evaluating pain during labor.

Authors:  Liang Sun; Hong Zhang; Qiaoyu Han; Yi Feng
Journal:  PeerJ       Date:  2021-12-22       Impact factor: 2.984

4.  Accurate classification of pain experiences using wearable electroencephalography in adolescents with and without chronic musculoskeletal pain.

Authors:  Elizabeth F Teel; Don Daniel Ocay; Stefanie Blain-Moraes; Catherine E Ferland
Journal:  Front Pain Res (Lausanne)       Date:  2022-09-27

Review 5.  Discovery and validation of biomarkers to aid the development of safe and effective pain therapeutics: challenges and opportunities.

Authors:  Karen D Davis; Nima Aghaeepour; Andrew H Ahn; Martin S Angst; David Borsook; Ashley Brenton; Michael E Burczynski; Christopher Crean; Robert Edwards; Brice Gaudilliere; Georgene W Hergenroeder; Michael J Iadarola; Smriti Iyengar; Yunyun Jiang; Jiang-Ti Kong; Sean Mackey; Carl Y Saab; Christine N Sang; Joachim Scholz; Marta Segerdahl; Irene Tracey; Christin Veasley; Jing Wang; Tor D Wager; Ajay D Wasan; Mary Ann Pelleymounter
Journal:  Nat Rev Neurol       Date:  2020-06-15       Impact factor: 42.937

6.  Pain phenotypes classified by machine learning using electroencephalography features.

Authors:  Joshua Levitt; Muhammad M Edhi; Ryan V Thorpe; Jason W Leung; Mai Michishita; Suguru Koyama; Satoru Yoshikawa; Keith A Scarfo; Alexios G Carayannopoulos; Wendy Gu; Kyle H Srivastava; Bryan A Clark; Rosana Esteller; David A Borton; Stephanie R Jones; Carl Y Saab
Journal:  Neuroimage       Date:  2020-08-29       Impact factor: 6.556
  6 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.