Quantification of pain degree by frequency features of single-chanelled EEG

Junichiro Kagita, Yasue Mitsukura

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The final aim of this paper is to classify pain degree by only using Electroencephalogram (EEG) measured with single-channel. In clinical care, pain degree is needed for choosing and evaluating treatments, and it is important for clinicians to quantify pain degree as objectively as possible. Pain rating scales such as the Visual Analogue Scales (VAS) are usually used to quantify pain degree, which is only capable of subjective values due to self-report. From that, a method to quantify pain degree objectively has great importance. In this paper, we propose the possibility of quantifying pain degree by only using EEG measured with single-channel. 28 Subjects' EEG is measured while in 2 states; pain-free (VAS score of 0) and pain (VAS score of 3-4). By extracting frequency features from the measured EEG, and classifying using Support Vector Machine (SVM), the subjects in 2 states were classified with the accuracy of 100%. The results show reliability and validity of classifying pain degree using EEG measured with single-channel.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE 15th International Workshop on Advanced Motion Control, AMC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages359-363
Number of pages5
ISBN (Electronic)9781538619469
DOIs
Publication statusPublished - 2018 Jun 1
Event15th IEEE International Workshop on Advanced Motion Control, AMC 2018 - Tokyo, Japan
Duration: 2018 Mar 92018 Mar 11

Other

Other15th IEEE International Workshop on Advanced Motion Control, AMC 2018
CountryJapan
CityTokyo
Period18/3/918/3/11

Fingerprint

Pain
Electroencephalography
Quantification
Quantify
Analogue
Support vector machines
Electroencephalogram
Support Vector Machine
Classify

Keywords

  • Machine learning
  • Pain degree
  • Prefrontal cortex
  • Signal processing
  • Single-channeled EEG

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Mechanical Engineering
  • Control and Optimization

Cite this

Kagita, J., & Mitsukura, Y. (2018). Quantification of pain degree by frequency features of single-chanelled EEG. In Proceedings - 2018 IEEE 15th International Workshop on Advanced Motion Control, AMC 2018 (pp. 359-363). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/AMC.2019.8371118

Quantification of pain degree by frequency features of single-chanelled EEG. / Kagita, Junichiro; Mitsukura, Yasue.

Proceedings - 2018 IEEE 15th International Workshop on Advanced Motion Control, AMC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 359-363.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Kagita, J & Mitsukura, Y 2018, Quantification of pain degree by frequency features of single-chanelled EEG. in Proceedings - 2018 IEEE 15th International Workshop on Advanced Motion Control, AMC 2018. Institute of Electrical and Electronics Engineers Inc., pp. 359-363, 15th IEEE International Workshop on Advanced Motion Control, AMC 2018, Tokyo, Japan, 18/3/9. https://doi.org/10.1109/AMC.2019.8371118
Kagita J, Mitsukura Y. Quantification of pain degree by frequency features of single-chanelled EEG. In Proceedings - 2018 IEEE 15th International Workshop on Advanced Motion Control, AMC 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 359-363 https://doi.org/10.1109/AMC.2019.8371118
Kagita, Junichiro ; Mitsukura, Yasue. / Quantification of pain degree by frequency features of single-chanelled EEG. Proceedings - 2018 IEEE 15th International Workshop on Advanced Motion Control, AMC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 359-363
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