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

Junichiro Kagita, Yasue Mitsukura

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトルProceedings - 2018 IEEE 15th International Workshop on Advanced Motion Control, AMC 2018
出版社Institute of Electrical and Electronics Engineers Inc.
ページ359-363
ページ数5
ISBN(電子版)9781538619469
DOI
出版ステータスPublished - 2018 6月 1
イベント15th IEEE International Workshop on Advanced Motion Control, AMC 2018 - Tokyo, Japan
継続期間: 2018 3月 92018 3月 11

出版物シリーズ

名前Proceedings - 2018 IEEE 15th International Workshop on Advanced Motion Control, AMC 2018

Other

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

ASJC Scopus subject areas

  • 人工知能
  • コンピュータ サイエンスの応用
  • 機械工学
  • 制御と最適化

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