Assessment of emotional reaction induced by visual stimulation based on cross-correlation between pulse wave transmission time and heart rate in the mayer wave-band

Makoto Yoshizawa, Norihiro Sugita, Akira Tanaka, Tatsuya Masuda, Ken Ichi Abe, Shigeru Chiba, Tomoyuki Yambe, Shin Ichi Nitta

Research output: Contribution to journalConference article

5 Citations (Scopus)

Abstract

Mayer wave (0.1Hz fluctuation) included in heart rate variability and blood pressure variability appears apparently in the resting state. On the other hand, it can be predicted that a strong emotional reaction may affect the relationship between these variabilities. This prediction suggests that the human emotional reaction can be quantified by the maximum correlation coefficient ρmax between heart rate and blood pressure whose frequency components are limited to the Mayer wave-band. However, the conventional method of obtaining ρmax needs a bulky and expensive device for measuring continuous blood pressure. In this study, a smaller and cheaper device for measuring pulse wave transmission time (PTT) has been developed. This paper has shown that the PTT can give ρmax instead of blood pressure and that ρmax obtained by the PTT may significantly reflect the emotional reaction on the basis of an experiment using nine healthy subjects with nine self-produced devices in which pictures were presented to the subjects to induce their emotional reactions.

Original languageEnglish
Pages (from-to)2411-2414
Number of pages4
JournalAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume26 IV
Publication statusPublished - 2004 Dec 1
EventConference Proceedings - 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2004 - San Francisco, CA, United States
Duration: 2004 Sep 12004 Sep 5

Keywords

  • Blood pressure variability
  • Emotional reaction
  • Heart rate variability
  • Mayer wave
  • The maximum correlation coefficient

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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