Bayesian STAI Anxiety Index Predictions Based on Prefrontal Cortex NIRS Data for the Resting State

  • Masakaze Sato
  • Wakana Ishikawa
  • Tomohiko Suzuki
  • Takashi Matsumoto
  • Takeo Tsujii
  • Kaoru Sakatani
Conference paper
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 765)

Abstract

Several distinctive activity patterns have been observed in the brain at rest. The aim of this study was to determine whether the STAI index can be predicted from changes in the oxy- and deoxy-hemoglobin (Hb) concentrations by using two-channel prefrontal cortex (PFC) NIRS data for the resting state. The study population comprised 19 subjects. Each subject performed four trials, each of which consisted of resting with no task for 3 min. Data were acquired using a portable NIRS device equipped with two channels. The prediction algorithm was derived within a Bayesian machine learning framework. The prediction errors for seven subjects were not greater than 5.0. Because the STAI index varied between 20 and 80, these predictions appeared reasonable. The present method allowed prediction of mental status based on the NIRS data at resting condition obtained in the PFC.

Keywords

NIRS Prefrontal cortex STAI anxiety index 

Notes

Acknowledgments

This research was partly supported by the Japan Science and Technology Agency, under the Strategic Promotion of Innovative Research and Development Program, and a Grant-in-Aid from the Ministry of Education, Culture, Sports, Science and Technology of Japan (B23300247).

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Masakaze Sato
    • 1
  • Wakana Ishikawa
    • 1
  • Tomohiko Suzuki
    • 1
  • Takashi Matsumoto
    • 1
  • Takeo Tsujii
    • 2
  • Kaoru Sakatani
    • 2
  1. 1.Department of Electrical Engineering and BioscienceWaseda UniversityFujimono-siJapan
  2. 2.Division of Optical Brain Engineering, Department of Neurological SurgeryNihon University School of MedicineTokyoJapan

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