The Utility of fNIRS Signals versus Self-Report for Classification of Fibromyalgia Syndrome

EKEN A., Gokcay D., BASKAK B., Baltaci A., KARA M.

25th Signal Processing and Communications Applications Conference (SIU), Antalya, Turkey, 15 - 18 May 2017 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume:
  • Doi Number: 10.1109/siu.2017.7960269
  • City: Antalya
  • Country: Turkey
  • Hacettepe University Affiliated: Yes


Fibromyalgia (FM) is a widespread painful disease that has a 2-8% prevalence. Its diagnosis is generally performed by American College of Rheumatology (ACR) criteria. However, these criteria are subjective and their reliability is controversial. In this study, painful stimulation and Transcutaneous Electrical Nerve Stimulation (TENS) were applied to both hands of healthy controls and FM patients and hemodynamic responses was measured by using Functional Near Infrared Spectroscopy (fNIRS). Features extracted from hemodynamic responses and self-report data were used with 4 different classifiers and 14 different parameters. In conclusion, classification performed by objective data collected from fNIRS signals (95%) gave higher accuracy than classification performed by subjective self-report data (83%). This study showed that painful stimulation and TENS application can be used to diagnose Fibromyalgia disease by using fNIRS.