Academic Editor: Sébastien Kindt
Background: Young adults with irritable bowel syndrome (IBS)
occasionally exhibit specific abdominal symptoms, including abdominal pain
associated with brain activity patterns. Decoded neural feedback (DecNef) is a
biofeedback exercise that allows symptomatic people to exercise self-control over
their brain activity patterns relative to those without symptoms. Thus, DecNef
can be used to self-control abdominal pain in patients with IBS. To establish a
DecNef practice for IBS, it is necessary to develop a classifier that can
distinguish the electroencephalography (EEG) patterns (EEG signatures) of IBS
between symptomatic and healthy people. Additionally, the accuracy of the
“classifier” must be evaluated. Methods: This study analyzed EEG data
obtained from symptomatic and asymptomatic young adults with IBS to develop a
support vector machine-based IBS classifier and verify its usefulness. EEG data
were recorded for 28 university students with IBS and 24 without IBS. EEG data
were frequency-analyzed by fast Fourier transform analysis, and IBS classifiers
were created by supervised learning using a support vector machine.
Results: The diagnostic accuracy of IBS symptoms was verified for the
whole brain and the frontal, parietal, and occipital regions. We estimated