A Method of Generating a Classifier that Determines the Presence or Absence of IBS Symptoms by Supervised Learning from the Frequency Analysis of Electroencephalogram Data.

Abstract

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. 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. The diagnostic accuracy of IBS symptoms was verified for the whole brain and the frontal, parietal, and occipital regions. We estimated >90% accuracy of the IBS classifier in the whole brain and frontal region. The results of this study suggest that EEG data can be used to determine the presence or absence of IBS symptoms. With the IBS classifier, EEG may help provide feedback regarding the presence or absence of symptoms to patients, which is the basis for developing self-management strategies for IBS.