Objective measurement of tinnitus using functional near-infrared spectroscopy and machine learning.

PloS One
Mehrnaz ShoushtarianJames B Fallon

Abstract

Chronic tinnitus is a debilitating condition which affects 10-20% of adults and can severely impact their quality of life. Currently there is no objective measure of tinnitus that can be used clinically. Clinical assessment of the condition uses subjective feedback from individuals which is not always reliable. We investigated the sensitivity of functional near-infrared spectroscopy (fNIRS) to differentiate individuals with and without tinnitus and to identify fNIRS features associated with subjective ratings of tinnitus severity. We recorded fNIRS signals in the resting state and in response to auditory or visual stimuli from 25 individuals with chronic tinnitus and 21 controls matched for age and hearing loss. Severity of tinnitus was rated using the Tinnitus Handicap Inventory and subjective ratings of tinnitus loudness and annoyance were measured on a visual analogue scale. Following statistical group comparisons, machine learning methods including feature extraction and classification were applied to the fNIRS features to classify patients with tinnitus and controls and differentiate tinnitus at different severity levels. Resting state measures of connectivity between temporal regions and frontal and occipital regions were...Continue Reading

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Methods Mentioned

BETA
imaging techniques
feature extraction
imaging technology

Software Mentioned

Matlab
WaveletFilter
NIRstart
NIRSite
NIRS
NIRS toolbox
NIRS Brain AnalyzIR Toolbox
AtlasViewer
Presentation
SPSS Statistics

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