Abstract
Hyperacusis is defined as an increased sensitivity to sounds, i.e. sounds presented at moderate levels can produce discomfort or even pain. Existing diagnostic methods, like the Hyperacusis Questionnaire (HQ) and Loudness Discomfort Levels (LDLs), have been challenged because of their variability and lack of agreement on appropriate cut-off values. We propose a novel approach by using psychoacoustic ratings of natural sounds as an assessment tool for hyperacusis. Subjects (n = 81) were presented with natural and artificial (tone pips, noises) sounds (n = 69) in a controlled environment at four sound levels (60, 70, 80 and 90 dB SPL). The task was to rate them on a pleasant to unpleasant visual analog scale. The inherent challenge of this study was to create a new diagnostic tool when no gold standard of hyperacusis diagnosis exists. We labeled our subjects as hyperacusic (n = 26) when they were diagnosed as such by at least two of three methods (HQ, LDLs and self-report). There was a significant difference between controls (n = 23) and hyperacusics in the median global rating of pleasant sounds. Median global ratings of unpleasant sounds and artificial sounds did not differ significantly. Then we selected the subset of sounds that could best discriminate the controls from the hyperacusics, the Core Discriminant Sounds (CDS), and we used them to develop a new metric: The CDS score. A normalized global score and a score for each sound level can be computed with respect to a control population without hyperacusis. A receiver operating characteristic analysis showed that the accuracy of our method in distinguishing subjects with and without complaints of hyperacusis (86%, 95% Confidence Interval (CI): 76–93%) is comparable to that of existing methods such as the LDL (77%, CI: 67–86%) and the HQ (80%, CI: 69–88%). We believe that the CDS score is more relevant to subject's complaints than LDLs and that it could be applied in a clinical environment in a fast and effective way, while minimizing discomfort and biases.
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