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. 2022 Apr 29:2022:1112598.
doi: 10.1155/2022/1112598. eCollection 2022.

Software Application toward Accessible Hearing Care Assessment: Gap in Noise Test

Khalid Alhussaini  1 , Shaza Saleh  2   3 , Adham Aleid  1 , Saad Alkhalaf  1 , Reem Badghaish  2 , Amir Altinawi  1 , Abdullatif Alwasel  1
Affiliations

Software Application toward Accessible Hearing Care Assessment: Gap in Noise Test

Khalid Alhussaini et al. J Healthc Eng. .

Retraction in

Abstract

Currently, several methods are being applied to assess auditory temporal resolution in a controlled clinical environment via the measurements of gap detection thresholds (GDTs). However, these methods face two issues: the relatively long time required to perform the gap detection test in such settings and the potential of inaccessibility to such facilities. This article proposes a fast, affordable, and reliable application-based method for the determination of GDT either inside or outside the soundproof booth. The proposed test and the acoustic stimuli were both developed using the MATLAB® programming platform. GDT is determined when the subject is able to distinguish the shortest silent gap inserted randomly in one of two segments of white noise. GDTs were obtained from 42 normal-hearing subjects inside and outside the soundproof booth. The results of this study indicated that average GDTs measured inside the booth (5.12 ± 1.02 ms) and outside (4.78 ± 1.16 ms) were not significantly different. The measured GDTs were also comparable to that reported in the literature. In addition, the GDT screening time of the proposed method was approximately 5 minutes, a screening time that is much less than that reported by the literature. Data show that the proposed application was fast and reliable to screen GDT compared to the standard method currently used in clinical settings.

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Conflict of interest statement

The authors have no conflicts of interest to declare.

Figures

Figure 1
Figure 1
The experimental setup includes stimuli, participant, headphones, and user-interface window.
Figure 2
Figure 2
Summarized representation of gap detection software algorithm flowchart.
Figure 3
Figure 3
GDTs (mean ± SD) from the OUT-test and IN-test.
Figure 4
Figure 4
GDTs (mean ± SD) from the OUT-test and IN-test performed in a different order.
Figure 5
Figure 5
GDTs (mean ± SD) from the OUT-test and IN-test classified with respect to gender.
See this image and copyright information in PMC

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