1988International Journal of AudiologyRequires access

Brainstem Response Audiometry: II. Classification of Hearing Loss by Discriminant Analysis

J.F.C. van der Drift, M. P. Brocaar, Gijsbert A. van Zanten

Open publisher page 12 citations

Abstract

In the companion paper [V.d. Drift et al.; Audiology 27: 260-270, 1988], it was shown graphically that conductive and cochlear hearing loss can be distinguished on the basis of the combinations of the auditory brainstem response threshold with the horizontal shift of the latency-level curve of peak V, its derivative or the latency of peak V at threshold level, respectively. In addition to the patient data used in the companion paper, 22 patients with mixed hearing loss were enrolled in the present study. The statistical technique of discriminant analysis was applied to find the optimum linear combination of auditory brainstem response data for classification of a hearing loss. The brainstem classification 'cochlear hearing loss' agrees with the diagnosis on the basis of the pure-tone audiogram in 85% of the cases. In cases with the brainstem classification 'conductive hearing loss', 93% showed at least a conductive component in the pure-tone audiogram.

About this research paper

What this paper is about

In the companion paper [V.d. Drift et al.; Audiology 27: 260-270, 1988], it was shown graphically that conductive and cochlear hearing loss can be distinguished on the basis of the combinations of the auditory brainstem response threshold with the horizontal shift of the latency-level curve of peak V, its derivative or the latency of peak V at threshold level, respectively. In addition to the patient data used in the companion paper, 22 patients with mixed hearing loss were enrolled in the present study. The statistical technique of discriminant analysis was applied to find the optimum linear combination of auditory brainstem response data for classification of a hearing loss. The brainstem classification 'cochlear hearing loss' agrees with the diagnosis on the basis of the pure-tone audiogram in 85% of the cases. In cases with the brainstem classification 'conductive hearing loss', 93% showed at least a conductive component in the pure-tone audiogram.

Why it matters

OpenAlex reports 12 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

In the companion paper [V.d. Drift et al.; Audiology 27: 260-270, 1988], it was shown graphically that conductive and cochlear hearing loss can be distinguished on the basis of the combinations of the auditory brainstem response threshold with the horizontal shift of the latency-level curve of peak V, its derivative or the latency of peak V at threshold level, respectively. In addition to the patient data used in the companion paper, 22 patients with mixed hearing loss were enrolled in the present study. The statistical technique of discriminant analysis was applied to find the optimum linear combination of auditory brainstem response data for classification of a hearing loss. The brainstem classification 'cochlear hearing loss' agrees with the diagnosis on the basis of the pure-tone audiogram in 85% of the cases. In cases with the brainstem classification 'conductive hearing loss', 93% showed at least a conductive component in the pure-tone audiogram.

Key concepts: Audiogram, Audiology, Auditory brainstem response, Hearing loss, Brainstem, Conductive hearing loss, Audiometry, Medicine

Related papers

Back to paper searchBrowse research topicsOriginal source
Brainstem Response Audiometry: II. Classification of Hearing Loss by Discriminant Analysis — Research Paper | ScholarLens