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暴露于噪声环境中的人类的线性和非线性瞬态诱发耳声发射

Linear and nonlinear transient evoked otoacoustic emissions in humans exposed to noise.

作者信息

Moleti A, Sisto R, Lucertini M

机构信息

Dipartimento di Fisica, Università di Roma Tor Vergata, Via della Ricerca Scientifica, 1, 00133, Rome, Italy.

出版信息

Hear Res. 2002 Dec;174(1-2):290-5. doi: 10.1016/s0378-5955(02)00703-7.

Abstract

Transient evoked otoacoustic emissions (TEOAEs) have been analyzed in a population of 134 ears, divided into three classes: (1) nonexposed ears in bilaterally normal hearing subjects, (2) audiometrically normal ears of subjects exposed to noise and affected by unilateral high-frequency (f>3 kHz) hearing loss in the contralateral ear, and (3) the contralateral impaired ears of the exposed subjects. The statistical distributions of global and spectral signal-to-noise ratio (SNR) were analyzed. TEOAEs were recorded both in the linear and nonlinear acquisition mode to evaluate the effectiveness of two standard averaging techniques with respect to their sensitivity to the early effects of noise exposure. Good discrimination between nonexposed and exposed ears was obtained using either the linear or the nonlinear mode. Despite its intrinsically higher SNR, the linear mode is not more sensitive than the nonlinear mode for this purpose because it is not possible to find a window for effectively cancelling the linear artifact while keeping a suitable sensitivity to the short-latency high-frequency aspect of the response. Moreover, with respect to another measurable parameter, the TEOAE latency, good discrimination is obtained only by using the nonlinear mode because, again, the linear artifact masks the high-frequency TEOAE response.

摘要

对134只耳朵进行了瞬态诱发耳声发射(TEOAEs)分析,这些耳朵分为三类:(1)双侧听力正常受试者的未暴露耳朵;(2)暴露于噪声且对侧耳朵受单侧高频(f>3 kHz)听力损失影响但听力测试正常的受试者的耳朵;(3)暴露受试者的对侧受损耳朵。分析了整体和频谱信噪比(SNR)的统计分布。在直线和非线性采集模式下记录TEOAEs,以评估两种标准平均技术对噪声暴露早期效应的敏感性的有效性。使用直线或非线性模式均可在未暴露耳朵和暴露耳朵之间获得良好的区分。尽管直线模式本质上具有更高的SNR,但在此目的上它并不比非线性模式更敏感,因为不可能找到一个窗口来有效消除线性伪迹,同时又能对响应的短潜伏期高频部分保持合适的敏感性。此外,对于另一个可测量参数TEOAEs潜伏期,仅通过使用非线性模式才能获得良好的区分,因为同样,线性伪迹会掩盖高频TEOAEs响应。

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