Institute of Clinical Research, Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark.
Research Unit for Oto-Rhino-Laryngology, Odense University Hospital, Odense, Denmark.
Trends Hear. 2020 Jan-Dec;24:2331216520960861. doi: 10.1177/2331216520960861.
Effective hearing aid (HA) rehabilitation requires personalization of the HA fitting parameters, but in current clinical practice only the gain prescription is typically individualized. To optimize the fitting process, advanced HA settings such as noise reduction and microphone directionality can also be tailored to individual hearing deficits. In two earlier studies, an auditory test battery and a data-driven approach that allow classifying hearing-impaired listeners into four auditory profiles were developed. Because these profiles were found to be characterized by markedly different hearing abilities, it was hypothesized that more tailored HA fittings would lead to better outcomes for such listeners. Here, we explored potential interactions between the four auditory profiles and HA outcome as assessed with three different measures (speech recognition, overall quality, and noise annoyance) and six HA processing strategies with various noise reduction, directionality, and compression settings. Using virtual acoustics, a realistic speech-in-noise environment was simulated. The stimuli were generated using a HA simulator and presented to 49 habitual HA users who had previously been profiled. The four auditory profiles differed clearly in terms of their mean aided speech reception thresholds, thereby implying different needs in terms of signal-to-noise ratio improvement. However, no clear interactions with the tested HA processing strategies were found. Overall, these findings suggest that the auditory profiles can capture some of the individual differences in HA processing needs and that further research is required to identify suitable HA solutions for them.
有效的助听器（HA）康复需要个性化的 HA 适配参数，但在当前的临床实践中，通常只有增益处方是个性化的。为了优化适配过程，还可以针对个体的听力缺陷来定制先进的 HA 设置，如降噪和麦克风方向性。在之前的两项研究中，开发了一个听觉测试电池和一种数据驱动的方法，可以将听力受损的听众分为四个听觉特征。因为这些特征被发现具有明显不同的听力能力，所以假设对这些听众进行更定制的 HA 适配会带来更好的结果。在这里，我们使用三种不同的测量方法（言语识别、整体质量和噪声烦恼）和六种具有不同降噪、方向性和压缩设置的 HA 处理策略，探索了四个听觉特征与 HA 结果之间的潜在相互作用。使用虚拟声学，模拟了一个逼真的语音噪声环境。刺激使用 HA 模拟器生成，并呈现给之前进行过特征分析的 49 名习惯性 HA 用户。四个听觉特征在平均辅助言语接收阈值方面存在明显差异，这意味着在信噪比改善方面存在不同的需求。然而，没有发现与测试的 HA 处理策略有明显的相互作用。总体而言，这些发现表明听觉特征可以捕捉到 HA 处理需求中的一些个体差异，需要进一步研究以确定适合它们的 HA 解决方案。
