Wohlbauer Dietmar M, Hem Charles, McCallick Caylin, Arenberg Julie G
bioRxiv. 2024 Nov 29:2024.10.31.621419. doi: 10.1101/2024.10.31.621419.
Cochlear implant listeners show difficulties in understanding speech in noise. Channel interactions from activating overlapping neural populations reduce the signal accuracy necessary to interpret complex signals. Optimizing programming strategies based on focused detection thresholds to reduce channel interactions has led to improved performance. In the current study, two previously suggested methods, channel deactivation and focused dynamic tripolar stimulation, were combined to create three cochlear implant programs. Utilizing an automatic channel selection algorithm from focused detection threshold profiles, three programs were created with the same deactivated channels but varying proportions of channels employing focused stimulation, monopolar, dynamic focused and a mixed program. Thirteen ears in eleven adult cochlear implant listeners with Advanced Bionics HiRes90k devices were tested. Vowel identification and sentence perception in quiet and noise served as outcome measures, and the influences of listening experience, age, clinical consonant-nucleus-consonant performance, and perceptual thresholds on speech performance were assessed.
Across subjects, different degrees of focusing showed individual performance improvements for vowels and sentences over the monopolar program. However, only slight trends and no significant group improvements were observed. Focused listening benefits were shown for individuals with less cochlear implant experience, and clinically poor performers seem to benefit more from focusing than good performers.
The current findings suggest that deactivating and focusing subsets of channels improves speech performance for some individuals, especially poor performers, a possible effect of reduced channel interactions. The findings also show that individual performance is largely variable, possibly due to listening experience, age, or the underlying detection threshold.
人工耳蜗使用者在噪声环境中理解言语存在困难。激活重叠神经群体产生的通道相互作用会降低解读复杂信号所需的信号准确性。基于聚焦检测阈值优化编程策略以减少通道相互作用已带来了性能提升。在本研究中,将两种先前提出的方法——通道停用和聚焦动态三极刺激——相结合,创建了三种人工耳蜗程序。利用基于聚焦检测阈值曲线的自动通道选择算法,创建了三个程序,它们具有相同的停用通道,但采用聚焦刺激、单极、动态聚焦和混合程序的通道比例不同。对11名使用Advanced Bionics HiRes90k设备的成年人工耳蜗使用者的13只耳朵进行了测试。将安静和噪声环境下的元音识别和句子感知作为结果指标,并评估了听力经验、年龄、临床辅音-元音-辅音表现以及感知阈值对言语表现的影响。
在所有受试者中,与单极程序相比,不同程度的聚焦在元音和句子方面均显示出个体性能的改善。然而,仅观察到轻微趋势,未发现显著的组间改善。对于人工耳蜗经验较少的个体,聚焦聆听有好处,而且临床上表现较差的使用者似乎比表现好的使用者从聚焦中获益更多。
目前的研究结果表明,停用和聚焦部分通道可改善部分个体的言语表现,尤其是表现较差的个体,这可能是通道相互作用减少的结果。研究结果还表明,个体表现差异很大,可能是由于听力经验、年龄或潜在的检测阈值所致。