Department of Biomedical Engineering, University of Melbourne, Parkville, VIC, 3010, Australia; The Bionics Institute, 384-388 Albert St, East Melbourne, VIC, 3002, Australia.
The Bionics Institute, 384-388 Albert St, East Melbourne, VIC, 3002, Australia; Department of Medical Bionics, University of Melbourne, Parkville, VIC, 3010, Australia.
Hear Res. 2018 Dec;370:74-83. doi: 10.1016/j.heares.2018.09.006. Epub 2018 Sep 28.
Cortical auditory evoked potential (CAEP) thresholds have been shown to correlate well with behaviourally determined hearing thresholds. Growth functions of CAEPs show promise as an alternative to single level detection for objective hearing threshold estimation; however, the accuracy and clinical relevance of this method is not well examined. In this study, we used temporal and spectral CAEP features to generate feature growth functions. Spectral features may be more robust than traditional peak-picking methods where CAEP morphology is variable, such as in children or hearing device users. Behavioural hearing thresholds were obtained and CAEPs were recorded in response to a 1 kHz puretone from twenty adults with no hearing loss. Four features, peak-to-peak amplitude, root-mean-square, peak spectral power and peak phase-locking value (PLV) were extracted from the CAEPs. Functions relating each feature with stimulus level were used to calculate objective hearing threshold estimates. We assessed the performance of each feature by calculating the difference between the objective estimate and the behaviourally-determined threshold. We compared the accuracy of the estimates using each feature and found that the peak PLV feature performed best, with a mean threshold error of 2.7 dB and standard deviation of 5.9 dB from behavioural threshold across subjects. We also examined the relation between recording time, data quality and threshold estimate errors, and found that on average for a single threshold, 12.7 minutes of recording was needed for a 95% confidence that the threshold estimate was within 20 dB of the behavioural threshold using the peak-to-peak amplitude feature, while 14 minutes is needed for the peak PLV feature. These results show that the PLV of CAEPs can be used to find a clinically relevant hearing threshold estimate. Its potential stability in differing morphology may be an advantage in testing infants or cochlear implant users.
皮质听觉诱发电位（CAEP）阈值已被证明与行为学确定的听力阈值密切相关。CAEP 的生长函数有望成为客观听力阈值估计的替代单一水平检测方法；然而，这种方法的准确性和临床相关性尚未得到很好的检验。在这项研究中，我们使用了时域和频域 CAEP 特征来生成特征生长函数。与传统的峰值拾取方法相比，频域特征可能更稳健，因为 CAEP 形态在儿童或听力设备使用者中可能会有所不同。我们在 20 名听力正常的成年人中获得了行为听力阈值，并记录了对 1 kHz 纯音的 CAEP。从 CAEPs 中提取了四个特征，包括峰峰值幅度、均方根、峰值谱功率和峰值锁相值（PLV）。使用与刺激水平相关的每个特征的函数来计算客观听力阈值估计值。我们通过计算客观估计值与行为确定的阈值之间的差异来评估每个特征的性能。我们比较了使用每个特征的估计值的准确性，并发现峰值 PLV 特征表现最佳，在 20 名受试者中，平均阈值误差为 2.7 dB，标准偏差为 5.9 dB。我们还检查了记录时间、数据质量和阈值估计误差之间的关系，发现对于单个阈值，使用峰峰值幅度特征，平均需要 12.7 分钟的记录时间，才有 95%的置信度认为阈值估计值在 20 dB 以内与行为阈值相符，而对于峰值 PLV 特征，则需要 14 分钟。这些结果表明，CAEP 的 PLV 可用于找到具有临床相关性的听力阈值估计值。其在不同形态下的潜在稳定性可能是测试婴儿或人工耳蜗使用者的优势。
