Department of Speech and Hearing Sciences, University of Washington, 1417 Northeast 42nd Street, Seattle, Washington 98105, USA.
J Acoust Soc Am. 2024 Sep 1;156(3):1674-1687. doi: 10.1121/10.0028537.
The auditory brainstem response (ABR) can be used to evaluate hearing sensitivity of animals. However, typical measurement protocols are time-consuming. Here, an adaptive algorithm is proposed for efficient ABR threshold estimation. The algorithm relies on the update of the predicted hearing threshold from a Gaussian process model as ABR data are collected using iteratively optimized stimuli. To validate the algorithm, ABR threshold estimation is simulated by adaptively subsampling pre-collected ABR datasets. The simulated experiment is performed on 5 datasets of mouse, budgerigar, gerbil, and guinea pig ABRs (27 ears). The datasets contain 68-106 stimuli conditions, and the adaptive algorithm is configured to terminate after 20 stimuli conditions. The algorithm threshold estimate is compared against human rater estimates who visually inspected the full waveform stacks. The algorithm threshold matches the human estimates within 10 dB, averaged over frequency, for 15 of the 27 ears while reducing the number of stimuli conditions by a factor of 3-5 compared to standard practice. The intraclass correlation coefficient is 0.81 with 95% upper and lower bounds at 0.74 and 0.86, indicating moderate to good reliability between human and algorithm threshold estimates. The results demonstrate the feasibility of a Bayesian adaptive procedure for rapid ABR threshold estimation.
听觉脑干反应（ABR）可用于评估动物的听力灵敏度。然而，典型的测量方案耗时较长。这里提出了一种用于高效 ABR 阈值估计的自适应算法。该算法依赖于从高斯过程模型更新预测的听力阈值，因为 ABR 数据是使用迭代优化的刺激收集的。为了验证该算法，通过自适应地对预收集的 ABR 数据集进行抽样来模拟 ABR 阈值估计。模拟实验在小鼠、虎皮鹦鹉、沙鼠和豚鼠的 5 个 ABR 数据集（27 只耳朵）上进行。这些数据集包含 68-106 个刺激条件，自适应算法配置为在 20 个刺激条件后终止。将算法阈值估计与视觉检查全波堆叠的人类评估者的估计进行比较。对于 27 只耳朵中的 15 只，算法阈值与人类估计值相差 10 dB 以内，平均而言，与标准做法相比，减少了 3-5 倍的刺激条件数量。组内相关系数为 0.81，95%置信区间上限和下限分别为 0.74 和 0.86，表明人类和算法阈值估计之间具有中等至良好的可靠性。结果表明，贝叶斯自适应程序用于快速 ABR 阈值估计是可行的。
