Institute of Sound and Vibration Research, Faculty of Engineering and the Environment, University of Southampton, UK.
Eriksholm Research Centre, Snekkersten, Denmark.
J Neurosci Methods. 2021 Nov 1;363:109352. doi: 10.1016/j.jneumeth.2021.109352. Epub 2021 Sep 9.
Statistical detection methods are routinely used to automate auditory evoked response (AER) detection and assist clinicians with AER measurements. However, many of these methods are built around statistical assumptions that can be violated for AER data, potentially resulting in reduced or unpredictable test performances. This study explores a frequency domain bootstrap (FDB) and some FDB modifications to preserve test performance in serially correlated non-stationary data.
The FDB aims to generate many surrogate recordings, all with similar serial correlation as the original recording being analysed. Analysing the surrogates with the detection method then gives a distribution of values that can be used for inference. A potential limitation of the conventional FDB is the assumption of stationary data with a smooth power spectral density (PSD) function, which is addressed through two modifications.
The FDB was compared to a conventional parametric approach and two modified FDB approaches that aim to account for heteroskedasticity and non-smooth PSD functions. Hotelling's T(HT2) test applied to auditory brainstem responses was the test case.
When using conventional HT2, false-positive rates deviated significantly from the nominal alpha-levels due to serial correlation. The false-positive rates of the modified FDB were consistently closer to the nominal alpha-levels, especially when data was strongly heteroskedastic or the underlying PSD function was not smooth due to e.g. power lines noise.
The FDB and its modifications provide accurate, recording-dependent approximations of null distributions, and an improved control of false-positive rates relative to parametric inference for auditory brainstem response detection.
统计检测方法通常用于自动检测听觉诱发电位（AER）并帮助临床医生进行 AER 测量。然而，许多这些方法是基于统计假设构建的，这些假设可能会在 AER 数据中被违反，从而导致测试性能降低或不可预测。本研究探讨了一种频域自举（FDB）及其一些修改方法，以保持在连续相关非平稳数据中的测试性能。
FDB 的目的是生成许多具有与正在分析的原始记录相似的序列相关的替代记录。然后，使用检测方法分析这些替代记录，得到可以用于推断的值的分布。常规 FDB 的一个潜在限制是假设数据具有平稳的功率谱密度（PSD）函数，这通过两种修改来解决。
将 FDB 与传统的参数方法以及两种旨在考虑异方差和非平滑 PSD 函数的修改后的 FDB 方法进行了比较。听觉脑干反应的 Hotelling's T(HT2) 测试是测试案例。
当使用常规 HT2 时，由于序列相关性，假阳性率显著偏离名义 alpha 水平。修改后的 FDB 的假阳性率始终更接近名义 alpha 水平，尤其是当数据具有很强的异方差性或由于例如电源线噪声而导致潜在 PSD 函数不光滑时。
FDB 及其修改方法提供了准确的、基于记录的零分布近似值，并相对于听觉脑干反应检测的参数推断，提高了假阳性率的控制。
