Background

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.

Method

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.

Comparisons with existing methods

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 ${\mathrm{T}}^{\mathrm{2}}$(HT2) test applied to auditory brainstem responses was the test case.

Results

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.

Conclusion

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.

中文翻译：

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使用频域自举控制听觉诱发反应检测的测试特异性

背景

统计检测方法通常用于自动化听觉诱发反应 (AER) 检测并协助临床医生进行 AER 测量。然而，这些方法中的许多方法都是围绕可能违反 AER 数据的统计假设而构建的，这可能会导致测试性能降低或不可预测。本研究探索了频域引导程序 (FDB) 和一些 FDB 修改，以在序列相关的非平稳数据中保持测试性能。

方法

FDB 旨在生成许多替代记录，所有记录都与被分析的原始记录具有类似的序列相关性。使用检测方法分析代理，然后给出可用于推理的值分布。传统 FDB 的一个潜在限制是假设静态数据具有平滑的功率谱密度 (PSD) 函数，这可以通过两个修改来解决。

与现有方法的比较

将 FDB 与传统的参数方法和两种旨在说明异方差和非平滑 PSD 函数的修改后的 FDB 方法进行了比较。霍特林${\mathrm{吨}}^{\mathrm{2}}$(HT2) 测试应用于听觉脑干反应是测试案例。

结果

使用传统 HT2 时，由于序列相关，假阳性率显着偏离名义 alpha 水平。修改后的 FDB 的假阳性率始终接近标称 alpha 水平，尤其是当数据强烈异方差或底层 PSD 函数由于例如电力线噪声而不平滑时。

结论

FDB 及其修改提供了准确的、依赖于记录的零分布近似值，以及相对于听觉脑干反应检测的参数推理的假阳性率的改进控制。