Automated extraction of auditory brainstem response latencies and amplitudes by means of non-linear curve registration.
Automated extraction of auditory brainstem response latencies and amplitudes by means of non-linear curve registration.
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DOI：
10.1016/j.cmpb.2020.105595
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发表时间：
2020-11
影响因子：
6.1
通讯作者：
de Boer J
中科院分区：
文献类型：
--
作者：
Krumbholz K;Hardy AJ;de Boer J
我们提出了一个高度自动化的过程中提取的lavelength和振幅的听觉脑干反应（ABR）的曲线配准的基础上，通过非线性时间扭曲。我们比较不同的注册条件下使用的一个例子ABR数据集与广泛的响应延迟和信噪比。我们证明，最佳的注册条件密切匹配的专家人类观察员的性能。动物实验结果表明，听觉脑干反应（ABR）的瞬态声音在阈上水平可能是有用的测量听力损伤，是隐藏到目前的听力测试。评估这样的ABR需要提取相关偏转或“波”的振幅和振幅。目前，这主要是由人类观察者手动挑选每个响应中的波峰和波谷来完成的-这一过程既耗时又需要专家经验。在这里，我们提出了一个高度自动化的程序，用于提取个人ABR波的潜伏期和振幅的基础上建立良好的非线性曲线配准的方法。首先，要分析的个人ABR的时间对齐-要么彼此或，如果可用的话，与预先存在的模板-通过本地压缩或拉伸其时间轴与平滑和可逆的时间扭曲功能。然后，通过在公共（对齐的）时间轴上拾取波的波峰和波谷的振幅并将这些与个体对齐的响应和逆时间弯曲函数相结合来获得相关ABR波的个体振幅和振幅。使用一个示例ABR数据集与广泛的响应延迟和信噪比（SNR），我们测试不同的选择拟合的时间扭曲函数。我们交叉验证的翘曲结果，使用独立的响应重复和比较自动和手动提取的lavelength和振幅ABR波I和V.使用贝叶斯方法，我们表明，最佳的注册条件，自动和手动数据统计相似。非线性曲线配准可以用于在时间上对齐各个ABR，并以与手动拾取的结果紧密匹配的方式提取它们的波潜伏期和振幅。
We propose a highly automated procedure for extracting latencies and amplitudes of auditory brainstem responses (ABRs) based on curve registration through non-linear time warping. We compare different registration conditions using an example ABR data set with a wide range of response latencies and signal-to-noise ratios. We demonstrate that the best registration condition closely matched the performance of expert human observers. Animal results have suggested that auditory brainstem responses (ABRs) to transient sounds presented at supra-threshold levels may be useful for measuring hearing damage that is hidden to current audiometric tests. Evaluating such ABRs requires extracting the latencies and amplitudes of relevant deflections, or “waves”. Currently, this is mostly done by human observers manually picking the waves’ peaks and troughs in each individual response – a process that is both time-consuming and requiring of expert experience. Here, we propose a highly automated procedure for extracting individual ABR wave latencies and amplitudes based on the well-established methodology of non-linear curve registration. First, the to-be-analysed individual ABRs are temporally aligned – either with one another or, if available, with a pre-existing template – by locally compressing or stretching their time axes with smooth and invertible time warping functions. Then, the individual latencies and amplitudes of relevant ABR waves are obtained by picking the latencies of the waves’ peaks and troughs on the common (aligned) time axis and combining these with the individual aligned responses and inverse time warping functions. Using an example ABR data set with a wide range of response latencies and signal-to-noise ratios (SNRs), we test different choices for fitting the time warping functions. We cross-validate the warping results using independent response replicates and compare automatically and manually extracted latencies and amplitudes for ABR waves I and V. Using a Bayesian approach, we show that, for the best registration condition, automatic and manual data were statistically similar. Non-linear curve registration can be used to temporally align individual ABRs and extract their wave latencies and amplitudes in a way that closely matches results from manual picking.
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影响因子：
1.6
作者：
Bradley, AP;Wilson, WJ
通讯作者：
Wilson, WJ
DOI：
10.1016/0165-5876(94)01110-j
发表时间：
1995-04-01
影响因子：
1.5
作者：
ISSA, A;ROSS, HF
通讯作者：
ROSS, HF
DOI：
10.1523/jneurosci.2845-09.2009
发表时间：
2009-11-11
期刊：
The Journal of neuroscience : the official journal of the Society for Neuroscience
影响因子：
--
作者：
Kujawa SG;Liberman MC
通讯作者：
Liberman MC
影响因子：
2.8
作者：
Johannesen, Peter T.;Buzo, Byanka C.;Lopez-Poveda, Enrique A.
通讯作者：
Lopez-Poveda, Enrique A.
影响因子：
3.3
作者：
Bharadwaj, Hari M.;Mai, Alexandra R.;Shinn-Cunningham, Barbara G.
通讯作者：
Shinn-Cunningham, Barbara G.