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前庭诱发肌源性电位的反卷积。

Deconvolution of the vestibular evoked myogenic potential.

机构信息

ENT Clinic, Münster University Hospital, Münster, Germany.

出版信息

J Theor Biol. 2012 Feb 7;294:87-97. doi: 10.1016/j.jtbi.2011.10.033. Epub 2011 Nov 6.

Abstract

The vestibular evoked myogenic potential (VEMP) and the associated variance modulation can be understood by a convolution model. Two functions of time are incorporated into the model: the motor unit action potential (MUAP) of an average motor unit, and the temporal modulation of the MUAP rate of all contributing motor units, briefly called rate modulation. The latter is the function of interest, whereas the MUAP acts as a filter that distorts the information contained in the measured data. Here, it is shown how to recover the rate modulation by undoing the filtering using a deconvolution approach. The key aspects of our deconvolution algorithm are as follows: (1) the rate modulation is described in terms of just a few parameters; (2) the MUAP is calculated by Wiener deconvolution of the VEMP with the rate modulation; (3) the model parameters are optimized using a figure-of-merit function where the most important term quantifies the difference between measured and model-predicted variance modulation. The effectiveness of the algorithm is demonstrated with simulated data. An analysis of real data confirms the view that there are basically two components, which roughly correspond to the waves p13-n23 and n34-p44 of the VEMP. The rate modulation corresponding to the first, inhibitory component is much stronger than that corresponding to the second, excitatory component. But the latter is more extended so that the two modulations have almost the same equivalent rectangular duration.

摘要

前庭诱发肌源性电位(VEMP)及其相关的方差调制可以通过卷积模型来理解。该模型中包含两个时间函数:一个是平均运动单位动作电位(MUAP),另一个是所有参与运动单位的 MUAP 速率的时间调制,简称速率调制。后者是感兴趣的函数,而 MUAP 则充当滤波器,会扭曲测量数据中包含的信息。本文展示了如何通过反卷积方法来消除滤波,从而恢复速率调制。我们的反卷积算法的关键方面如下:(1)仅用几个参数来描述速率调制;(2)通过对 VEMP 与速率调制进行 Wiener 反卷积来计算 MUAP;(3)使用一种基于重要性的函数来优化模型参数,其中最重要的项量化了测量值和模型预测值之间的方差调制差异。通过模拟数据验证了该算法的有效性。对真实数据的分析证实了这样一种观点,即基本上存在两个分量,它们大致对应于 VEMP 的 p13-n23 和 n34-p44 波。与第一个抑制分量相对应的速率调制比与第二个兴奋分量相对应的速率调制强得多。但后者更为扩展,因此两个调制具有几乎相同的等效矩形持续时间。

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